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212 Unique Biology Research Topics For Students And Researchers

Every student studying something related to biology — botany, marine, animal, medicine, molecular or physical biology, is in an interesting field. It’s a subject that explores how animate and inanimate objects relate to themselves. The field unveils the past, the present, and what lies in the future of the relationship between the living and nonliving things.

This is precisely why you need custom and quality biology topics for your college and university essay or project. It’ll make it easy to brainstorm, research, and get to writing straight away. Before the deep dive, what is biology?

What Is Biology?

Everyone knows it’s the scientific study of life, but beyond that, biology facilitates the comprehension of living and nonliving things. It’s a branch that explores their anatomy, behavior, distribution, morphology, and physiology.

For example, it understands how genes are classified and constituted into generations. It encompasses various branches, including botany, medicine, genetics, ecology, marine biology, zoology, and molecular biology.

Here are what some of these mean:

Botany: This study of plants examines their structure, physiology, ecology, economic importance, and distribution, among others. It also deals with their biochemical processes, properties, and social interactions between plants. It extends to how plants are vital for human life, survival, and growth and how they play a significant role in stabilizing environmental health. Zoology: Zoology studies animal behavior, brain, structure, physiology, class, and distribution. It’s the general study of the lives of both living and extinct animals. It explains animal classification, the animal kingdom, evolution, habitat, embryology, and life span. Physiology: Physiology deals with the daily functions of the human body: How it works and the factors that make it work. It examines molecular behavior, the chemistry and physics behind locomotion, and how the cells in the living organisms’ body function. It helps understand how humans and animals get sick and what can be done to alleviate pain. Microbiology: Dealing with microorganisms, it examined how viruses, algae, fungi, bacteria, protozoa, and slime molds become parts of human life. They’re regarded as microbes, which play substantial roles in the human biochemical processes, including climate change, biodegradation, biodeterioration, food spoilage, biotech, and epidemiology. Marine Biology: This is the scientific study of organs in the sea. It understands their family classification, how they survive, and what makes wild marine animals different from domesticated and consumable ones. It also explores their interaction with the environment through several processes. The marine biologist studies marines in their natural environment, collects data on their characteristics, human impact on their living, and how they relate with themselves.

Now that you know all these, here are some custom biology topics to research for your university or college essay and paper.

Controversial Biology Topics

There are many controversial subjects in every field, and biology isn’t exempt from controversy. If you’d like to create an original essay through diverse opinions, here are biology topics for you:

What are your thoughts on the post-Roe V Wade world?
How can the post-Roe V Wade policy affect developing countries looking up to America for their laws?
Abortion and feminism: discuss
Does saving life justify cloning?
Explain the principle of abortion in medical practice
The effects of cloning in medicine
How does genetics contribute to obesity?
Explain why a parent could have Hepatitis B virus and only one of five offspring have the virus
Is homosexuality really in the gene?
How does depression correlate with genetics?
Additives and how they affect the genes
Examine how genetic mutations work
Discuss the grounds that you could prove for legalizing human cloning
Which is more immoral: Human or animal cloning?
How is nanotechnology different from biotechnology?
Discuss the manifestation of nanotechnology in science
Explain three instances where public opinion has held back scientific inventions
How does transgenic crop work?
Would you say genetically modified food is safe for consumption?
Explain why sexual abuse leads to trauma.

Biology Research Paper Topics

You’d need to write an extensive paper on biology one day. This could be when you’re in your final year in college or the university or submitting to a competition. You’d need Biology topics to research for brainstorming, and here are 30 of them:

Stem cells and tissue formation processes
Why are there different congenital disabilities?
Mixtures in anticancer drugs?
What are the complexities of existing HIV drugs?
What is the contribution of chemotherapy to cancer?
Examine the chemotherapy process and why it doesn’t work for some patients.
Explain the origin of developmental diseases
How do germs affect the cells?
What are the consequences of the sun on the white person’s and black person’s skin?
Why are some diseases treatable through drugs while some are not?
Scientific lessons learned from COVID-19 and ideas to tackle the next virus
If animals are carriers of the virus, what should be done to them?
Examine five animals in extinction and what led to it
Discuss the subject of endangered species and why people should care
Is a plant-based diet sustainable for human health?
Account for the consequence of living on Mars on human health
Discuss the inconveniences involved in space travel
How does space flight contribute to environmental disasters
Discuss the emergence of leukemia
Explain how the immune systems in humans work
Evaluate the factors that weaken the immunological system
What would you consider the deadliest virus?
Autoimmune: what is it, origin and consequences
Immune disorder: origin and how it affects the body
Does stress affect the ability to have sex?
Contribution of vaccine to eradicating disease: Discuss
What are the complexities in taking the Hepatitis B vaccine while being positive?
Allergies: why do humans have them?
DNA modification: how does it work?
Explain the misconceptions about the COVID-19 vaccines.

Interesting Biology Topics

Biology doesn’t have to be boring. Different aspects of biology could be fun to explore, especially if you’ve had a flair for the study since your elementary school classes.

You can either write an essay or paper with the following interesting biology research topics:

Human emotions and conflicts with their intellectual intelligence
Emotions: Its influence on art and music and how the perception of art influences the world
The consequences of marijuana and alcohol on teenagers
Compare and contrast how alcohol affects teenagers and adults
Discuss the contributions of neuroscience to the subject of emotional pain
Explain how the brain process speech
Discuss the factors that cause autism
Explain what is meant when people say humans are animals
Why do scientists say humans are pessimists?
Factors contributing to the dopamine levels human experience
How does isolation affect the human brain?
What factors contribute to instinctive responses?
Noise pollution: how it affects living organisms
Fire ecology: The contributions of plants to fire outbreak
Explain the science behind how hot temperature, soil, and dry grass start a fire
Microbes: what do you understand by bioremediation?
Explain urban ecology and the challenges it pokes to solve
Discuss how excessive internet usage affects the human memory
Evaluate how conservation biology contributes to the extinction prevention efforts
Discuss the role of satellites and drones in understanding the natural world
Why do we need space travel and studies?
Explain the limitations of limnology studies
What are infectious-disease-causing agents all about?
Discuss what epigenetics studies encompass
Why is cancer research essential to the world?
Discuss climate change: Governments are not interested, and there is no alternative
How is behavioral science studies a core part of the understanding of the world?
Discuss the issues with genetic engineering and why it’s a challenge
Evaluate the strengths and weaknesses in the arguments for a plant-based diet
Create a survey amongst students of biology asking why they chose to study the course.

Biology Research Topics For College Students

If you find any of the above beyond your intellectual and Research capacity, here are some topics you can handle. You can use these for your essays, projects, quizzes, or competitions.

These custom yet popular biology research topics will examine famous personalities and other discourse in biology:

Effects of the human hormone on the mind
Why do men get erect even when they’re absentminded?
How does women’s arousal work?
How can melatonin be valuable for therapy?
Risky behavior: Hormones responsible for the risk
Stem and cloning: what is the latest research on the subject?
Hormones: changes in pregnancy
Why do pregnant women have an appetite for random and remote things?
The role of physical activities in hormone development
Examine the benefits and threats of transgenic crops
The fight against COVID-19: assess current successes
The fight against smallpox: assess current successes
The fight against HIV: history, trends, and present research
Discuss the future of prosthetic appliances
Examine the research and the future of mind-controlled limbs
What does cosmetic surgery mean, and why is it needed?
Analyze the meaning and process of vascular surgery
Discuss the debate around changes in genital organs for males and females in transgender bodies
How do donors and organ transplants work?
Account for the work of Dr. Malcom E Miller
Discuss the contribution of Charles Darwin to human evolution
Explain the trends in biomedicine
Discuss the functions of x-rays in botany
Assess the most efficient systems for wildlife preservation
Examine how poverty contributes to climate hazards
Discuss the process involved in plant metabolism
The transformation of energy into a living thing: discuss
Prevention for sexually transmitted disease: What are the misconceptions?
Analyze how the human body reacts to poison
Russian Poisoning: What are the lessons scientists must learn?
COVID-19: Discuss the efforts by two or three governments to prevent the spread
Discuss the contributions of Pfizer during the pandemic.

Marine Biology Research Topics

This subject explains orgasms in the sea, how they survive, and their interaction with their environment. If you have a flair for this field, the following Biology research topics may interest you:

Discuss what quantitative ecology through modeling means
Smallest diatoms and marine logistics: discuss
How is the shark studied?
Acidification of seas: Causes and consequences
Discuss the concept of the immortality of Jellyfishes
Discuss the differences between seawater and freshwater in marine study
Account for some of the oldest marine species
Discuss the evolution of the deep sea
Explain whales’ communication techniques
What does plankton ecology encompass?
The importance of coral reefs to seawater
Challenges that encompass geological oceanography
How tourism affects natural animal habitat
Discuss some instances of the domestication of wild marine animals
Coastal zone: pros and cons of living in such areas
How do sharks perceive enemies?
Analyze why some animals can live in water but can’t live on land
Explain how plants survive in the sea
Compare and contrast the different two species of animals in the water
How can marine energy be generated, stored, and used?

Molecular Biology Research Topics

Focusing on the construct of cells and analysis of their composition, it understands the alteration and maintenance of cellular processes. If you’d like to focus on molecular biology, here are 15 good biology research topics for you:

Ethical considerations in molecular genetics
Discuss the structure and component of the gene
Examine the restrictions in DNA
What are the peculiarities in modern nucleic acid analysis
What goes into the Pharmaceutical production of drugs
Evaluate the building blocks of life
Discuss the systems of RNA translation to protein
PCR: How DNA is tested and analyzed
Why is prion disease so dangerous?
Compare and contrast recessive genes vs. dominant genes
Can there be damage to the human DNA, and can it be repaired?
Constraints in the research of microarray data analysis
Protein purification: How it evolves
Objectives of nucleic acid
Explain the structure of a prion.

Biology Research Topics For High School

Your teachers and professors will be awed if you create impeccable essays for your next report. You need to secure the best grades as you move closer to graduation, and brainstorming any of these popular biology research topics will help:

Identify the most endangered species
The challenges to animal extinction
What are the things everyone should know about sea life?
Discuss the history of genetics
Explain the biological theory of Charles Darwin
How did the lockdown affect social interaction?
Why do some people refuse the vaccine?
Origin of genetics
What is animal hunting, and why is it fashionable
Explain the evolution of a virus
Role of lockdown in preventing deaths and illnesses
Invasive species: What does it mean?
Endangered animals: How do they survive in the face of their hazards?
Lockdown and their role in reducing coronavirus transmission
Vaccine distribution: Ideas for global distribution
Why can viruses become less virulent?
Discuss the evolution of the world
Explain the evolution of the planet
Explain what Elon Musk means when he says life on Mars is possible
What does herd immunity mean?
Flu: why is there a low incidence in 2020?
Relationship between archaeology and biology
Antiviral drug: What it means
Factors leading to the evolution of humans
Give instances of what natural selection means
What is considered the dead branches of evolution
Whale hunting: What it means and the present trends
Who is Stephen Jay, and what is his role in paleontology?
Origin of diseases: why must humans fall sick?
Why are humans called higher animals?

Human Biology Research Topics

Human biology understands humans and their relationship between themselves and their environment. It also studies how the body works and the impediments to health. Here are some easy biology research topics to explore on the subject:

How do gut bacteria affect the brain?
What are the ethical concerns around organ transplants?
The consequence of alcohol on the liver
The consequences of extreme salt on the human body
Why do humans need to deworm regularly?
The relationship between obesity and genetics
Genetically modified foods: Why are they needed?
How sun exposure affects human skin
Latest trends: Depression is hereditary
Influence of music on the human brain
What are the stages of lung cancer
Forensic DNA: latest trends
How visual consumptions affect how humans think
What is the process that leads to pregnancy?
Explain the role of nanotechnology in HIV research
Discuss any experiment with stem cells you know about
Explain how humans consume food
Discuss the process of metabolism as well as its criticality to human health
Explore the consistent challenges technology poses to human health
Explain the process of body decay to a skeleton.

Cell Biology Research Topics

There are many evolutionary biology research paper topics formed not by the nomenclature but for what they stand for. Cell biology is one of the most complex branches of the field.

It examines minor units and the living organisms that make them up. The focus is on the relationship between the cytoplasm, membrane, and parts of the cell. Here are some topics to explore for your scientific dissertation writing :

How does chromatin engage in the alterations of gene expression?
What are the usual cell infections, and why does the body have immunity defections?
Identify and account for the heritage of Robert Brown in his core career focus
Explain the structure of the animal cell and why It’s what it is
Identify the cells in the human body as well as their functions
Explain a scenario and justify the context of animals photosynthesizing like plants
Why do bacteria invade the body, and how do they do it?
Why are mitochondria considered the powerhouse of the cell
Use the molecular analysis tool to explain multicellular organisms
Examine how the White blood cells fight disease
What do you understand about the role of cell biology in the treatment of Alzheimer’s Disease
What are the latest research methods in cell biology?
Identify the characteristics of viruses and why they threaten human existence.
Discuss the differences between DNA and RNA
What part of the body is responsible for human functionality for as long as the individual wants?

Get Biology Research Help As Soon As Possible

Creating the best essays or papers is easier now that you have custom biology research topics. However, you may still need support writing your paper beyond these topic ideas. After all, the first stage of writing like experts is brainstorming ideas and researching which is most feasible to write about.

If you truly want to wow your professor or teacher but can’t afford to dedicate all the required time, here’s an alternative. You can hire writing helpers online for quality papers at a cheap price, and we can help with that. We are a team of writers with many years of writing experience for students in Europe and North America. You can even buy thesis online with us, as well as editing services.

Each paper is assigned to writers with expertise in a specific field. This enables them to provide in-depth analysis as your assignment requires. We’re based online, which means you won’t have issues with accessibility and availability. Just tell us what you need, and we will get it done.

49 Most Interesting Biology Research Topics

August 21, 2023

In need of the perfect biology research topics—ideas that can both showcase your intellect and fuel your academic success? Lost in the boundless landscape of possible biology topics to research? And afraid you’ll never get a chance to begin writing your paper, let alone finish writing? Whether you’re a budding biologist hoping for a challenge or a novice seeking easy biology research topics to wade into, this blog offers curated and comprehensible options.

And if you’re a high school or transfer student looking for opportunities to immerse yourself in biology, consider learning more about research opportunities for high school students , top summer programs for high school students , best colleges for studying biomedical engineering , and best colleges for studying biology .

What is biology?

Well, biology explores the web of life that envelops our planet, from the teeny-tiny microbes to the big complex ecosystems. Biology investigates the molecular processes that define existence, deciphers the interplay of genes, and examines all the dynamic ways organisms interact with their environments. And through biology, you can gain not only knowledge, but a deeper appreciation for the interconnectedness of all living things. Pretty cool!

There are lots and lots of sub-disciplines within biology, branching out in all directions. Throughout this list, we won’t follow all of those branches, but we will follow many. And while none of these branches are truly simple or easy, some might be easier than others. Now we’ll take a look at a few various biology research topics and example questions that could pique your curiosity.

Climate change and ecosystems

The first of our potentially easy biology research topics: climate change and ecosystems. Investigate how ecosystems respond and adapt to the changing climate. And learn about shifts in species distributions , phenology , and ecological interactions .

1) How are different ecosystems responding to temperature changes and altered precipitation patterns?2) What are the implications of shifts in species distributions for ecosystem stability and functioning?

2) Or how does phenology change in response to climate shifts? And how do those changes impact species interactions?

3) Which underlying genetic and physiological mechanisms enable certain species to adapt to changing climate conditions?

4) And how do changing climate conditions affect species’ abilities to interact and form mutualistic relationships within ecosystems?

Microbiome and human health

Intrigued by the relationship between the gut and the rest of the body? Study the complex microbiome . You could learn how gut microbes influence digestion, immunity, and even mental health.

5) How do specific gut microbial communities impact nutrient absorption?

6) What are the connections between the gut microbiome, immune system development, and susceptibility to autoimmune diseases?

7) What ethical considerations need to be addressed when developing personalized microbiome-based therapies? And how can these therapies be safely and equitably integrated into clinical practice?

8) Or how do variations in the gut microbiome contribute to mental health conditions such as anxiety and depression?

9) How do changes in diet and lifestyle affect the composition and function of the gut microbiome? And what are the subsequent health implications?

Urban biodiversity conservation

Next, here’s another one of the potentially easy biology research topics. Examine the challenges and strategies for conserving biodiversity in urban environments. Consider the impact of urbanization on native species and ecosystem services. Then investigate the decline of pollinators and its implications for food security or ecosystem health.

10) How does urbanization influence the abundance and diversity of native plant and animal species in cities?

11) Or what are effective strategies for creating and maintaining green spaces that support urban biodiversity and ecosystem services?

12) How do different urban design and planning approaches impact the distribution of wildlife species and their interactions?

13) What are the best practices for engaging urban communities in biodiversity conservation efforts?

14) And how can urban agriculture and rooftop gardens contribute to urban biodiversity conservation while also addressing food security challenges?

Bioengineering

Are you a problem solver at heart? Then try approaching the intersection of engineering, biology, and medicine. Delve into the field of synthetic biology , where researchers engineer biological systems to create novel organisms with useful applications.

15) How can synthetic biology be harnessed to develop new, sustainable sources of biofuels from engineered microorganisms?

16) And what ethical considerations arise when creating genetically modified organisms for bioremediation purposes?

17) Can synthetic biology techniques be used to design plants that are more efficient at withdrawing carbon dioxide from the atmosphere?

18) How can bioengineering create organisms capable of producing valuable pharmaceutical compounds in a controlled and sustainable manner?

19) But what are the potential risks and benefits of using engineered organisms for large-scale environmental cleanup projects?

Neurobiology

Interested in learning more about what makes creatures tick? Then this might be one of your favorite biology topics to research. Explore the neural mechanisms that underlie complex behaviors in animals and humans. Shed light on topics like decision-making, social interactions, and addiction. And investigate how brain plasticity and neurogenesis help the brain adapt to learning, injury, and aging.

20) How does the brain’s reward circuitry influence decision-making processes in situations involving risk and reward?

21) What neural mechanisms underlie empathy and social interactions in both humans and animals?

22) Or how do changes in neural plasticity contribute to age-related cognitive decline and neurodegenerative diseases?

23) Can insights from neurobiology inform the development of more effective treatments for addiction and substance abuse?

24) What are the neural correlates of learning and memory? And how can our understanding of these processes be applied to educational strategies?

Plant epigenomics

While this might not be one of the easy biology research topics, it will appeal to plant enthusiasts. Explore how epigenetic modifications in plants affect their ability to respond and adapt to changing environmental conditions.

25) How do epigenetic modifications influence the expression of stress-related genes in plants exposed to temperature fluctuations?

26) Or what role do epigenetic changes play in plants’ abilities to acclimate to changing levels of air pollution?

27) Can certain epigenetic modifications be used as indicators of a plant’s adaptability to new environments?

28) How do epigenetic modifications contribute to the transgenerational inheritance of traits related to stress resistance?

29) And can targeted manipulation of epigenetic marks enhance crop plants’ ability to withstand changing environmental conditions?

Conservation genomics

Motivated to save the planet? Conservation genomics stands at the forefront of modern biology, merging the power of genetics with the urgent need to protect Earth’s biodiversity. Study genetic diversity, population dynamics, and how endangered species adapt in response to environmental changes.

30) How does genetic diversity within endangered species influence their ability to adapt to changing environmental conditions?

31) What genetic factors contribute to the susceptibility of certain populations to diseases, and how can this knowledge inform conservation strategies?

32) How can genomic data be used to inform captive breeding and reintroduction programs for endangered species?

33) And what are the genomic signatures of adaptation in response to human-induced environmental changes, such as habitat fragmentation and pollution?

34) Or how can genomics help identify “hotspots” of biodiversity that are particularly important for conservation efforts?

Zoonotic disease transmission

And here’s one of the biology research topics that’s been on all our minds in recent years. Investigate the factors contributing to the transmission of zoonotic diseases , like COVID-19. Then posit strategies for prevention and early detection.

35) What are the ecological and genetic factors that facilitate the spillover of zoonotic pathogens from animals to humans?

36) Or how do changes in land use, deforestation, and urbanization impact the risk of zoonotic disease emergence?

37) Can early detection and surveillance systems be developed to predict and mitigate the spread of zoonotic diseases?

38) How do social and cultural factors influence human behaviors that contribute to zoonotic disease transmission?

39) And can strategies be implemented to improve global pandemic preparedness?

Bioinformatics

Are you a data fanatic? Bioinformatics involves developing computational tools and techniques to analyze and interpret large biological datasets. This enables advancements in genomics, proteomics, and systems biology. So delve into the world of bioinformatics to learn how large-scale genomic and molecular data are revolutionizing biological research.

40) How can machine learning algorithms predict the function of genes based on their DNA sequences?

41) And what computational methods can identify potential drug targets by analyzing protein-protein interactions in large biological datasets?

42) Can bioinformatics tools be used to identify potential disease-causing mutations in human genomes and guide personalized medicine approaches?

43) What are the challenges and opportunities in analyzing “omics” data (genomics, proteomics, transcriptomics) to uncover novel biological insights?

44) Or how can bioinformatics contribute to our understanding of microbial diversity, evolution, and interactions within ecosystems?

Regenerative medicine

While definitely not one of the easy biology research topics, regenerative medicine will appeal to those interested in healthcare. Research innovative approaches to stimulate tissue and organ regeneration, using stem cells, tissue engineering, and biotechnology. And while you’re at it, discover the next potential medical breakthrough.

45) How can stem cells be directed to differentiate into specific cell types for tissue regeneration, and what factors influence this process?

46) Or what are the potential applications of 3D bioprinting in creating functional tissues and organs for transplantation?

47) How can bioengineered scaffolds enhance tissue regeneration and integration with host tissues?

48) What are the ethical considerations surrounding the use of stem cells and regenerative therapies in medical treatments?

49) And can regenerative medicine approaches be used to treat neurodegenerative disorders and restore brain function?

Biology Research Topics – Final thoughts

So as you take your next steps, try not to feel overwhelmed. And instead, appreciate the vast realm of possibilities that biology research topics offer. Because the array of biology topics to research is as diverse as the ecosystems it seeks to understand. And no matter if you’re only looking for easy biology research topics, or you’re itching to unravel the mysteries of plant-microbe interactions, your exploration will continue to deepen what we know of the world around us.

High School Success

Mariya holds a BFA in Creative Writing from the Pratt Institute and is currently pursuing an MFA in writing at the University of California Davis. Mariya serves as a teaching assistant in the English department at UC Davis. She previously served as an associate editor at Carve Magazine for two years, where she managed 60 fiction writers. She is the winner of the 2015 Stony Brook Fiction Prize, and her short stories have been published in Mid-American Review , Cutbank , Sonora Review , New Orleans Review , and The Collagist , among other magazines.

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Open access
Published: 18 May 2020

Detecting qualitative changes in biological systems

Cristina Mitrea 1 , 2 ,
Aliccia Bollig-Fischer 2 , 3 ,
Călin Voichiţa 1 ,
Michele Donato 4 ,
Roberto Romero ORCID: orcid.org/0000-0002-4448-5121 5 , 6 , 7 , 8 &
Sorin Drăghici ORCID: orcid.org/0000-0002-0786-8377 1 , 9

Scientific Reports volume 10 , Article number: 8146 ( 2020 ) Cite this article

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Currently, most diseases are diagnosed only after significant disease-associated transformations have taken place. Here, we propose an approach able to identify when systemic qualitative changes in biological systems happen, thus opening the possibility for therapeutic interventions before the occurrence of symptoms. The proposed method exploits knowledge from biological networks and longitudinal data using a system impact analysis. The method is validated on eight biological phenomena, three synthetic datasets and five real datasets, for seven organisms. Most importantly, the method accurately detected the transition from the control stage (benign) to the early stage of hepatocellular carcinoma on an eight-stage disease dataset.

Data-driven detection of critical points of phase transitions in complex systems

Systems biology in cardiovascular disease: a multiomics approach

A census of pathway maps in cancer systems biology

Introduction.

In most, if not all, non-trauma health-care cases, pathological conditions are defined by phenotypic or clinical changes. For example, cancer is usually diagnosed after the patient experiences symptoms caused by significant transformations in their physiology. However, the progression from a healthy state to one of disease is gradual, happening over a period of time. This is particularly true in the case of conditions such as cancer or neurodegenerative disorders, for which the onset of the underlying pathology is believed to begin much earlier than the clinical, detectable onset 1 , 2 . What if one could identify a departure from the healthy state well before a tumor is present, when changes can perhaps still be reversed? What if one could identify qualitative changes in the states of a biological system without even knowing what the states are? Here, we propose a technique that aims at identifying such qualitative changes without any a priori knowledge about the nature of the changes. The preliminary results herein demonstrate the potential of this approach using several datasets derived from eight biological phenomena and seven organisms.

The goal is to develop an approach that can detect qualitative changes in the system, where a qualitative change is defined as a change that involves observable macroscopic phenotypical or clinical changes. We should emphasize that no known approach is available to tackle this type of problems. There are no clearly defined states or classes available a priori, so no supervised machine learning approaches can be used. We would like to be able to detect changes as they happen if possible, without massive amounts of partially redundant data collected beforehand, so no unsupervised methods could be used to extract common features and build clusters. Here we are looking at a system without having a reference set of genes, so no enrichment approach will be useful. Finally, there is no predefined phenotype, and therefore no gene set analysis methods can be employed either. What we would like to achieve here is a method capable of (1) monitoring the activity of a system by taking periodic measurements and (2) detecting when a specific system undergoes a qualitative change without prior knowledge about it. To the best of our knowledge, no existing method could approach this task with a reasonable chance of success.

In this paper, we propose a qualitative change detection (QCD) approach, an analysis method that uses sequential measurements as described by a time series (or by progressive disease stages), together with all known interactions described by biological networks, and that applies an impact analysis approach to identify the time interval in which the system transitions to a different qualitative state.

In practical terms, the data to be analyzed is a time series of gene expression or any other sequential measurements of systemic states such as the one described in disease progression. Time-series data have been used in many ways, e.g. to infer information regarding regulatory mechanisms, the rate of change for a gene, the order in which genes are (de)activated, and the causal effects of gene expression changes 3 . Often, time series-data are used to extract gene profiles that can be be used to better understand the phenomena or phenotypes 4 , 5 , 6 , 7 . The analysis of time-series data can also be used to identify disease biomarkers either as a single gene, a group of genes, or a network of genes 8 .

In the landscape of analysis methods for high-throughput data (see Fig. 1 ), the proposed method falls under the category of dynamic network analysis. Other methods in the same category aim to either identify significantly perturbed systems 9 , time intervals with the highest difference in expression for each gene from a predefined set 10 , dynamic network biomarkers using local network entropy 11 , or time periods of differential gene expression using Gaussian processes 12 . However, all of these approaches perform comparisons between disease profiles and a reference profile (e.g. healthy). In the paradigm proposed here, none of these existing methods can be applied because the goal is to identify a transition to a qualitatively different state without knowing the gene expression profile of the new state, and hence, without the ability to make a comparison between the control and disease phenotypes.

Overview of existing approaches as categorized by looking at the time component (horizontal axis) and the system information (vertical axis). From the time component perspective one can distinguish between two categories: snapshot data and time course data. Time course data is richer in information but also has increased complexity as opposed to snapshot data. From the system information perspective one could consider sets of genes together with their interactions (pathways) or without such interactions (gene sets). Pathways are much richer in information but also have increased complexity as opposed to gene sets. Based on these categories, the existing methods can be divided into the four groups shown, of which the gene set analysis is the most common, including more than 70 methods 64 , 65 . Gene set analysis takes as input a collection of gene sets and a snapshot of expression data that compares two phenotypes and ranks the gene sets based on their relevance to the phenotype computed by the analysis. Pathway analysis has the same workflow as the gene set analysis but also takes into consideration the interactions between the genes as described by the topology of the pathways 66 , 67 . Network discovery from time course data takes as input data collected at multiple time points and a set of genes and infers relations between the genes in the input set 68 . Network dynamic analysis is the most recent, and has only 4 existing methods 10 , 11 , 12 , 69 . Methods in this category (including the proposed method) use time series data and pathways to gain knew knowledge about the underlying phenomena.

A biological system is characterized by a tendency to reach and maintain a state of homeostatic balance, considered to be a stable state. An alteration made by internal or external stimuli can trigger the system to transition from one stable state to another, referred to as a qualitative change. Notably, any of the system components taken in isolation may not vary dramatically; however, the system as a whole may undergo a qualitative change. Conversely, in a resilient system, important variations of one or a group of components may happen without necessarily involving a qualitative systemic change. Importantly, most systems have built-in tolerance mechanisms such that the response to a stimulus is delayed until the signal is perceived as real in order to filter noise and to conserve the energy necessary to undergo a systemic change.

We developed and implemented a data analysis method capable of detecting qualitative changes in biological systems despite these challenges. The workflow of the analysis is summarized in Fig. 2 . The input to QCD consists of: (i) time-series data and (ii) a network model of the biological system under study. QCD uses the input data to evaluate the system perturbation between each pair of time points/system states using a pathway impact analysis approach 13 , 14 , 15 , 16 . An expectation maximization algorithm is then used to separate large and small system perturbations values, thus identifying important differences between those states. Lastly, the analysis finds the disjunct overlaps of the intervals with large system perturbation that identify one or more time intervals during which the biological systems has undergone qualitative changes, referred to henceforth as change intervals.

Workflow of the QCD method. The algorithm takes as input time series data and network(s) that models the biological system. The time series data is used to compare every pair of time points (time interval). In STEP I, a pathway impact analysis is used to compute a perturbation score for each comparison. In STEP II, an expectation maximization algorithm is employed to identify the parameters of a gamma mixture model and select the interval(s) when the system/pathway/network experienced a large perturbation. In STEP III, change intervals are selected by identifying the overlap of the set of intervals with large system perturbation and selecting the narrowest disjunct time intervals.

Conventional approaches to the analysis of time series gene expression data are extremely useful tools to identify genes that are behaving in a similar way. However, these methods are not designed to identify systemic changes. The goal of the proposed approach is to identify transitions from one state to another, rather than study a particular state or a particular time profile. Our goal is to show that the proposed approach is able to identify such meaningful transitions across different organisms and various phenomena.

The analysis of eight well-studied phenomena was performed with the proposed method (QCD) for seven model organisms using both synthetic and real data. To assess the ability of QCD to detect qualitative changes, results were compared to prior knowledge of the phenomenon under study. QCD uses system knowledge, as described by a known gene signaling network or a map of neurons and their synaptic connections, as well as sequential measurements of the system components (genes or neurons). Data were obtained by measuring either the mRNA level of the genes involved in the system, in the case of real data, or generated based on equations describing the model of each organism, in the case of synthetic data.

The results of the analyses show that QCD can reliably identify the time interval during which a biological system goes from one qualitative state to another in response to organism development or to a shift in environmental conditions. We evaluate the method using phenomena that involve major physiological changes. We also evaluate the method for phenomena involving more subtle, yet important changes. Major physiological changes analyzed using synthetic data are E. coli flagellum building 17 , 18 and B. subtilis sporulation 17 , 19 . The subtle change analyzed using synthetic data is C. elegans avoidance reflex 17 , 20 . Major physiological changes analyzed using real gene expression data are yeast sporulation 21 and fruit fly pupariation 22 . More subtle changes analyzed using real gene expression data involve fruit fly ethanol exposure 23 .

QCD was compared with an existing method developed by Liu and colleagues used to detect network biomarkers and the pre-disease state (herein abbreviated DNBM) 11 . In addition to the six datasets mentioned above, we also ran QCD on the two datasets from the Liu et al . study. The first dataset is derived from a mouse study of exposure to a toxic gas (carbonyl chloride). Using these data QCD identified one qualitative change, before the exposure became lethal, preceding the pre-disease state detected by Liu et al . The second dataset contains data describing the progression of human hepatocellular carcinoma. Using these data, QCD identified a qualitative change from a benign stage (control) to a pre-malignant stage (high-grade dysplastic nodules), also preceding the pre-disease state detected by the Liu et al . study.

Bacterium flagellum building

When in an environment lacking nutrients, the E. coli bacterium initiates the process of building a flagellum that will provide the motility necessary for finding an environment rich in nutrients.

We analyzed the process of building the E. coli flagellar motor, using synthetic data and the flagellum building network 18 (see Fig. 3A ). Previous studies describe this network as a multi-output coherent type 1 feed-forward loop (C1-FFL) 18 , 24 . A C1-FFL is a network in which one gene activates another and, together, they activate another gene or (groups of) genes in the multi-output networks 24 , 25 .

The input and results of the qualitative change detector (QCD) for the E. coli flagella building phenomenon. Panel (A) The multi-output coherent type 1 feed-forward loop (C1-FFL) network that describes the flagellum building, together with the activation thresholds ( $\beta $ on the edge) for each of the six groups of genes (dark green boxes) 17 , 18 . The flagellum building is depicted in the cartoons matching the activation of each group of genes. The black box denotes building the flagellum hook which is the point of no return in this process and hence the real change interval that we aim to discover. Panel (B) The heatmap of the sampled data (input to QCD), and the real change interval (black arc below the heatmap and black vertical line positioned in the center of the interval) as described by literature. The change interval detected by QCD is shown by the green arc below the heatmap and the green vertical line positioned in the center of the interval (very close to the black line showing the actual point of no return). The stages of the flagella building are presented as cartoons in chronological order on the top part of the figure.

The flagella building network is a generalization of the C1-FFL. In essence, the flagella building network is a multi-output C1-FFL in which the exact timing of the sequence of steps is controlled by the different activation thresholds (see the edge labels in Fig. 3A ). These thresholds ensure that all the elements of the flagellum are built in a specific order so that it can properly assemble (e.g. the base of the structure must be in place before all other elements). Due to the different activation thresholds, a reverse order of the activation thresholds for $flhDC$ and $fliA$ yields a first-in first-out (FIFO) order in the gene transcription. This is typical of sensory transcription networks as a mechanism used to filter out (not react to) noise containing false positive signals of short duration.

Gene expression data was generated for the flagellum building network for a period of $10$ hours using a continuous function that models the protein accumulation and parameters from previous studies 17 , 18 . Samples were taken every 30 minutes leading to a gene expression time course dataset with 21 time points. Panel B in Fig. 3 shows the evolution of gene expression over time for the genes involved in this phenomenon.

Importantly, the organism commits to building the flagellum when the first hook of the flagellar motor starts to be built ( $fliA$ reaches the threshold to regulate the next group of genes, $fliD$ and $flgK$ ) 18 . This is an important check point in the flagella building process as the assembly of the following component can still be halted if necessary 26 . However, after this checkpoint, the bacterium commits to building the flagellum (see the top of panel B in Fig. 3 ). For these reasons, the interval between 240 and 270 minutes can be considered the boundary that separates the two qualitatively different states: with and without flagellum. The goal of our approach is to find this interval without any knowledge about the phenomenon and with knowledge only from the gene expression data and the network of the system.

The E. coli flagellum construction is controlled by two transcription factors, $flhDC$ and $fliA$ (see Fig. 3A ). The master regulator $flhDC$ activates $fliA$ and there is an $OR$ relationship through which these two master regulators activate the other genes in the network ( $12$ genes). The genes are part of $6$ groups: (i) $fliL$ , (ii) $fliE$ and $fliF$ , (iii) $flgA$ , $flgB$ , $flhB$ , (iv) $fliD$ , $flgK$ , (v) $fliC$ , (vi) $meche$ , $mocha$ and $flgM$ .

QCD compares all system states (time points) to each other using a pathway impact analysis. In essence, the state of the system at each time point is compared to the state at all other time points using a pathway impact analysis 13 that takes into consideration all gene expression changes, the position of each gene on the pathway (Fig. 3A ), and the type and direction of every interaction to determine if the state of the system was altered. The result of this impact analysis is a set of system perturbation factors that quantify the system perturbation. To determine the significant system perturbations, we assume there are two types of intervals: i) those with large perturbations between the states involved, and ii) those with small perturbations caused only by random fluctuations. We then use an expectation maximization algorithm to fit a gamma mixture model of two distributions to the perturbation factors (see Fig. 4 ). The intersection of the two distributions will be the optimal threshold that can be used to separate the large perturbations from the small perturbations as presented in Fig. 4A . Using this approach, we assign a “large” or “small” perturbation status to each comparison. Panel B in Fig. 4 shows all the state comparisons considered, in which the gray and black arcs show small perturbations and the red arcs show large perturbations between the states of the system at those time points.

Identifying the qualitative change interval for the E. coli flagella building phenomenon. Panel (A) Identifying state comparisons involving large perturbations. The black line shows the observed density of the perturbation values for all pairwise comparisons of system states. We assume that some comparisons will be characterized by large perturbations, while others by small perturbations. A mixture of two gamma distributions are fitted to the observed data to yield the distributions of large (red) and small (blue) perturbation whose mixture best fits the observed data (red and blue lines). The intersection point (yellow vertical line) is the optimal threshold used to distinguish between the large and small perturbations. Panel (B) The arcplot of all comparisons performed by QCD between all pairs of system states. Red arcs, above the x axis, represent comparisons that show a large perturbation, while gray arcs, below the x axis, represent comparisons with a small perturbation. All the comparisons between states in the intervals S0–S6 and S10–S20 are associated with small perturbations. At the same time, the vast majority of all possible comparisons between any state in the interval S0–S6 and any state in the interval S10–S20 are associated with large perturbations. The black arcs are comparisons between a state in the interval S0–S6 and a state in the interval S10–S20 that are associated with small system perturbations. The smallest interval of overlapping large perturbation intervals, the interval between S6 and S10, is the detected change interval.

In essence, most of the comparisons between any time point earlier than 180 mins and any time point after 300 mins show large perturbations (exceptions are marked by the black arcs). This suggests that a qualitative change of the system occurs between 180 and 300 mins, which is indeed the case. The real change takes place between 240 minutes, when $fliD$ and $flgK$ expression begins, and 270 minutes, when $fliA$ starts to regulate the next group of genes and the building of the first hook of the flagellar motor begins.

The identification of a change interval should be followed by an analysis of the states of the system before and after a change interval in order to gain insight into the system transition. Without loss of generality, we will consider the situation in which there is a single change interval, as in this dataset. Furthermore, we also assume that the system is in a stable state before and after the change interval. Under these circumstances, we can group the states in which the system is stable into meta-states.

A meta-state is a group of consecutive states where all comparisons between states within a meta-state have a small perturbation and all comparisons between states from a meta-state to states outside it (excluding the states in any change intervals) have a large perturbation.

The results shown in panel B of Fig. 4 suggest that states S0–S6 might form a meta-state, MS1. Similarly, the states S10–S20 might define a second meta-state, MS2. To investigate these potential meta-states, all comparisons (arcs) were studied from the perspective of the above definition of a meta-state. From this perspective, all these comparisons can be either consistent or inconsistent with the expectations noted above. This is a binary choice, and under the null hypothesis in which there are no meta-states, the probability that a comparison is consistent or not should be 0.5. Based on this framework, a binomial model can be used to calculate a p-value characterizing the amount of evidence that indicates the existence of a true meta-state (comparisons consistent with the definition vs. inconsistent comparisons). More details can be found in the Methods section, subsection “Meta-states statistical validation”. Groups of states with significant p-values will be reported as meta-states.

In this case, both groups of states identified by QCD had highly significant p-values: $p=5.44\times 1{0}^{-19}$ for meta-state 1 (S0–S6) and $p=3.61\times 1{0}^{-28}$ for meta-state 2 (S10–S20).

Bacterium sporulation

When deprived of food, the B. subtilis bacterium turns into a spore, a robust structure able to survive in an environment lacking nutrients. This is a crucial feature that ensures the bacterium’s survival in an environment scarce in food in which it cannot survive in its active form.

Compared to the E. coli flagellum-building network, which includes only activation signals, the network controlling sporulation also includes repression signals (Fig. 5A ). This network has a hierarchical structure that consists of four transcription factors: $sigmaE$ , $sigmaK$ , $GerE$ , $SpoIIID$ and three groups of genes $Z1$ , $Z2$ , $Z3$ . This network is comprised of two network motifs, each of them represented by two networks. The two coherent feed-forward loops (C1-FFLs) aim at $sigmaK$ and $Z3$ , respectively, while the two incoherent type-1 feed-forward loops (I1-FFLs) are centered around $Z1$ and $Z2$ , respectively. The C1-FFLs are denoted as coherent because their central genes, $sigmaK$ and $Z3$ , respectively, receive activation signals from both genes upstream of each. Specifically, $sigmaK$ receives activation signals from both $SpoIIID$ and $sigmaE$ , while $Z3$ receives activation signals from both $GerE$ and $sigmaK$ . In contrast, the incoherent network is characterized by a gene that receives one activation and one repression signal from the two genes immediately upstream of the target gene. For example, $Z1$ is activated by $sigmaE$ but repressed by $SpoIIID$ .

The input and results for QCD for B. subtilis sporulation. Panel (A) shows the sporulation network. The genetic network represented by the two coherent type 1 feed-forward loops (C1-FFLs) and two incoherent type-1 FFLs (I1-FFLs) that describe the sporulation network as reported in previous studies 17 , 19 . Panel (B) shows the heatmap of the sampled data. The real change interval is shown by the black arc below the heatmap (black vertical line positioned at the average of the interval limits) as described by literature. The change interval detected by the proposed method is shown by the green arc (green vertical lines positioned at the average of the interval limits), match perfectly with the actual timing of these events.

The gene expression data was sampled from the spore formation network for a period of $10$ hours using a continuous function that models the protein accumulation and parameters observed in previous studies 17 , 19 . Samples were taken every 30 minutes leading to a gene expression time course dataset with 21 time points.

Importantly, the organism commits to the spore formation when the second suppressor ( $GerE$ ) is expressed (4 h = 240 min) 19 . In turn, $GerE$ is regulated by $sigmaK$ which also regulates the communication between the mother cell and the spore through a checkpoint that is crucial for the formation of viable spores. Hence, the true interval of change is the interval between 210 minutes, when $sigmaK$ shows the first change in expression, and 240 minutes, when $GerE$ shows the first change in expression.

Our method was applied on the sporulation network and the synthetic gene expression dataset obtained by the above sampling. In these data, QCD identified one change interval (210–240 min) (Fig. 5B ). The detected interval exactly matches the time interval between the time when the spore formation starts ( $GerE$ is being expressed) and up to the moment when the next group of sporulation genes ( $Z2$ ) is activated.

We also evaluated the two groups of system states: before the change interval (0–210 min) and after the change interval (240–600 min), as potential meta-states MS1 and MS2, respectively. The p-value for each was highly significant: $p=2.31\times 1{0}^{-19}$ , for MS1, and $p=6.23\times 1{0}^{-32}$ , for MS2. These p-values validate the hypothesis that these are true meta-states. Interestingly, these meta-states can be mapped to the rod-shaped bacterium form and the endospore form, respectively, while the detected change interval can be associated with the process of spore formation. These results are consistent with previous studies and interpretations 19 . Specifically, the sigmaK factor expression was identified as the critical control element in the regulatory mechanisms and the coordination of spore formation between the mother cell and forespore. In particular, sigmaK activates GerE which in turn triggers the expression of the last set on genes. For this reason, the true time point that can be considered as separating the rod-shaped bacterium from the endospore state is the point in which sigmaK becomes expressed (shown by the black line in Fig. 5 ). Before the change interval, the bacterium preserved most of its initial characteristics, while after this interval, the bacterium assumed most of the characteristics of an endospore. During the change interval, the system exhibited characteristics of both “spore” and “no spore” states. To conclude, in the study of Bacterium sporulation phenomenon, DQC accurately identified the transition from the rod-shaped bacterium form (no spore) to the endospore (spore) form.

Worm avoidance reflex

A phenomenon involving more subtle changes is the nociception reflex. Nociception is a sensory process that allows the detection of harmful stimuli and activates a reflex response to move a part of the body or the whole body away from the stimulus. Nociceptors are present in fish, worms, and fruit flies, among others, and help trigger an avoidance reflex such as a backward movement. In the roundworm ( C. elegans ), the avoidance reflex network is composed of two parallel receptor neurons that communicate with two sequential command neurons (Fig. 6A ).

The input and results for QCD for the C. elegans avoidance reflex. Panel (A) top: The network that describes the avoidance reflex network as presented in previous studies 17 , 20 is a multi-input coherent type 1 feed-forward loop (C1-FFL) with two inputs. Synaptic weights are marked by the $\beta $ values on the edges. Panel (A) bottom: The signal dynamics of the avoidance reflex network. Panel (B) The heatmap of the sampled data (which is the input to QCD) and the real change interval shown here by the black arc below the heatmap (black vertical line positioned in the center of the interval) as described by literature. The change interval detected by the proposed method and shown by a green arc below the heatmap (vertical lines positioned in the center of the interval), matches almost perfectly with the actual timing of these events.

The C. elegans avoidance reflex network is a generalization of the C1-FFL in the form of a multi-input C1-FFL. As previously described, C1-FFL is a network of three nodes in which one node activates another and, together, they activate another node 24 , 25 . In multi-input C1-FFL networks, the initial activation is performed by multiple nodes or groups of nodes rather than by just one node. $ASH$ is the main nociceptor and triggers avoidance behavior in response to harmful stimuli such as the nose touch and volatile chemicals. $FLP$ is a sensory neuron triggered by painful, heat-related stimuli or mechanical stimuli, such as a harsh nose touch, that initiates the nematode’s backward movement. $AVD$ is a command interneuron that functions as a modulator for backward locomotion induced by a head touch. Neurons $AVA$ and $AVD$ drive the worm’s backward movement.

Neuronal signal data was generated for the avoidance reflex network over a period of $8$ milliseconds, using a continuous function that models the signal processing and parameters observed in previous studies 17 , 20 . Samples were taken every millisecond leading to a time course dataset with eight time points.

The nematode commits to the backward movement at 3ms, which is the moment the nose touch ( $FLP$ - spiking function) reaches the threshold to trigger the second command interneuron ( $AVD$ ). The movement starts at 5ms when the $AVA$ neuron starts firing 20 . The two time-points mark the 3 to 5 ms time interval which is the real change interval. Using these data, QCD identified the narrower 4 ms to 5 ms interval (Fig. 6B ).

In addition, the two groups of system states, before and after the change interval, were evaluated as potential meta-states. The p-values for the two groups of states are highly significant: $p=4.28\times 1{0}^{-4}$ for meta-state 1 and $p=4.28\times 1{0}^{-4}$ for meta-state 2. In summary, in the case of the avoidance reflex, the detected change interval is a transition between “no movement” and “moving backward” meta-states.

Results of the first three case studies, for which we used synthetic data, proved that QCD can be quite accurate. However, in practice, the data from real biological experiments can be very noisy. In order to investigate the capabilities of this approach to detect the correct change interval from real gene expression data, we used datasets collected from three different experiments: yeast sporulation, fruit fly metamorphosis, and acute ethanol exposure (see Figs. 7 , 8 and 9 ). All data are available in the public domain in the Gene Expression Omnibus (GEO) 27 , 28 . Again, we chose different phenomena and different model organisms for a thorough method evaluation.

Baker’s yeast sporulation

Starvation for nitrogen and carbon sources (high stress) induces meiosis and spore formation in diploid yeast ( S. cerevisiae ) cells. Stress-tolerant haploid spores are formed through cell division (meiosis) within the mother cell. This is a qualitative and obvious physiological change in yeast cells adapting to their environment. The sporulation process has been thoroughly studied and is well understood 21 , which makes it a good candidate on which to validate QCD.

The input and results for QCD for yeast sporulation. The input is the regulation of autophagy pathway from KEGG 29 – 31 (sce04140)*, in Panel (A), and gene expression data from the GEO dataset GSE27, in Panel (B). The data captures the sporulation phenomenon, specifically the transition from diploid cells through meiosis to the spore cells. Panel (B) shows the heatmap of the time course (0 to 11.5 hours) for the measured KEGG pathway genes (in red), with the change interval detected for the phenomenon (green arc and the green vertical line in the center of the interval (0.5–7 h)), as well as the real change interval (black arc and the black vertical line in the center of the interval (2–7 h)). *For details about the pathway notations see the KEGG legend at: https://www.genome.jp/kegg/document/help_pathway.html .

We used the Kyoto Encyclopedia of Genes and Genomes (KEGG) 29 , 30 , 31 pathway database as a source for the biological networks describing the studied phenomena. The regulation of autophagy pathway (KEGG ID: sce04140) describes the phenomena involved in sporulation. This pathway consists of mechanisms involved in processing internal and external stresses including nutrient availability. As a result, regulation of autophagy is essential for survival because it is used to maintain important cellular functions when environmental conditions change.

The QCD method was applied on the regulation of autophagy pathway and gene expression data from the yeast sporulation study by Chu et al . (GSE27, 21 ). Panel A in Fig. 7 shows this pathway, as well as the genes measured in this experiment, marked in red. The experiment spanned 11.5 hours and data were collected at seven unequally spaced time points (0, 0.5, 2, 5, 7, 9, and 11.5 hours). The experiment was designed such that the sampling captures all known stages of the biological process. Sporulation is divided into four major stages: early, middle, mid-late, and late 21 .

The commitment to sporulation starts in the middle stage (2–5 h) and spans the mid-late stage (meiosis II phase, 5–7 h) 21 . Therefore, the true change interval for this phenomenon is 2 h to 7 h. As observed by Chu et al ., the transition phase ends after the mid-late stage. This study also showed that one of the first discernible steps of spore morphogenesis occurs after the meiosis II spindles are formed, which makes the late phase a stable one. Also, the middle-late phase is still part of the change interval as previous studies reported that the middle-late phase includes the major cytological events of sporulation 32 , 33 . Panel B of Fig. 7 displays the measured changes of the genes on the regulation of autophagy pathway over the time course noted above.

In this case, QCD identifies a qualitative change in the interval from 0.5 h to 7 h, which includes the real change interval (2 h to 7 h) and starts one time point earlier. The change interval is the transition that separates the two potential meta-states (active state and spore state). The active and spore potential meta-states have p-values of $p=0.062$ and $p=0.0195$ , respectively.

Sometimes small gene-level changes (not noticeable by eye) across the system can lead to important systemic changes. This is exactly the problem that our method was designed to address: the inability to easily identify important qualitative changes when they happen incrementally. The transition from healthy to disease is in many cases similar to the transition from young to old: any two consecutive measurements taken at short intervals are unlikely to show any important changes. However, the transition is happening and at some point, the current state will be significantly different from states long before. Our method is designed precisely for the purpose of detecting such changes and distinguishing them from mere random fluctuations present in any stable state.

Fruit fly metamorphosis

Three major states — egg, larva and pupa — occur during the development of the fruit fly. The larvae typically pass through three molting stages (instars) during which they shed various body elements and form new ones. Importantly, the third molting stage the larvae pupate and become adults, which marks the completion of the metamorphosis process.

The input and results for QCD on fruit fly metamorphosis (pupariation). The input is the Hedgehog pathway from KEGG 29 – 31 (dme04340), in Panel (A), and gene expression data from the GEO dataset GSE3057, in Panel (B). The data captures the pupariation phenomenon, specifically transition from the end of the larva stage through the prepupa stage and to the beginning of the pupa stage of the fruit fly. Panel (B) shows the heatmap of the time course ( $-18$ to 12 hours) for the measured KEGG pathway genes (in red), with the change interval detected for the phenomenon (green arc and the green vertical line in the center of the interval ( $-18$ –0 h)), as well as the real change interval (black arc and the black vertical line in the center of the interval ( $-4$ –0 h)).

The QCD method was applied on the Hedgehog signaling pathway from KEGG 29 , 30 , 31 (pathway ID: dme 04340) and data publicly available for the metamorphosis of D. melanogaster (GSE3057, 22 ). The Hedgehog signaling pathway, named after the signaling molecule Hedgehog (Hh), has a crucial role in organizing the body plan for the fruit fly during development. Panel A in Fig. 8 shows this pathway as well as the genes measured in the metamorphosis experiment (in red in this figure). The experiment started 18 hours before pupariation, spanned 30 hours, and was sampled at nine time points, two prior to pupariation ( $-18$ hours and $-4$ hours), and the other seven time points equally spaced over 12 hours after the actual pupariation (0 h, 2 h, 4 h, 6 h, 8 h, 10 h, 12 h).

Panel B of Fig. 8 shows the measured changes of the genes on this pathway over the time course described above. Puparium formation is triggered at the end of the third instar larvae stage that occurred during this experiment in the interval from $-4$ hours to 0 hours, and is marked by a high peak of the steroid hormone 20-hydroxyecdysone 22 . A second peak of the steroid hormone 20-hydroxyecdysone occurs roughly at the 10-hour time point and triggers the transformation from prepupa to pupa 22 . Puparium formation represents the onset of metamorphosis; therefore, the real change interval for this case study is indeed from $-4$ hours to 0 hours. The QCD method identifies one change interval from $-18$ hours to 0 hours. Notably, the third instar larvae stage, which starts 24 hours before pupariation and lasts until 0 hours (prepupae phase starts), is not a stable state in which the organism (fruit fly) exists. Therefore, the QCD not only correctly identifies the qualitative transition from larva to pupa, but it also shows the organism is in a continuous transition during the third instar larvae stage. The second change in this experiment (prepupa to pupa) arguably perturbs the system less than the first one since both prepupa and pupa are part of the pupal stage.

Notably, in this case study the change takes place at the beginning of the time course. To determine potential-meta-states relative to this change interval, we selected the only state before the change interval ( $-18$ h) as the potential meta-state 1 and all states after the change interval (0 h–12 h) as potential meta-state 2. These two meta-states are characterized by highly significant p-values: $p=7.81\times 1{0}^{-3}$ and $p=3.73\times 1{0}^{-9}$ , respectively.

Fruit fly acute ethanol exposure

The fruit fly has been used as a model to study drug addiction. In the fruit fly, drug addiction produces physiological effects similar to those observed in mammals because the cellular neuronal mechanism that mediate the signals from the chemical compounds found in these drugs is conserved across these species.

The input and results for QCD on fruit fly ethanol exposure. The input is the Hedgehog pathway from KEGG 29 – 31 (dme04340) in Panel (A), and gene expression data from GEO GSE18208, in Panel (B). The data captures the acute ethanol exposure phenomenon, specifically transition from the “sober” stage through the “drunk” stage and back to the “sober” stage. Panel (B) shows the heatmap of the time course (control, 0 to 3.5 hours) for the measured KEGG pathway genes (in red), with the change interval detected for the phenomenon (green arc and the green line in the center of the intervals (0.5–1 h) and (1–1.5 h), as well as the real change interval (black arc with a black line in the center of the interval (1–2 h)).

To apply the QCD method, we used the Hedgehog signaling pathway (KEGG ID: dme04340) and the acute ethanol exposure data available from GEO (GSE18208) and described by Kong et al . 23 . The Hedgehog signaling pathway was chosen for its capability to model major mechanisms involved in fruit fly development, including its adaptive mechanisms. Panel A in Fig. 9 displays this pathway, as well as the genes measured in this experiment, marked in red. Panel B of Fig. 9 shows the measured changes of the genes on this pathway over the time course from the biological experiment. The experiment spanned 3.5 hours (210 minutes) of recovery after a 30-minute ethanol exposure, sedating up to 75% of the flies. Samples were taken at eight time points. The time points include one control, before exposure, one at 0 hour, right after exposure and every 30 minutes after that up to 3.5 hours; the missing data point at 2.5 hours (150 min) was not provided in the dataset. This experiment’s treatment conditions included exposure to humidified air or ethanol vapor (60%) for 30 minutes, and then recovery for up to 210 minutes 23 . The recovery period from ethanol sedation has been reported by another study to be approximately between 40 minutes and 2 hours 34 , which is the real change interval. Based on this recovery time, by the end of this experiment (210 minutes), the fruit flies should recover from the effects of ethanol exposure. In the GSE18208 dataset 40 minutes was not one of the sampled time points; therefore, to mark the real change interval, we used the very next time point available in the dataset, the one-hour time point.

The intuitive physiological transitions expected for these data are from no exposure (sober) to exposure to ethanol (drunk) and back to fully recovered (sober). However, the drunken state is temporary, since it is followed by recovery. Because of this transition, we expected two change intervals, from sober to drunk and from drunk to sober. Furthermore, the initial and end states (sober before exposure and sober after recovery) were expected to be very similar from a gene expression point of view. In other words, the sober state is the same in the initial and final state in this case, as opposed to the flagellum building case where the initial and final states, with and without flagellum, are obviously different.

The ethanol exposure has a delayed effect at the gene level. According to Kong et al ., the expression of immunity genes increased after ethanol exposure in the time range from 0.5 hours to 1.5 hours 23 . Because of this delayed effect, we did not expect the biggest changes between the control and 0 hours but rather between the control and some later time point(s).

The QCD results on these data have shown that the biological system indeed goes through two qualitative changes, and the change intervals are: 0.5 hours to 1 hour and 1 hour to 1.5 hours, matching the expected transitions from a sober state to a drunken state and then back to the sober state. The effects of the ethanol exposure appear to peak at the 1-hour time point. Based on the change intervals and the return of the system to its initial state, there are two groups of states that may form meta-states. These potential meta-states consist of the following time points: control, 0 hour, 0.5 hours, and 1.5 hours to 3.5 hours, for meta-state 1, and the 1-hour time point for meta-state 2. The distribution of the significant and non-significant transitions yielded a highly significant p-value, $p=1.37\times 1{0}^{-5}$ , for meta-state 1, but a non-significant p-value ( $p=0.22$ ) for meta-state 2. This result is probably due to the small number of comparisons involving the single time point included in meta-state 2.

Human hepatitis C virus (HCV) infection to hepatocellular carcinoma (HCC) progression

Hepatocellular carcinoma (HCC) is a common liver cancer that can be the result of an infection with the hepatitis C virus (HCV). The progression from HCV infection spans multiple disease stages before reaching HCC, as reported by Wurmbach et al . 35 . We used the data from this study to identify qualitative changes for this phenomenon. The dataset (GSE6764, 35 ) contains gene expression collected from 75 samples (48 patients) and covers eight progressive stages of HCV induced HCC: four no-cancer stages including no HCV/control, cirrhosis, low-grade dysplastic, and high-grade dysplastic, and four cancer stages including very early HCC, early HCC, advanced HCC, and very advanced HCC. Normal liver control is used as the initial stage and stages are ordered by disease progression.

To apply QCD on these data, we used the viral carcinogenesis pathway from KEGG 29 , 30 , 31 (hsa05203) as the network/map of the biological system. The viral carcinogenesis pathway describes the signaling mechanisms involved in inflammatory responses such as the one triggered by HCV. Panel A in Fig. 10 shows this pathway as well as the genes measured in this experiment marked in red. Panel B of Fig. 10 shows the measured changes of the genes on this pathway over the different disease stages from the biological experiment.

The input and results for QCD on human hepatitis C virus (HCV) to hepatocellular carcinoma (HCC) progression. The input is the viral carcinogenesis pathway from KEGG 29 – 31 (hsa05203), in Panel (A), and gene expression data from GEO GSE6764, in Panel (B). The data captures the progression from human HCV to HCC, specifically the transition from control (healthy) through the progressive stages of liver damage up very advanced HCC. Panel (B) shows the heatmap of the disease progression (control to very advanced HCC) for the measured KEGG pathway genes (in red), with the change interval detected for the phenomenon (green arc and the green line in the center of the interval (control – high-grade dysplastic nodules)). The dark green vertical line (very early HCC) marks the pre-disease state detected by the DNBM method).

From these data, the QCD identified one qualitative change (change interval) from stage zero (control), a benign state to stage three (high-grade dysplastic), the last of the four benign states and a state in which treatments are effective. The group of states before the change interval was considered as potential meta-state one (MS1) and contains only the control state. The group of states after the change interval was considered as potential meta-state two (MS2) and contains five states: high grade dysplastic nodules, very early HCC, early HCC, advanced HCC, and very advanced HCC. In essence, the analysis identified the transition from the benign state (first meta-state) to the cancerous state (second meta-state). The p-values of these meta-states were $p=0.031$ for MS1 and $p=3.05\times 1{0}^{-5}$ for MS2.

We compared the results of QCD in this case to the results of an existing method developed to detect network biomarkers and the pre-disease state (DNBM) 11 . The DNBM takes as input both the high-throughput data and the large network of protein-protein interactions for the organism under study. The output of DNBM is a pre-disease state in the form of a sample or list of samples from the data. The hypothesis is that a subset of the large network, termed the leading network, is the first to change toward the disease state, which makes its components and structure causally related with the disease. The DMBM models the change in gene expression over time as a Markov process. Then, a state-transition-based local network entropy (SNE) is used as a general, early measure of upcoming transitions by estimating the resilience of the network. The SNE is a Shannon-type entropy 36 , intended to quantify the change in state for the biological network.

Notably, the DNBM identifies one single (pre-disease) state prior to the onset of disease, while the proposed QCD identifies a change interval of transition to disease, which can be much more informative regarding the disease evolution, as well as providing an opportunity for therapeutic intervention. In addition, in the case of the QCD, the impact analysis approach may provide a better evaluation of the system’s impact than the network entropy. At the same time, a reinforcement of the impact by comparing every two time points may provide a better approximation of the change onset. Therefore, evaluating the systemic change between every two time points results in the early-detection property.

For this case study, the DNBM detected the pre-disease state at the fifth stage, very early HCC, which is the first malignant stage. The existent DNBM detected the start of the malignant state while our proposed QCD method detected the transition from benign to malignant.

DNBM was also evaluated on a dataset for mouse exposure to carbonyl chloride (phosgene). Exposure to carbonyl chloride produces irreversible lung injury and potentially life-threatening pulmonary edema that manifest within a day. We also evaluated the QCD on the same dataset (see Supplementary Methods Section 1.2 for details). The results in this case yielded perturbation factors that were hard to separate into large and small perturbations resulting in a poor fit of the mixture of gamma distributions. This is indicated by a larger value of the KLD and smaller value of the KS p-value. For this data set, the KLD yields a value of 2.92 (compared to the other data sets for which KLD values are around 0.1 or less). Also, the same dataset yields a KS p-value of 0.39. This is still far from being significant but also very significantly different from all the others which are above 0.85). Even in this case study, with the worst fit, the QCD method identified one qualitative change which corresponds to the time interval for the initiation of latent effects of the toxic gas exposure. In other words, QCD identified an interval during which damage is treatable 37 , while the DNBM identified a later time point as being the pre-disease state.

These results show the applicability of this method in developing preventive therapies. Identifying the genes that change within the change interval could lead to the identification of very early markers for disease and potential targets for disease prevention. A detailed description of the results of the QCD analysis at each step of the analysis workflow for all eight datasets is included in Section 1 of the Supplementary Methods.

In the case of disease progression, once a change interval is identified one should start the therapeutic intervention as early as possible within the change interval. For example, in the case of the HCV to HCC progression that could be any time up to the high-grade dysplastic stage.

To further evaluate the potential of the proposed method to detect changes as they occur, we ran the method on data from only the first three stages of the disease progression. DQC detected a change interval from the first (control) to the third stage (low-grade dysplastic), showing that a systemic qualitative change is happening and can be detected at a very early stage, as soon as the disease process has started.

Disease prevention and early detection are two major healthcare objectives that contribute to improving quality of life. Currently, early detection of complex diseases is achieved only after the physiological traits of the phenotype are present, when existing treatments may be ineffective. Chronic disease, a particular case of complex disease, is generally detected in the late stage of a relatively slow, progressive process. Representative examples that affect a large number of people are heart disease, cancer, and neurodegenerative disorders. It is a real challenge for people with these diseases to maintain a good quality of life after diagnosis. Understanding when the transition to disease occurs is a good first step towards interrupting the process and maintaining the healthy state.

To maintain the healthy state, one needs to monitor the biological system and measure the gene expression or any parameters the system has in order to assess how much the system is changing. The moment a qualitative change occurs, either cumulative or sudden, a change interval emerges. For instance, in the case of the eight stages of HCC, a qualitative change occurs from control to high-grade dysplasia. A cirrhotic liver is characterized by the presence of scar tissue due to long-term damage. In an attempt to replace the damaged cells in the cirrhotic liver, clusters of newly formed cells can occur in the scar tissue. Dysplastic (abnormally grown) nodules found in the liver are typically identified in cirrhotic livers. Low-grade dysplastic nodules (LGDN) cells are larger than the normal liver cells 38 . High-grade dysplastic nodules (HGDN) cells are smaller than the normal liver cells and have a greater nucleus-to-cytoplasm-size ratio 38 . The difference between HGDNs and very early HCC is the stromal invasion present in the latter 39 . A study on the LGDNs and HGDNs in HCC development concluded that LGDNs together with large regenerative nodules, should be monitored with ultrasound, while HGDNs should be preventively treated due to their high malignant risk 40 . Taken together, these data support the qualitative change identified by QCD from a low malignant risk stage of the liver disease to a high risk stage and close precursor to the malignant stage of very early HCC.

To further investigate the results of our analysis in the case of HCC progression, we identified the differentially expressed (DE) genes (absolute log ${}_{2}$ fold change greater than 1) when comparing the control to high-grade dysplasia and the control to very advanced HCC. The total number of measured genes is 20,156. In the control versus high-grade dysplasia comparison, there are 149 DE genes, while in the control versus very advanced HCC comparison, there are 1,355 DE genes, which is almost an order of magnitude higher. This suggests that using the differentially expressed genes across the change interval, as opposed to the genes that differ between the control and very advanced HCC, offers a more focused analysis. In essence, the comparison across the narrowest change interval targets the genes involved in the initial tumor formation, rather than all genes that change as a consequence of the cancer.

The number of common DE genes among the two comparisons is 80, representing 53% of the initial 149 genes. We downloaded the curated list of cancer genes available in the cancer gene census 41 ( http://cancer.sanger.ac.uk/census ). This list is presented together with the catalogue of somatic mutations in cancer (COSMIC) 42 ( http://cancer.sanger.ac.uk/cosmic ). We used this list of cancer genes to filter the 80 common genes to obtain a cancer gene set. The result consists of two genes: CHEK2 and FAT1 (see Section 2.1 in Supplementary Methods for the expression profile). These genes are highly relevant to the condition under study considering CHEK2 mutations have been linked to various cancers 43 , 44 ; it has also been shown to be a mediator of a tumorigenic mechanism in HCC 45 . Furthermore, FAT1 has been shown to have an oncogenic role in HCC 46 , 47 , and it has been identified as a biomarker in multiple cancers 48 , 49 .

The viral carcinogenesis pathway from KEGG 29 , 30 , 31 was used to identify the change interval for the HCV-induced HCC progression. We also used this pathway to filter the 80 common genes and to obtain a “viral carcinogenesis” gene set, which contains genes from the pathway that change at the onset of the disease. The result consists of two early growth response genes: EGR2 and EGR3 (see Section 2.2 in Supplementary Methods for the expression profile). EGR2 has been shown to be an apoptosis promoter gene 50 , which is downregulated by miRNAs in cancer 51 , 52 . EGR3 has been shown to be involved in a number of cancers and in the regulation of the immune response 53 , 54 , 55 , 56 , and this gene has recently been linked to HCC when it was used to inhibit the growth of tumor cells 57 .

We designed and implemented an analytical method capable of detecting qualitative changes in the state of a biological system by monitoring its gene expression levels. This has been conducted with no training on previous examples, with no expert supervision, and with thresholds set using sound statistical criteria. The only hypothesis used here is that a qualitative change will involve enough pathway components to perturb the pathway in a significant way. The method requires a network of the system, which may limit its applicability. However, most biological systems do have associated networks. For instance, the KEGG pathway database includes about 200 signaling pathways for human, about 190 signaling pathways for mouse and about 190 signaling pathways for rat. Many such pathway databases exist: KEGG 29 , 30 , 31 , Reactome 58 , BioCarta 59 , NCI-PID 60 , WikiPathways 61 , and PANTHER 62 . The proposed method leverages this existing body of knowledge which is expected to grow in the future. In principle, these diagrams can be used to study how the system changes between states. However, most or all existing analysis methods would require an a priori definition of the states to be compared. Once these states are defined, a myriad of methods can be used to identify differentially expressed genes or pathways. One of the major contributions of the proposed method is that it can detect significant system changes without somebody having to define them a priori just by monitoring the system.

To evaluate the proposed method, we used both synthetic and real data. The cases used for validation cover a wide range of biological phenomena and model organisms as presented in the Results section (see Table 1 for a summary). Identifying a change interval implies recognizing the transition the system goes through from a state of relative equilibrium to another. The states of relative equilibrium the system transitions from are denoted here as meta-states and the transition as the change interval. Notably, in each case study, the system transitions between meta-states that are of great importance if we hypothesize that such transitions are infrequent and that a qualitative change is required for a system to undergo such transitions. We also assessed the statistical significance of the potential meta-states for each of the eight case studies. Results show that out of 16 putative meta-states, 13 are significant at a threshold of 5% (see Table 2 ).

It is important to emphasize that the proposed method accomplishes two goals. First, the method identifies qualitative changes. These changes are identified based on the system perturbation factors. Second the methods also identifies meta-states, if they exist. A meta-state is a group of states that are very similar to each other. Sometimes, qualitative changes happen between meta-states and sometime qualitative changes happen without clear meta-states on both sides of the change. The p-values in Table 2 are used to test the hypothesis that a group of states form a meta-state. In each dataset and each meta-state, there is a single test and a single p-value. The fact that some p-values are not significant, simply means that the states on that side of the qualitative change are not very similar to each other and do not form a meta-state. For instance, in the case of the mice exposed to phosgene, before exposure, the individual expression values may involve many physiological differences. However, after exposure, the changes induced by the toxicant are higher than any normal physiological differences since they are associated with severe chemical trauma ultimately leading to death.

The proposed method was applied on a wide range of biological phenomena and was able to detect important transitions between system meta-states with high accuracy in the first six case studies having a known change interval: building a motility motor in E. coli , spore formation in B. subtilis and S. cerevisiae , backwards movement triggered by the nose touch in C. elegans , and both acute ethanol exposure and metamorphosis in D. melanogaster .

We also compared QCD to an existing method developed by Liu et al . 11 for detecting the pre-disease state and network biomarkers on two datasets. These are two case studies where the phenomena are more complex. When analyzing the data for the exposure to the toxic gas phosgene in mice, QCD identified the cellular damage at an earlier time point, when treatment is still effective 37 .

When analyzing data for hepatitis C virus infection progression to hepatocellular carcinoma (HCC) in humans, QCD identified the transition from control to high-grade dysplasia. In this case, the existing method identified as the pre-disease state, i.e., the “very early HCC” stage, which can be interpreted as the start of the malignant state. Importantly, the change interval detected by QCD immediately precedes this pre-disease state detected by the existing method and marks the transition from benign to malignant. Intervention during this interval may prevent this transition and disease progression may be halted.

To summarize, we have evaluated the proposed method QCD on both synthetic (noise free) and real (noisy) data, on a total of eight case studies for six model organisms and one human dataset and the QCD identified the qualitative changes in each case. We have also used both time course data as well as disease stages as system states in our analyses, and QCD performed well for both types of data.

An immediate application for QCD could be to identify when the transition between different disease stages happens for other diseases. However, QCD is a versatile approach that can be applied to systemic states in different contexts (time course, disease progression, drug dose, BMI, age).

The QCD method can also be applied in the study of drug synergies and synthetic lethality where it could identify the time interval when one drug sensitizes the cell and the second drug has maximum efficacy in a time-dependent way. In turn, this could maximize the effect of combination therapies for various diseases. Another important application for the conceptual framework described herein is the prediction of obstetrical disease in early pregnancy, so interventions can mitigate or prevent the “great obstetrical syndromes” that are primarily observed during the third trimester of pregnancy 63 . In future work, we plan to use the QCD method to predict obstetrical disease based on transcriptomics, metabolomics, proteomics, lipidomics, and other data. A system state in the QCD framework can be any of, but not limited to, the following: a developmental stage, the response to a certain therapeutic dose, the stage of a disease, patients who share physiological traits or disease outcome. The analysis of time series expression data using QCD could potentially be used to decide the duration of adjuvant chemotherapy or disease recurrence. However, the most important application of this approach would imply a paradigm shift: one could use a QCD-like approach with the aim of identifying the departure from the healthy state instead of diagnosing the onset of disease.

Qualitative change detection (QCD) method

In this paper, we propose a paradigm shift: instead of detecting the onset of disease, we would like to be able to detect the departure from the healthy state. The qualitative change detection (QCD) analysis presented here is able to detect intervals when a biological system undergoes qualitative changes such as the transition from healthy to disease.

The workflow of the analysis (see Fig. 2 ) consists of the following steps:

Compare the status of the system between each pair of time points using an existing statistical method called pathway impact analysis (IA) 13 – 16 and assess the levels of perturbation;

Separate large and small inter-state perturbations using a gamma mixture model fitted to the system perturbation by an expectation maximization algorithm;

Calculate the change interval(s) as the narrowest disjunct interval(s) of large changes.

In step 1 the perturbation of the system between all pairs of system states is computed utilizing IA. First, sequential states are assigned to the chronologically ordered time points or disease progression stages when the data were sampled. We then compare all pairs of systems states using IA 13 , which was previously developed to evaluate the pathway impact when comparing two phenotypes; herein, we use it to calculate a system/pathway impact factor for each comparison of two system states (time points). The input of impact analysis includes the changes in expression between the two time-points for the measured genes, while the output will be a perturbation factor for the pathway. The result of this first step will be a list of time intervals (comparisons) with their computed pathway perturbation factor.

The pathway impact analysis takes as input signaling networks (pathways) and a list of genes with their respective changes between two states of a system (e.g. condition vs. control). In a typical signaling pathway, nodes represent genes or gene products and edges represent signals, such as activation or repression, directed from one node to another. The goal of IA is to identify the pathways significantly impacted in a given phenotype by analyzing all measured expression changes for all genes, as well as all of their interactions, as described by each pathway. This type of analysis incorporates two types of evidence, which taken together estimate the disruption on a pathway when comparing two phenotypes. The first type is evidence given by the perturbation analysis. The magnitude of expression change (log fold-change) and the pathway structure are used to compute a perturbation factor for each gene (Eq. ( 1 )). For each of the pathways edges such as activation, activation through phosphorylation and inhibition/repression are used in the analysis with the respective values (1, 1, $-1$ ). All other edges have a value of 0. This is part of the implementation of the impact analysis 13 , 14 , 15 , 16 . The gene perturbation factors are summed up to the pathway level to account for the observed pathway perturbation.

$PF(g)$ - perturbation factor for gene g

$US(g)$ - set of genes directly upstream of g

${\beta }_{ug}$ - strength of interaction between u and g

DS(g) - set of genes directly downstream of g

$\Delta E(g)$ - log fold change in expression for g

# - cardinality

For the perturbation analysis, we sum the absolute value of the gene perturbation factors (Eq. ( 2 )) so that the up-regulation and down-regulation do not cancel each other.

$PF(P)$ - perturbation factor for pathway P

$| \cdot | $ - absolute value operator

We use the all-gene approach, without gene weights; therefore, since we do not select differentially expressed genes, the enrichment part cannot be computed. The pathway perturbation factors are positive values with 0 marking no perturbation — the higher the value, the larger the pathway perturbation. We work under the assumption that the pathway perturbation factors follow a gamma distribution with mode = 0 when the pathway is not perturbed.

In step 2, the distribution of the pathway perturbation factors is modeled using a gamma mixture model (see Fig. 11 ). The hypothesis states that if there is a change interval the system state comparisons will yield a mix of large and small system perturbations. Small system perturbations are expected when comparing system states before and after the change interval. Large system perturbations are expected when comparing system states before the change interval to states after the change interval. Therefore, a mixture of two gamma distributions is used: one for the comparisons in which the system is unperturbed (i.e., the null hypothesis) and another for comparisons in which the system is perturbed.

The fit of a mixture of two gamma distributions (blue and red lines) to the observed perturbation values of the system as computed for all pair-wise comparisons (thick black line). The fitted mixture distribution is marked by the thinner black line. The difference between the fitted and observed data is shaded in light gray. A goodness of fit measure is the overlap calculated as the ratio between the intersection and union of the areas under the observed data (thick black line) and fitted model (thinner black line). A perfect fit would yield an overlap of 100%. The null hypothesis is that there are no change intervals and therefore there are only small system perturbations (blue distribution). If a second distribution is found to be present (red), the threshold used to distinguish between small and large system perturbations will be the yellow vertical line. Under these circumstances, the blue area under the blue line is the Type 1 error and the red area under the red line is the Type 2 error.

The EM algorithm has a number of parameters that can potentially influence the results. Such parameters include the initial shape and scale for the fitted gamma distributions, the convergence criteria (epsilon), the maximum number of iterations and the maximum number of restarts. The two gamma distributions parameters are initialized so that their modes corresponds to the minimum and maximum values of the perturbation factors, which puts the model in the correct range from the beginning. Other parameters such as the maximum number of iterations or epsilon are not influencing the results, as long as the values are reasonable. For instance, we use 100 as the maximum number of iterations but in all cases, the algorithm converged in fewer than 100 iterations. Therefore, even though in principle, the results can be influenced by the values of these various parameters, in practice the results were stable in all experiments we performed. In the proposed form, the user does not need to choose any parameters. As a potential improvement on the proposed technique, one could use a stochastic version of the EM algorithm.

The mixture model fitting will provide two distributions that best fit the data together with a percentage that estimates how much of the observed data comes from each of these two distributions. If any of the distributions has a percentage of less than 10%, the QCD analysis considers that there is only one distribution and, therefore, there is no significant change, and no change interval. The algorithm for this step is available in Supplementary Materials Section 1.1 .

If both distributions fitted contribute more than 10%, the goodness of fit is then evaluated by computing the percentage of overlap between the observed and fitted distributions of system perturbations (see Fig. 11 , overlap). Other statistical approaches (the Kolmogorov-Smirnov test and the Kullback-Leibler divergence) are also used to evaluate the goodness of fit and results are presented in the Supplementary Methods Section 1.3 . If the mixture contains more than 10% of either of the distributions, the intersection of the two distributions is used as the threshold to select comparisons with large system perturbations. Comparisons that yield a pathway perturbation factor higher than this threshold will be marked as having a large system perturbation.

We also explored the alternative of using the fit of a single gamma distribution to the perturbation factors in order to decide if there is a systemic change captured by the data. In the case of fitting only one distribution for that purpose the results are slightly worse, or just as good in most cases (see Supplementary Methods Section 4 ).

An important requirement is to demonstrate that the approach does not report false positive changes in random data or in cases in which there are no changes in the organism. Section 3 in Supplementary Materials includes the results obtained with controls only, as well as results obtained with random data. These results show that the proposed approach does not report falsely significant changes.

In step 3, change intervals are computed as the overlap of comparisons with a large system perturbation using an algorithm based on the definition in the subsection titled “Change interval formal definition”. The algorithm takes as input a list of comparisons with an assigned system perturbation value and a predefined system perturbation threshold computed in step 2 described above. The algorithm iterates over the list of comparisons and identifies the start and end points of change intervals as points that have at least one comparison that shows a large perturbation (higher than the threshold) starting or ending in the respective points, and no large perturbation comparisons start or end in between those points. The output is a list of change intervals described by their start and end points. Note that the change interval does not have to be a comparison that shows a large system perturbation by itself.

Change interval formal definition

$N$ = the number of time points

$S$ = the set of states

$p$ = the set of perturbation values

$CI$ = the set of change intervals

$pcut$ = the perturbation threshold

Definitions:

$S$ = $\{{S}_{i}| i\in \{0,\ldots ,N-1\}\}$

$p$ = $\{{p}_{ij}| i,j\in \{0,\ldots ,N-1\},i < j\}$ , where ${p}_{ij}$ is the system perturbation value when comparing ${S}_{i}$ and ${S}_{j}$

$CI$ = $\{(x,y),x,y\in \{0,\ldots ,N-1\},x < y\}$ , that satisfy the following conditions

$\forall i,j\in \{x,\ldots ,y\},(i,j)\ \ne \ (x,y)$ and ${p}_{ij}\le pcut$

(i) $\exists i\in \{0,\ldots ,y-1\}$ such that ${p}_{iy} > pcut$

(ii) $\exists j\in \{x+1,\ldots ,N-1\}$ such that ${p}_{xj} > pcut$

(iii) $x$ is the max value to satisfy the above conditions for a given $y$

(iv) $y$ is the min value to satisfy the above conditions for a given $x$

Meta-states statistical validation

To better understand the phenomenon under study, after the detection of a change interval, the states of the system before and after a change interval should be analyzed to gain insight regarding the state of the system before and after a qualitative change. To describe this analysis, the situation in which there is a single change interval will be considered, as in the E. coli flagellum building dataset. In this case, the system is considered to be stable before and after the change interval. In this context, we group the states in which the system is stable into meta-states. We define a meta-state as a group of consecutive states that satisfy the following two conditions:

All comparisons between states within a meta-state have a small system perturbation;

All comparisons between states from a meta-state to states outside the meta-state (excluding the states in the change interval) have a large system perturbation.

In the above, definition, the “small” and “large” perturbations, are defined based on the threshold between the two gamma distributions computed in the previous step and shown as the yellow line in Fig. 11 .

Note that all comparisons between the states within a change interval and the meta-states immediately before and immediately after it may have a small system perturbation. This is because, during the change interval, the system is in transition between the two meta-states; therefore, its state during the transition is a mix of the two meta-states that may not be qualitatively different from either of them.

Based on the detected change interval, groups of sequential system states can form potential meta-states (see panel B in Fig. 12 ). Panel A in Fig. 12 shows the ideal results of all comparisons between all states involved in these meta-states. In essence, all comparisons within each potential meta-state should show a small system perturbation while all comparisons between a meta-state time point and a time point outside the meta-state (excluding the change interval) should show a large system perturbation.

Meta-states in the E. coli flagellum building case study. Arc plots show possible comparison between time-points (states): comparisons with large system perturbation are red, and comparisons with small system perturbation are gray or black. Panel (A) the expected arc plots of two theoretical meta-states (groups of states in the black ellipses) relative to the detected change interval (S6–S10): all comparisons within each potential meta-state should show a small system perturbation while all comparisons between a meta-state time point and a time point outside the meta-state (excluding the change interval) should show a large system perturbation. Panel (B) the actual arc plot showing the observed large perturbation (red) vs small perturbation comparisons (gray and black) for all possible state comparisons. Black arcs show comparisons between states of potential meta-states (groups of states in the black ellipses) to states outside the potential meta-state (excluding the change interval) that show a small perturbation. Panel (C) the arc plot shows the observed comparisons for potential meta-state I (S0–S6, states in the black ellipse). Black arcs show comparisons between states of potential meta-state I to states outside it (excluding the change interval) that show a small perturbation. Panel (D) the arc plot shows the observed comparisons for potential meta-state II (S10–S20, states in the black ellipse). Black arcs show comparisons between states of potential meta-state II to states outside it (excluding the change interval) that show a small perturbation.

To validate each observed potential meta-state, a statistical approach is applied to evaluate how closely it meets the conditions of a theoretical meta-state. The validation of the potential meta-state is described for the E. coli flagellum building dataset. The data was sampled at 21 time points (system states S0–S20) and the change interval was detected as (S6–S10). In this case, there are two potential meta-states: MS1, which contains the states before the change interval (states from S0 to S6), and MS2, which contains the states after the change interval (states from S10 to S20). To investigate the potential meta-states, all comparisons (arcs) (see Fig. 12B ) are considered from the perspective of the meta-state definition above. For MS1, all comparisons between the states S0 to S6 should yield only small system perturbations. In addition, all comparisons between any states in MS1 and any states outside MS1 (not including the change interval) should involve large perturbations. With these considerations, all comparisons involving MS1 states can be assigned a binary value: either consistent or inconsistent with the expectations above. For each potential meta-state, a statistic is computed as the number of time intervals with status consistent with the status (large/small) assigned in the corresponding theoretical meta-state (meta-state definition). Under the null hypothesis, in which there are no groups of system states that form meta-states, the probability that a comparison is consistent or not should be 0.5. Based on this framework, a binomial model is used to calculate a p-value for the statistic computed for each of the groups of states that are potential meta-states (see Fig. 12C,D ):

n - the number of trials;

0.5 - probability that the status of a comparison

is consistent with the meta-state definition.

The p-value computed for the potential meta-state characterizes the amount of evidence indicating the existence of a true meta-state (comparisons consistent with the definition, vs. inconsistent comparisons). A significant p-value lower than a predefined threshold would confirm the identification of a true meta-state. In our case studies, most p-values were significant at a 1% threshold (see details in Section 1.4 of the Supplementary Methods).

Synthetic data parameters

For the E. coli flagellum building and B. subtilis sporulation, the gene expression synthetic data were generated using the interactions described by the biological network and Hill functions for protein accumulation (Eq. ( 4 )) and decay (Eq. ( 5 )) with a rate of $\alpha =0.005$ .

Given that $X\to Y$ denotes that transcription factor $X$ regulates gene $Y$

${Y}_{st}$ – steady state expression level for gene $Y$

$\alpha $ – decay rate for protein $Y$

$t$ – time

$Y(t)$ – expression level for gene $Y$ at time $t$

For the third case study, C. elegans , data were generated using a step function for the ${X}_{1}=FLP$ neuron and a constant function (0) for the ${X}_{2}=ASH$ neuron. The following formula describes the change in voltage over time for the $Y=AVD$ neuron:

The following formula describes the change in voltage over time for the $Z=AVA$ neuron:

Constants 0.5 and 0.4 are the strengths of the synaptic connections, and ${K}_{Y}$ and ${K}_{Z}$ are the activation thresholds.

For gene expression from biological experiments, microarray data were downloaded from the GEO database. The CEL files downloaded from GEO were processed using custom R scripts (R version 3.1.2). Data pre-processing (background correction and normalization) was performed using the threestep function from the affyPLM (version 1.42.0) R package. Gene IDs were mapped to gene symbols using the respective annotation packages from R: org.Sc.sgd.db (yeast), org.Dm.eg.db (fruit fly), org.Mm.eg.db and moe430a.db (mouse), org.Hs.eg.db and hgu133plus2.db (human). Gene expression at a specific time-point was computed as the average of the replicates for the specific time point when replicates were available. The ROntoTools 1.6.1 R package was used for impact analysis. The mixtools 1.0.3 R package was used for the mixture model analysis.

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Acknowledgements

This work has been partially supported by the following grants: NIH RO1 DK089167, NIH STTR R42GM087013, NSF DBI-0965741 (to SD), by the Robert J. Sokol M.D. Endowment in Systems Biology (to SD), and by the Thomas Rumble Fellowship (to CM). This research was also supported, in part, by the Perinatology Research Branch, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS), and, in part, with federal funds from the NICHD/NIH/DHHS under Contract No. HHSN275201300006C. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any of the funding agencies. Thanks to all the colleagues from the Intelligent Systems and Bioinformatics Laboratory at Wayne Sate University for useful feedback during the design and development of this study. Specifically, we thank Dr. Tin Nguyen for useful input regarding the random data generation.

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Mitrea, C., Bollig-Fischer, A., Voichiţa, C. et al. Detecting qualitative changes in biological systems. Sci Rep 10 , 8146 (2020). https://doi.org/10.1038/s41598-020-62578-8

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In the world of research, there are two general approaches to gathering and reporting information: qualitative and quantitative approaches. Qualitative research generates non-numerical data while quantitative research generates numerical data or information that can be converted into numbers.

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1. Does it have Method and Results sections?

EXAMPLE ABSTRACT

a) If not, the article is secondary research (typically a review) .

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STEP 3: Now, let's determine what type of original research this is.

3a. In the Method section, is the study described as an interview, observation, or questionnaire? Does the study involve looking into self-reported beliefs, thoughts, etc. ?

a ) If you answered yes, it’s a qualitative study .

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a ) If you answered yes, it’s a retrospective study .

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200+ Unique And Interesting Biology Research Topics For Students In 2023

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Biology is the study of living things, like animals, plants, and even tiny organisms too small to see. It helps us understand how these living things work and how they interact with each other and their environment. Biologists, or scientists who study biology, explore topics like how animals breathe, how plants grow, and how our bodies function. By learning about biology, we can better care for the Earth and all its living creatures.

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31. Studying the structure and function of DNA and RNA molecules.

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52. Analyzing the impact of climate change on a local ecosystem.

53.Studying the biodiversity of a particular rainforest region.

54. Investigating the physiological adaptations of animals to extreme temperatures.

55. Analyzing the effects of pollution on aquatic ecosystems.

56. Studying the life history and conservation status of an endangered species.

57. Investigating the molecular mechanisms of a specific disease.

58. Studying the ecological interactions within a coral reef ecosystem.

59. Analyzing the genetics of plant hybridization and speciation.

60. Investigating the behavior and communication of a particular bird species.

Marine Biology Research Topics

61. Studying the impact of ocean acidification on coral reefs.

62. Analyzing the migration patterns of marine mammals.

63. Investigating the physiology of deep-sea creatures under high pressure.

64. Studying the ecology of phytoplankton and their role in the marine food web.

65. Analyzing the behavior of different species of sharks.

66. Investigating the conservation of sea turtle populations.

67. Studying the biodiversity of deep-sea hydrothermal vent communities.

68. Analyzing the effects of overfishing on marine ecosystems.

69. Investigating the adaptation of marine organisms to extreme cold in polar regions.

70. Studying the bioluminescence and communication in marine organisms.

AP Biology Research Topics

71. Investigating the role of specific enzymes in cellular metabolism.

72. Analyzing the genetic variation within a population.

73. Studying the mechanisms of hormonal regulation in animals.

74. Investigating the principles of Mendelian genetics through trait analysis.

75. Analyzing the ecological succession in a local ecosystem.

76. Studying the physiology of the human circulatory system.

77. Investigating the molecular biology of a specific virus.

78. Studying the principles of natural selection through evolutionary simulations.

79. Analyzing the genetic diversity of a plant species in different habitats.

80. Investigating the effects of different environmental factors on plant growth.

Cell Biology Research Topics

81. Investigating the role of mitochondria in cellular energy production.

82. Analyzing the mechanisms of cell division and mitosis.

83. Studying the function of cell membrane proteins in signal transduction.

84. Investigating the cellular processes involved in apoptosis (cell death).

85. Analyzing the role of endoplasmic reticulum in protein synthesis and folding.

86. Studying the dynamics of the cytoskeleton and cell motility.

87. Investigating the regulation of cell cycle checkpoints.

88. Analyzing the structure and function of cellular organelles.

89. Studying the molecular mechanisms of DNA replication and repair.

90. Investigating the impact of cellular stress on cell health and function.

Human Biology Research Topics

91. Analyzing the genetic basis of inherited diseases in humans.

92. Investigating the physiological responses to exercise and physical activity.

93. Studying the hormonal regulation of the human reproductive system.

94. Analyzing the impact of nutrition on human health and metabolism.

95. Investigating the role of the immune system in disease prevention.

96. Studying the genetics of human evolution and migration.

97. Analyzing the neural mechanisms underlying human cognition and behavior.

98. Investigating the molecular basis of aging and age-related diseases.

99. Studying the impact of environmental toxins on human health.

100. Analyzing the genetics of organ transplantation and tissue compatibility.

Molecular Biology Research Topics

101. Investigating the role of microRNAs in gene regulation.

102. Analyzing the molecular basis of genetic disorders like cystic fibrosis.

103. Studying the epigenetic modifications that control gene expression.

104. Investigating the molecular mechanisms of RNA splicing.

105. Analyzing the role of telomeres in cellular aging.

106. Studying the molecular pathways involved in cancer metastasis.

107. Investigating the molecular basis of neurodegenerative diseases.

108. Studying the molecular interactions in protein-protein networks.

109. Analyzing the molecular mechanisms of DNA damage and repair.

110. Investigating the use of CRISPR-Cas9 for genome editing.

Animal Biology Research Topics

111. Studying the behavior and communication of social insects like ants.

112. Analyzing the physiology of hibernation in mammals.

113. Investigating the ecological interactions in a predator-prey relationship.

114. Studying the adaptations of animals to extreme environments.

115. Analyzing the genetics of inherited traits in animal populations.

116. Investigating the impact of climate change on animal migration patterns.

117. Studying the diversity of marine life in coral reef ecosystems.

118. Analyzing the physiology of flight in birds and bats.

119. Investigating the molecular basis of animal coloration and camouflage.

120. Studying the behavior and conservation of endangered species.

Neuroscience Research Topics
Mental Health Research Topics

Plant Biology Research Topics

121. Investigating the role of plant hormones in growth and development.

122. Analyzing the genetics of plant resistance to pests and diseases.

123. Climate change and plant phenology are being examined.

124. Investigating the ecology of mycorrhizal fungi and their symbiosis with plants.

125. Investigating plant photosynthesis and carbon fixing.

126. Molecular analysis of plant stress responses.

127. Investigating the adaptation of plants to drought conditions.

128. Studying the role of plants in phytoremediation of polluted environments.

129. Analyzing the genetics of plant hybridization and speciation.

130. Investigating the molecular basis of plant-microbe interactions.

Environmental Biology Research Topics

131. Analyzing the effects of pollution on aquatic ecosystems.

132. Investigating the biodiversity of a particular ecosystem.

133. Studying the ecological consequences of deforestation.

134. Analyzing the impact of climate change on wildlife populations.

135. Investigating the use of bioremediation to clean up polluted sites.

136. Studying the environmental factors influencing species distribution.

137. Analyzing the effects of habitat fragmentation on wildlife.

138. Investigating the ecology of invasive species in new environments.

139. Studying the conservation of endangered species and habitats.

140. Analyzing the interactions between humans and urban ecosystems.

Chemical Biology Research Topics

141. Investigating the design and synthesis of new drug compounds.

142. Analyzing the molecular mechanisms of enzyme catalysis.

143.Studying the role of small molecules in cellular signaling pathways.

144. Investigating the development of chemical probes for biological research.

145. Studying the chemistry of protein-ligand interactions.

146. Analyzing the use of chemical biology in cancer therapy.

147. Investigating the synthesis of bioactive natural products.

148. Studying the role of chemical compounds in microbial interactions.

149. Analyzing the chemistry of DNA-protein interactions.

150. Investigating the chemical basis of drug resistance in pathogens.

Medical Biology Research Topics

151. Investigating the genetic basis of specific diseases like diabetes.

152. Analyzing the mechanisms of drug resistance in bacteria.

153. Studying the molecular mechanisms of autoimmune diseases.

154. Investigating the development of personalized medicine approaches.

155. Studying the role of inflammation in chronic diseases.

156. Analyzing the genetics of rare diseases and genetic syndromes.

157. Investigating the molecular basis of viral infections and vaccines.

158. Studying the mechanisms of organ transplantation and rejection.

159. Analyzing the molecular diagnostics of cancer.

160. Investigating the biology of stem cells and regenerative medicine.

Evolutionary Biology Research Topics

161. Studying the evolution of human ancestors and early hominids.

162. The genetic variety of species and between species is being looked at.

163. Investigating the role of sexual selection in animal evolution.

164. Studying the co-evolutionary relationships between parasites and hosts.

165. Analyzing the evolutionary adaptations of extremophiles.

166. Investigating the evolution of developmental processes (evo-devo).

167. Studying the biogeography and distribution of species.

168. Analyzing the evolution of mimicry in animals and plants.

169. Investigating the genetics of speciation and hybridization.

170. Studying the evolutionary history of domesticated plants and animals.

Cellular Biology Research Topics

171. Investigating the role of autophagy in cellular homeostasis.

172. Analyzing the mechanisms of cellular transport and trafficking.

173. Studying the regulation of cell adhesion & migration.

174. Investigating the cellular responses to DNA damage.

175. Analyzing the dynamics of cellular membrane structures.

176. Studying the role of cellular organelles in lipid metabolism.

177. Investigating the molecular mechanisms of cell-cell communication.

178. Studying the physiology of cellular respiration and energy production.

179. Analyzing the cellular mechanisms of viral entry and replication.

180. Investigating the role of cellular senescence in aging and disease.

Good Biology Research Topics Related To Brain Injuries

181. Analyzing the molecular mechanisms of traumatic brain injury.

182. Investigating the role of neuroinflammation in brain injury recovery.

183. Studying the impact of concussions on long-term brain health.

184. Analyzing the use of neuroimaging in diagnosing brain injuries.

185. Investigating the development of neuroprotective therapies.

186. Studying the genetics of susceptibility to brain injuries.

187. Analyzing the cognitive and behavioral effects of brain trauma.

188. Investigating the role of rehabilitation in brain injury recovery.

189. Studying the cellular and molecular changes in axonal injury.

190. Looking into how stem cell therapy might be used to help brain injuries.

Biology Quantitative Research Topics

191. Investigating the mathematical modeling of population dynamics.

192. Analyzing the statistical methods for biodiversity assessment.

193. Studying the use of bioinformatics in genomics research.

194. Investigating the quantitative analysis of gene expression data.

195. Studying the mathematical modeling of enzyme kinetics.

196. Analyzing the statistical approaches for epidemiological studies.

197. Investigating the use of computational tools in phylogenetics.

198. Studying the mathematical modeling of ecological systems.

199. Analyzing the quantitative analysis of protein-protein interactions.

200. Investigating the statistical methods for analyzing genetic variation.

Importance Of Choosing The Right Biology Research Topics

Here are some importance of choosing the right biology research topics:

1. Relevance to Your Interests and Goals

Choosing the right biology research topic is important because it should align with your interests and goals. Studying something you’re passionate about keeps you motivated and dedicated to your research.

2. Contribution to Scientific Knowledge

Your research should contribute something valuable to the world of science. Picking the right topic means you have the chance to discover something new or solve a problem, advancing our understanding of the natural world.

3. Availability of Resources

Consider the resources you have or can access. If you pick a topic that demands resources you don’t have, your research may hit a dead end. Choosing wisely means you can work efficiently.

4. Feasibility and Manageability

A good research topic should be manageable within your time frame and capabilities. If it’s too broad or complex, you might get overwhelmed. Picking the right topic ensures your research is doable.

5. Real-World Impact

Think about how your research might benefit the real world. Biology often has implications for health, the environment, or society. Choosing a topic with practical applications can make your work meaningful and potentially change lives.

Resources For Finding Biology Research Topics

There are numerous resources for finding biology research topics:

1. Online Databases

Look on websites like PubMed and Google Scholar. They have lots of biology articles. Type words about what you like to find topics.

2. Academic Journals

Check biology magazines. They talk about new research. You can find ideas and see what’s important.

3. University Websites

Colleges show what their teachers study. Find teachers who like what you like. Ask them about ideas for your own study.

4. Science News and Magazines

Read science news. They tell you about new things in biology. It helps you think of research ideas.

5. Join Biology Forums and Communities

Talk to other people who like biology online. You can ask for ideas and find friends to help you. Use websites like ResearchGate and Reddit for this.

Conclusion

Biology Research Topics offer exciting opportunities for exploration and learning. We’ve explained what biology is and stressed the importance of picking a good research topic. Our tips and extensive list of over 200 biology research topics provide valuable guidance for students.

Selecting the right topic is more than just getting good grades; it’s about making meaningful contributions to our understanding of life. We’ve also shared resources to help you discover even more topics. So, embrace the world of biology research, embark on a journey of discovery, and be part of the ongoing effort to unravel the mysteries of the natural world.

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Biology Research Topics

Are you in need of captivating and achievable research topics within the field of biology? Your quest for the best biology topics ends right here as this article furnishes you with 100 distinctive and original concepts for biology research, laying the groundwork for your research endeavor.

Table of Contents

Our proficient researchers have thoughtfully curated these biology research themes, considering the substantial body of literature accessible and the prevailing gaps in research.

Should none of these topics elicit enthusiasm, our specialists are equally capable of proposing tailor-made research ideas in biology, finely tuned to cater to your requirements.

Thus, without further delay, we present our compilation of biology research topics crafted to accommodate students and researchers.

Research Topics in Marine Biology

Impact of climate change on coral reef ecosystems.
Biodiversity and adaptation of deep-sea organisms.
Effects of pollution on marine life and ecosystems.
Role of marine protected areas in conserving biodiversity.
Microplastics in marine environments: sources, impacts, and mitigation.

Biological Anthropology Research Topics

Evolutionary implications of early human migration patterns.
Genetic and environmental factors influencing human height variation.
Cultural evolution and its impact on human societies.
Paleoanthropological insights into human dietary adaptations.
Genetic diversity and population history of indigenous communities.

Biological Psychology Research Topics

Neurobiological basis of addiction and its treatment.
Impact of stress on brain structure and function.
Genetic and environmental influences on mental health disorders.
Neural mechanisms underlying emotions and emotional regulation.
Role of the gut-brain axis in psychological well-being.

Cancer Biology Research Topics

Targeted therapies in precision cancer medicine.
Tumor microenvironment and its influence on cancer progression.
Epigenetic modifications in cancer development and therapy.
Immune checkpoint inhibitors and their role in cancer immunotherapy.
Early detection and diagnosis strategies for various types of cancer.

Cell Biology Research Topics

Mechanisms of autophagy and its implications in health and disease.
Intracellular transport and organelle dynamics in cell function.
Role of cell signaling pathways in cellular response to external stimuli.
Cell cycle regulation and its relevance to cancer development.
Cellular mechanisms of apoptosis and programmed cell death.

Developmental Biology Research Topics

Genetic and molecular basis of limb development in vertebrates.
Evolution of embryonic development and its impact on morphological diversity.
Stem cell therapy and regenerative medicine approaches.
Mechanisms of organogenesis and tissue regeneration in animals.
Role of non-coding RNAs in developmental processes.

Human Biology Research Topics

Genetic factors influencing susceptibility to infectious diseases.
Human microbiome and its impact on health and disease.
Genetic basis of rare and common human diseases.
Genetic and environmental factors contributing to aging.
Impact of lifestyle and diet on human health and longevity.

Molecular Biology Research Topics

CRISPR-Cas gene editing technology and its applications.
Non-coding RNAs as regulators of gene expression.
Role of epigenetics in gene regulation and disease.
Mechanisms of DNA repair and genome stability.
Molecular basis of cellular metabolism and energy production.

Research Topics in Biology for Undergraduates

41. Investigating the effects of pollutants on local plant species.
Microbial diversity and ecosystem functioning in a specific habitat.
Understanding the genetics of antibiotic resistance in bacteria.
Impact of urbanization on bird populations and biodiversity.
Investigating the role of pheromones in insect communication.

Synthetic Biology Research Topics

Design and construction of synthetic biological circuits.
Synthetic biology applications in biofuel production.
Ethical considerations in synthetic biology research and applications.
Synthetic biology approaches to engineering novel enzymes.
Creating synthetic organisms with modified functions and capabilities.

Animal Biology Research Topics

Evolution of mating behaviors in animal species.
Genetic basis of color variation in butterfly wings.
Impact of habitat fragmentation on amphibian populations.
Behavior and communication in social insect colonies.
Adaptations of marine mammals to aquatic environments.

Best Biology Research Topics

Unraveling the mysteries of circadian rhythms in organisms.
Investigating the ecological significance of cryptic coloration.
Evolution of venomous animals and their prey.
The role of endosymbiosis in the evolution of eukaryotic cells.
Exploring the potential of extremophiles in biotechnology.

Biological Psychology Research Paper Topics

Neurobiological mechanisms underlying memory formation.
Impact of sleep disorders on cognitive function and mental health.
Biological basis of personality traits and behavior.
Neural correlates of emotions and emotional disorders.
Role of neuroplasticity in brain recovery after injury.

Biological Science Research Topics:

Role of gut microbiota in immune system development.
Molecular mechanisms of gene regulation during development.
Impact of climate change on insect population dynamics.
Genetic basis of neurodegenerative diseases like Alzheimer’s.
Evolutionary relationships among vertebrate species based on DNA analysis.

Biology Education Research Topics

Effectiveness of inquiry-based learning in biology classrooms.
Assessing the impact of virtual labs on student understanding of biology concepts.
Gender disparities in science education and strategies for closing the gap.
Role of outdoor education in enhancing students’ ecological awareness.
Integrating technology in biology education: challenges and opportunities.

Biology-Related Research Topics

The intersection of ecology and economics in conservation planning.
Molecular basis of antibiotic resistance in pathogenic bacteria.
Implications of genetic modification of crops for food security.
Evolutionary perspectives on cooperation and altruism in animal behavior.
Environmental impacts of genetically modified organisms (GMOs).

Biology Research Proposal Topics

Investigating the role of microRNAs in cancer progression.
Exploring the effects of pollution on aquatic biodiversity.
Developing a gene therapy approach for a genetic disorder.
Assessing the potential of natural compounds as anti-inflammatory agents.
Studying the molecular basis of cellular senescence and aging.

Biology Research Topic Ideas

Role of pheromones in insect mate selection and behavior.
Investigating the molecular basis of neurodevelopmental disorders.
Impact of climate change on plant-pollinator interactions.
Genetic diversity and conservation of endangered species.
Evolutionary patterns in mimicry and camouflage in organisms.

Biology Research Topics for Undergraduates

Effects of different fertilizers on plant growth and soil health.
Investigating the biodiversity of a local freshwater ecosystem.
Evolutionary origins of a specific animal adaptation.
Genetic diversity and disease susceptibility in human populations.
Role of specific genes in regulating the immune response.

Cell and Molecular Biology Research Topics

Molecular mechanisms of DNA replication and repair.
Role of microRNAs in post-transcriptional gene regulation.
Investigating the cell cycle and its control mechanisms.
Molecular basis of mitochondrial diseases and therapies.
Cellular responses to oxidative stress and their implications in ageing.

These topics cover a broad range of subjects within biology, offering plenty of options for research projects. Remember that you can further refine these topics based on your specific interests and research goals.

Frequently Asked Questions

What are some good research topics in biology?

A good research topic in biology will address a specific problem in any of the several areas of biology, such as marine biology, molecular biology, cellular biology, animal biology, or cancer biology.

A topic that enables you to investigate a problem in any area of biology will help you make a meaningful contribution.

How to choose a research topic in biology?

Choosing a research topic in biology is simple.

Follow the steps:

Generate potential topics.
Consider your areas of knowledge and personal passions.
Conduct a thorough review of existing literature.
Evaluate the practicality and viability.
Narrow down and refine your research query.
Remain receptive to new ideas and suggestions.

Who Are We?

For several years, Research Prospect has been offering students around the globe complimentary research topic suggestions. We aim to assist students in choosing a research topic that is both suitable and feasible for their project, leading to the attainment of their desired grades. Explore how our services, including research proposal writing , dissertation outline creation, and comprehensive thesis writing , can contribute to your college’s success.

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The 10 Most Interesting Biology Research Topics

If you ask any biology expert whether their research subject is interesting, of course they’ll say yes. Even moss can be fascinating when you look at it under a magnifying glass. Some biology topics are easier for non-biologists to get more excited about than others, though.

Whether you’re looking for a research topic for a college paper or an area to specialize in if you’re majoring in biology , here are some of the most interesting things going on in the biology world right now.

1. CRISPR and Genetic Engineering

Normally, we think of our DNA as being set in stone. But what if it isn’t? What if you could literally change your DNA?

Scientists have only recently figured out how to use CRISPR to edit DNA sequences.

That’s what CRISPR promises. Short for Clustered Regularly Interspaced Short Palindromic Repeat, CRISPR is essentially a search and cut/paste function all rolled into one, but for DNA. This organizational pattern appears naturally in bacteria, and scientists have only recently figured out how to use it to edit DNA sequences in other organisms — including humans.

CRISPR is taking off in a big way due to its staggering potential. Imagine if you could cure genetic diseases, change your eye color, or make people permanently resistant to tricky viruses like HIV. It’s easy to see the potential for life-changing benefits and great harm at the same time, and that’s why there’s currently so much excitement about CRISPR.

2. Epidemiology and Coronavirus

Epidemiology is the study of how diseases spread in populations. Now that we’re in one of the largest pandemics in recent memory , it’s easy to see why this is such an interesting topic. Finding a vaccine and ways to prevent the spread of COVID-19 has become the single greatest all-hands-on-deck effort of our time.

Even before the coronavirus outbreak, epidemiology intrigued scientists. Epidemiologists are often thought of as a modern-day Indiana Jones because they work in remote jungles and chase dangerous and terrifying diseases like Ebola all around the world.

If you haven’t heard of prions, make sure you’re sitting down. These infectious-disease-causing agents are responsible for things like mad cow disease, chronic wasting disease, and (possibly) Alzheimer’s disease .

They work by essentially turning brains into Swiss cheese. Prions aren’t fully living organisms like bacteria or parasites; rather, they’re bits of misshapen proteins that cause other proteins to become deformed in a chain reaction until the brain is literally full of holes.

Because prions aren’t truly alive, there’s no real way to “kill” them. As a result, they can persist in the environment and stay contagious for years, even surviving normal sterilization techniques at hospitals and labs. Scientists are trying to understand how prions work on a basic level, and how to prevent them from accumulating in the environment and causing disease.

4. Climate Change

Despite what some governments might think, climate change has emerged as one of the most pressing issues of our time. Scientists have measured the chemical signal from greenhouse gases and determined that it’s a direct result of human industrial activity .

Climate change is one of the most pressing issues of our time.

The battle against climate change seems daunting. First, scientists must convince the general public — especially legislators — that the phenomenon exists. Then, there’s the huge task of identifying possible solutions.

With global consequences, climate change scientist is a job that’s not likely to end within our lifetimes, meaning there’s plenty of room for new environmental specialists.

5. Cancer Biology

About 2 out of every 5 people in the U.S. will develop cancer in their lifetimes, according to the National Cancer Institute .

What scientists are learning now is that “cancer” is more of an umbrella term for many different diseases that all have the same outcome: uncontrolled cell growth and, eventually, death. It’s not as simple as finding the solution to one disease because cancer is actually many diseases, each with its own cause, progression, outcome, and treatment.

Now that scientists know more about cancer, the path forward is clear: More research on each type of cancer is needed before we can understand and ultimately eradicate it.

6. Behavioral Economics

You know you should pay off your debt and save more money , but most of us don’t do that. Why not? And what are scientifically backed ways that we can learn how to do these things?

Behavioral economics is about how your biology affects both your finances and happiness.

This is what the captivating new field of behavioral economics aims to answer. The multidisciplinary approach combines two vastly different areas — economics and behavior — in an effort to understand how humans can live happier lives.

One major paper on the topic linked peak happiness levels to an income of around $75,000. Yet in 2018, the median household income fell short of that mark by almost $15,000 . In other words, behavioral economics is the study of how your biology affects your finances and happiness.

7. Endangered Species Recovery

It’s estimated that up to 8,700 species go extinct every year. At this rate, we’re due for the biggest mass extinction since the time of the dinosaurs.

And it’s not just pandas, caribou, and other cuddly things that are dying out — many of the smaller organisms that keep the ecosystem running smoothly are disappearing, as are “ugly” animals that are still very important to the environment.

Even though the public is generally supportive of recovery efforts, the government conservationists responsible for this work almost always face budget shortfalls. If you’re willing to deal with the challenges of working as a wildlife biologist , it can be a highly rewarding career.

8. Astrobiology

We haven’t discovered life outside of Earth — yet. Astronomers have crunched the numbers and found overwhelming support for the idea that somewhere out in the cosmos, there’s a good chance life exists. Astrobiologists study what those life forms might look like, such as how they reproduce and survive.

Even before we find extraterrestrial life, though, there’s plenty of work for astrobiologists to do now with preparing current Earth-dwellers for when we’ll eventually jet off this planet. Astrobiologists study things like how gravity affects astronauts’ bones, how plants grow in space, and what happens to circadian rhythms on planets with a different day/night cycle.

9. Synthetic Biology

Evolution hasn’t created the perfect version of everything yet, but it’s come pretty close in a lot of areas. There are lessons we can learn about all kinds of things from the natural world, such as how to design the quietest aircraft by studying owl feathers .

Other synthetic biologists focus on how to redesign organisms to perform useful tasks. For example, you could specialize in engineering microorganisms to produce medical-grade insulin or help with bioremediation efforts in polluted areas.

10. Epigenetics

What if I told you that the stresses your grandparents endured, such as famines, wars, and inequality, could still be affecting you today through your genes ? It’s not such a far-fetched idea when you consider the field of epigenetics.

Epigenetics is the study of how genes are inherited and whether they’re active or inactive.

Epigenetics is the study of how genes are inherited in either a switched-on or switched-off state. Even though you inherit certain genes from your ancestors, some might not actually be “turned on” despite being present in your DNA. Just like a light switch, genes that have been “turned off” are technically inactive.

Epigenetics is a relatively new field of biology that adds a whole new layer of complexity to the already exciting realm of genetic research.

A scientist gazes at a screen displaying color-coded genetic patterns, the images reflecting off his glasses.

Andrew Brookes/Getty Images

Never Stop Learning

These 10 biology topics are some of the hottest areas in scientific research today, but don’t limit yourself — there’s more than enough knowledge to satisfy a curious mind in any field. You might just have to look a little closer under the microscope to find an entirely new world.

Feature Image: sanjeri / Getty Images

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Are you in need of captivating and achievable research topics within the field of biology? Your quest for the best biology topics ends right here as this article furnishes you with 100 distinctive and original concepts for biology research, laying the groundwork for your research endeavor.
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These 10 biology topics are some of the hottest areas in scientific research today, but don’t limit yourself — there’s more than enough knowledge to satisfy a curious mind in any field. You might just have to look a little closer under the microscope to find an entirely new world.

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49 Most Interesting Biology Research Topics

What is biology?

Climate change and ecosystems

Microbiome and human health

Urban biodiversity conservation

Bioengineering

Neurobiology

Plant epigenomics

Conservation genomics

Zoonotic disease transmission

Bioinformatics

Regenerative medicine

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What Is Biology?

What Is A Good Biology Research Topic?

Tips For Choosing A Biology Research Topics

1. Choose What Interests You

2. Select a Significant Topic

3. Check If It’s Doable

4. Add Your Unique Perspective

5. Seek Guidance

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Molecular Biology Research Topics For Undergraduates

Life Science Research Topics For High School Students

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