Quantitative Biology
The Simons Center for Quantitative Biology (SCQB) is 麻豆传媒社区鈥檚 home for mathematical, computational, and theoretical research in biology. Research at the SCQB focuses broadly on revealing how genomes work, how they evolve, and what makes them go wrong in disease. Members of the SCQB also develop computational tools and genomic technologies that are broadly useful to the community. The SCQB is supported by a generous endowment from the . Additional funding has been provided by the Starr Foundation and Lavinia and Landon Clay.
Announcements
The SCQB is a growing group with positions at various levels.
We are accepting applications for our new postdoc training program in machine learning.
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Our faculty are experts in the mathematical and physical sciences who address open problems in biology, often in close collaboration with experimentalists. Most research in the center falls in the general areas of gene regulation, evolutionary genomics, disease-related human genomics, and genomic technology development. However, our work also touches on neuroscience, immunology, and plant biology, among other fields.
Members of the SCQB maintain close collaborative ties across Laboratoryand with many other New York area groups, including Stony Brook University and the New York Genome Center.
Leadership
Chair
Adam Siepel, Ph.D.
QB Curriculum
Justin Kinney, Ph.D.
QB Seminar Series
Hannah Meyer, Ph.D.
Center Staff
Sr. Scientific Administrator & Assistant to the Chair
Susan Fredricks
Sr. Scientific Administrator
Antonia Little
Assistant Director of Administration, Cancer & Simons Centers
Katie Brenner
Quantitative Biology External Advisory Committee
This Simons Center for Quantitative Biology External Advisory Committee meets annually to provide strategic advice and general guidance.
, Ph.D.
Professor of Molecular Biology and Genetics
Cornell University
, Ph.D.
Anne T. and Robert M. Bass Professor of Humanities and Sciences
Professor of Statistics
Stanford University
, Ph.D.
Professor of Biological Sciences and Systems Biology
Columbia University
, Ph.D.
Viola Ward Brinning and Elbert Calhoun Brinning Professor
Head of Laboratory of Theoretical Condensed Matter Physics
The Rockefeller University
, Ph.D.
Silver Professor of Neural Science, Mathematics, Data Science, and Psychology | New York University
Scientific Director | Center for Computational Neuroscience, FlatIron Institute, Simons Foundation
, Ph.D. (Chair)
Bloomberg Distinguished Professor of Biomedical Engineering, Computer Science, and Biostatistics
Director, Center for Computational Biology
Johns Hopkins University
Simons Center for Quantitative Biology Annual Reports
- 2023 Annual Report (pdf)
- 2022 Annual Report (pdf)
- 2021 Annual Report (pdf)
- 2020 Annual Report (pdf)
- 2019 Annual Report (pdf)
- 2018 Annual Report (pdf)
At the Lab Season 1 Research Rewind: AI+
October 29, 2024
This season鈥檚 final Research Rewind brings us from the realm of quantitative biology to neuroscience, genomics, and beyond.
How does cancer spread? Follow the map
September 25, 2024
LaboratoryProfessor Adam Siepel and postdoc Armin Scheben use genetic barcodes to map how prostate cancer spreads.
The curious immune cells caught between worlds
September 24, 2024
麻豆传媒社区鈥檚 Hannah Meyer shows innate-like T cells mature differently in humans and mice. Her discovery could improve preclinical immunotherapy studies.
Laboratorygrad student wins International Birnstiel Award
September 23, 2024
Shushan Toneyan won the award for her thesis research in 麻豆传媒社区鈥檚 Koo lab. Toneyan is the co-creator of CREME, an AI-powered virtual laboratory.
Is CREME AI鈥檚 answer to CRISPR?
September 16, 2024
CREME, the latest AI toolkit from 麻豆传媒社区, is a virtual laboratory that may help scientists find new therapeutic targets in the genome.
The nervous system鈥檚 matchmaker
September 2, 2024
麻豆传媒社区鈥檚 Saket Navlakha has devised a new computer algorithm that could have many popular real-world applications. His inspiration: the nervous system.
At the Lab Episode 17: AI SQUID
July 30, 2024
Tune in to this week鈥檚 podcast to hear about the latest artificial intelligence model coming out of 麻豆传媒社区.
At the Lab Episode 14: What鈥檚 that smell?
July 9, 2024
You might not realize, but that question is central to the human experience. On this week鈥檚 podcast, 麻豆传媒社区鈥檚 Saket Navlakha sniffs out answers.
SQUID pries open AI black box
June 21, 2024
麻豆传媒社区鈥檚 Koo and Kinney labs have built a tool to suss out how AI analyzes the genome. What sets it apart? Decades of quantitative genetics knowledge.
At the Lab Episode 8: Birds of a feather
May 21, 2024
How did some birds get such distinct colors? LaboratoryProfessor Adam Siepel joins us for a journey across evolution鈥檚 鈥渋slands of differentiation.鈥
Autism genetics: The faces behind the data
May 16, 2024
Laboratoryresearch on autism involves massive databases with thousands of genomes. Meet a few of the brave individuals who help make this work possible.
The LaboratorySchool of Biological Sciences鈥 class of 2024
May 5, 2024
The School of Biological Sciences awarded Ph.D. degrees to 11 students this year. Here are some stories and reflections from their time at 麻豆传媒社区.
At the Lab Episode 5: A heart of golf
April 30, 2024
A 500-year-old mystery stumbled on by Leonardo da Vinci has been solved using modern clinical data. Meet the Laboratoryscientist at the heart of it all.
Why some RNA drugs work better than others
March 6, 2024
麻豆传媒社区鈥檚 Justin Kinney and Spinraza inventor Adrian Krainer tested the newly approved SMA treatment, risdiplam, and another RNA therapeutic, branaplam.
Can AI uncover breast cancer risk factors?
February 26, 2024
This question lies at the heart of a new interdisciplinary collaboration between 麻豆传媒社区鈥檚 Camila dos Santos and Peter Koo.
A quiz for the ages
January 29, 2024
Want to know the secret to a long life? So do Laboratoryscientists. Take this short quiz to see what they鈥檝e found out about aging and longevity.
Joshua-Tor named LaboratoryDirector of Research
January 2, 2024
The Laboratoryprofessor and HHMI investigator steps into her new role effective January 2, 2024.
Smells like learning
October 31, 2023
Laboratoryresearch suggests certain neurons help us tell apart different smells while others help us learn to distinguish between similar odors.
You say genome editing, I say natural mutation
October 19, 2023
Laboratoryscientists have discovered that evolution and genome editing in crops are less predictable than previously thought.
Holy immunity! Bat genes key against COVID, cancer
October 16, 2023
Rapid evolution has streamlined bats鈥 immune systems. This may explain why they鈥檙e resistant to cancer and viruses like Ebola or COVID-19.
How popular steroids could mess up some cancer treatments
June 23, 2023
Scientists have long wondered how common steroids work and why cancer immunotherapy fails in certain patients. The answers may be one and the same.
The digital dark matter clouding AI
June 5, 2023
Scientists have unknowingly encountered mysterious noise while using AI to decipher our genetic code. Laboratoryhas found a way to cut through the fog.
AI training: A backward cat pic is still a cat pic
May 4, 2023
This basic rule of thumb is helping 麻豆传媒社区鈥檚 quantitative biologists train AI to get a better read of the human genome.
How evolved is your knowledge?
January 26, 2023
Test your knowledge of evolution with this quiz, inspired by the March 2023 performances of Isabella Rossellini鈥檚 play, Darwin鈥檚 Smile, at 麻豆传媒社区.
Unlocking cancer鈥檚 ancestry
December 27, 2022
New software may help reveal the complete connections between ancestry and cancer, which could lead to better, more personalized treatments.
Finding the right AI for you
December 5, 2022
AI鈥檚 popularity has reached a point where there are too many options. How do you know which AI is right for you? Laboratoryscientists have a solution.
Welcome to Biology + Beyond
November 14, 2022
LaboratoryPresident and CEO Bruce Stillman introduces a special issue of Nautilus magazine now online, featuring the Lab鈥檚 latest groundbreaking research
Even fruit flies count
October 25, 2022
Fruit flies know if they鈥檝e smelled an odor once, twice, many times, or never before.
Exposing the evolutionary weak spots of the human genome
September 22, 2022
Researchers built a computer program that tracks harmful mutations throughout human evolution. It may help uncover the origins of genetic diseases.
How the thymus trains T cells to fight infections
August 2, 2022
Laboratoryscientists identified, for the first time, the RNA in humans used to train T cells to attack dangerous or foreign proteins in the body.
AI is helping scientists explain our brain
February 28, 2022
Neuroscientists are turning to artificial intelligence to help them understand the brain, but what if AI misses the true story?
Getting a step ahead of TB鈥檚 drug resistance evolution
February 15, 2022
Mutations are not random, with some kinds of changes occurring more often than others. Laboratoryresearchers may be able to predict which direction evolution is li
Brain waves churn differently when paying attention
February 2, 2022
See the shapes and speeds of electrical waves in the brain change in response to attentiveness.
Finding structure in the brain鈥檚 static
February 1, 2022
Laboratoryresearchers found that the brain鈥檚 state of attentiveness may be encoded in the shapes and speeds of slow electrical waves.
Calculating the path of cancer
October 4, 2021
A new mathematical approach is helping cancer researchers at Laboratorydetermine how mutations lead to different behaviors in cancerous cells.
LaboratoryPh.D. program: Graduating class of 2021
August 22, 2021
The LaboratorySchool of Biological Sciences awarded Ph.D. degrees to seven students this year, who describe some of their experiences.
URP: Summer camp for undergrads
July 29, 2021
The Undergraduate Research Program brings college students from around the world to Laboratoryfor a summer of research and fun.
How to outwit evolution
July 21, 2021
LaboratoryAssistant Professor David McCandlish uses statistical methods to predict the evolution of antibiotic resistance in bacteria.
Using 鈥済uilt by association鈥 to classify cells
July 14, 2021
Using a new computational statistics tool, Laboratoryresearchers classify cells to understand how an organism functions.
Solving genetic disease puzzles with quantitative biology
June 17, 2021
Laboratoryquantitative biologist Jesse Gillis teams up with an immunology specialist at Northwell Health to analyze a complex genetic disorder.
Making AI algorithms show their work
May 13, 2021
AI machines are often better than humans at discerning patterns. Laboratoryresearchers developed a way to find out why.
How a bad day at work led to better COVID predictions
May 3, 2021
A Laboratorycomputer scientist and an MSK infectious disease physician developed a method for predicting COVID-19 severity in cancer patients.
Are you sure you heard that sound?
April 14, 2021
Laboratoryand Washington University of St. Louis researchers studied hallucination-like perceptions in humans and mice that mimic schizophrenia symptoms.
AI researchers ask: What鈥檚 going on inside the black box?
February 8, 2021
Although researchers have figured out how to train computers to recognize things, they have yet to understand how machines make those predictions.
How does anyone stay healthy in a world full of germs?
January 15, 2021
Computational biology is uncovering the immune system鈥檚 tricks for identifying foreign invaders.
Mice with too many chandelier cells lack depth perception
December 8, 2020
Chandelier cells should decrease in number as animals develop. Mice with too many cells lack depth perception.
Problems with depth perception caused by too many cells
December 7, 2020
Chandelier cells should decrease in number as animals develop. If too many remain, brain systems may not work properly.
How to figure out what you don鈥檛 know
November 30, 2020
LaboratoryAssistant Professor Tatiana Engel discusses how a model like Ptolemy鈥檚 seems to explain the world and yet is wrong.
Birds of a feather do flock together
November 17, 2020
Researchers found a genetic mechanism for how brand new species acquire distinct traits.
How to figure out what you don鈥檛 know
October 26, 2020
Sometimes, what seems like a good way to understand the world turns out to be wrong. A new machine learning tool lets scientists find better answers.
How to tune out common odors and focus on important ones
May 11, 2020
The fly brain uses a simple computing trick to ignore prevalent odors and focus on newer but rarer odorants.
Predicting the evolution of genetic mutations
April 14, 2020
Laboratoryquantitative biologists have designed a computational approach for predicting the evolution of a rapidly mutating virus or cancer.
A science career path: David McCandlish
April 10, 2020
Assistant Professor David McCandlish is a quantitative biologist who walks the line between advanced mathematics and the life sciences at 麻豆传媒社区.
COVID-19 machine learning effort: Preprints are key
March 25, 2020
Preprint papers play a key role in a U.S. government-led machine learning effort to understand the COVID-19 pandemic.
Tatiana Engel named 2020 Sloan Fellow
February 12, 2020
Assistant Professor Tatiana Engel is named a 2020 Sloan Fellow for her work on computational models of decision-making.
What Google could learn from a fruit fly
January 21, 2020
By tapping into life鈥檚 algorithms, scientists are finding elegant solutions to some of the hardest problems in computer science.
The non-human living inside of you
January 9, 2020
A large part of human DNA doesn鈥檛 aid the normal workings of the body. This 鈥渏unk DNA鈥 contains ancient viruses that may spur diseases like ALS.
Finally, machine learning interprets gene regulation clearly
December 26, 2019
Machine learning and a new kind of easily-interpretable artificial neural network is helping scientists make sense of crucial gene regulation.
Saket Navlakha taps the power of biology and computers
December 17, 2019
Associate Professor Saket Navlakha is bringing ideas about 鈥渁lgorithms in nature鈥 to the Simons Center for Quantitative Biology.
Bridge to education
December 15, 2019
麻豆传媒社区鈥檚 DNA Learning Center builds new bridges between unique science education and diverse groups.
Making sense of the genome…at last
December 6, 2019
Quantitative biologists like 麻豆传媒社区鈥檚 Adam Siepel are finally making sense of the flood of data contained in the human genome.
The Lab partners with award-winning magazine
December 6, 2019
Nautilus, an award-winning science magazine, has partnered with Laboratoryto bring the story of the lab鈥檚 scientists and research to a brand-new audience.
Research profile: Adam Siepel
November 12, 2019
Adam Siepel, Chair of the Simons Center for Quantitative Biology, uses advanced computational methods to solve complex biological questions.
Peter Koo wants to understand how machines learn biology
September 20, 2019
Dr. Peter Koo joins the Laboratoryfaculty as an assistant professor. His focus is on exploring how artificial intelligence integrates with biology and genomics.
Of mice and model organisms
July 31, 2019
An in-depth look at how veterinarians at Laboratoryhelp take care of the various organisms that help researchers answer fundamental biological questions.
There’s more to smell than meets the nose
July 22, 2019
Neuroscience researchers work to figure out our brains process smells, including what features are essential to identifying and separating odors.
Quantifying how the brain smells
July 22, 2019
Neuroscience researchers at Laboratoryare trying to figure out how the brain processes smells and what features of odors are important in that process.
Seeing with sequencing—A public lecture with three Laboratoryexperts
April 19, 2019
Quantitative biologists discuss how physics, modern computing power, and a new perspective on biology can make sense of our complex genomes.
NIH grant awarded for interneuron research
April 4, 2019
Laboratorypostdoc Maggie Crow will use her NIH grant to pursue the quantification and analysis of specific types of neurons in the brain.
Hannah Meyer joins LaboratoryQuantitative Biology faculty
March 26, 2019
Hannah Meyer is the newest Quantitative Biology Fellow at 麻豆传媒社区, studying how our immune system learns to identify and fight pathogens.
Genetic ‘usual suspects’ identified in researchers’ new list
March 4, 2019
An exhaustive ranked list of 鈥渦sual suspect鈥 genes involved in disease may prove invaluable for future research and drug discovery.
David McCandlish named Sloan Research Fellow
February 19, 2019
Assistant Professor David McCandlish has been named a 2019 Sloan Research Fellow for his promising work in the field of quantitative biology.
How does math help us understand the brain?
January 31, 2019
An exploration of how computational neuroscientist Tatiana Engel uses math to understand how the brain makes decisions.
How does natural selection affect the genome?
December 18, 2018
Adam Siepel explains how natural selection can tell researchers how informative sifting through the complex human genome will be.
How much are we learning? Natural selection is science鈥檚 best critic
December 17, 2018
Researchers determine that natural selection and our evolutionary history may be the best guides for future research.
Molly Hammell wins CZI award for ALS study
December 5, 2018
Associate Professor Molly Hammell wins award for proposed study to find transposable elements that are implicated in ALS.
The big problem of small data: A new approach
October 18, 2018
You鈥檝e heard of 鈥渂ig data鈥 but what about small? Researches have crafted a modern approach that could solve a decades-old problem in statistics.
A science writer鈥檚 quest to understand heredity
May 30, 2018
LabDish spoke with science writer Carl Zimmer about what he learned about heredity as he zig-zagged through Laboratorywhile writing his new book.
Portrait of a Neuroscience Powerhouse
April 27, 2018
A relatively small neuroscience group at Laboratoryis having an outsized impact on a dynamic and highly competitive field
Evolving sets of gene regulators explain some of our differences from other primates
January 29, 2018
What makes us different from our primate relatives? Gene regulation is one important evolutionary factor
New method can more precisely determine when a cell has 鈥榗ashed鈥 RNA 鈥榗hecks鈥 written by active genes
January 26, 2018
Laboratoryscientists have designed software that enables biologists to determine with unprecedented accuracy how much protein a given cell is making.
Base Pairs Episode 13: A lesson in class
December 15, 2017
We share three stories about classification in life sciences and how genetic information is changing how we define important categories.
A lesson in class
December 15, 2017
In this episode of Base Pairs, we discuss how genetic information is changing how we define important categories.
Brain cell types are defined by gene activity that shapes their communication patterns
October 20, 2017
Laboratoryresearchers have identified key families of genes in neurons which drive communication.
First cell-type census of mouse brains: surprises about structure, male-female differences
October 5, 2017
A multiyear project in the Brain Initiative, qBrain is already revealing the brain as never before.
Neuron types in the brain are defined by gene activity that shapes their communication patterns
September 21, 2017
Neurons are defined by determining which cells they connect with and how they communicate across synapses
Base Pairs Episode 11.5: What Silicon Valley and biology research share
September 15, 2017
A听few favorite moments from our talk听with theoretical physicist and quantitative biologist, Associate Professor Gurinder 鈥淢ickey鈥 Atwal.
What Silicon Valley and biology research share
September 15, 2017
Further discussion with Associate Professor Gurinder "Mickey" Atwal about the need for more math and physics in biology.
Base Pairs Episode 11: Biology, behind the screens
August 15, 2017
A 鈥渂ehind the screens鈥 look at how biology is addressing its 鈥渕ost wonderful problem鈥濃攖oo much data. Associate Professor Gurinder S. 鈥淢ickey鈥 Atwal.
Biology, behind the screens
August 15, 2017
Associate Professor Gurinder "Mickey" Atwal talks about the essential mystery at the center of quantitative biology.
New statistical method finds shared ancestral gene variants involved in autism鈥檚 cause
June 21, 2017
Researchers find children with autism are genetically more like other autistic children than their unaffected siblings.
Reconstructing ancient human history from DNA
June 20, 2017
Free public lecture featuring Adam Siepel, Ph.D., LaboratoryProfessor and Chair of the Simons Center for Quantitative Biology.
Math teacher wins school popularity contest (again)
June 7, 2017
Math isn鈥檛 exactly known as 鈥渆veryone鈥檚 favorite subject,鈥 yet Associate Professor Mickey Atwal has won the Watson School of Biological Science鈥檚.
Newly discovered mutations impair key cell pathways in pancreatic cancer
May 8, 2017
Researchers have found important new clues to the development of pancreatic cancer.
Non-modern family
August 15, 2016
This episode on Base Pairs explores how genetic information to better understand human history.
Neanderthals mated with modern humans much earlier than previously thought
May 17, 2016
Using several methods of DNA analysis, a research team has found strong evidence of interbreeding between Neanderthals and modern humans.
Neanderthals mated with modern humans much earlier than previously thought, study finds
February 12, 2016
Using several different methods of DNA analysis, an international research team has found what they consider to be strong evidence of interbreeding.
The biggest beast in the Big Data forest? One field鈥檚 astonishing growth is, well, 鈥榞enomical鈥!
July 6, 2015
Scientists work to figure out how to capture, store, process and interpret all that genome-encoded biological information.
Laboratoryquantitative biologist Michael Schatz awarded 2015 Sloan Foundation Research Fellowship
February 20, 2015
Associate Professor Michael Schatz receives a 2015 Alfred P. Sloan Foundation Research Fellowship
Harnessing data from nature鈥檚 great evolutionary experiment
January 21, 2015
Scientists develop a computational method to estimate the importance of each letter in the human genome
As data generation has grown increasingly efficient and inexpensive, the interpretation of large data sets has emerged as a limiting step for advances in biology. Researchers at the SCQB aim to make sense of this 鈥渂ig data鈥 through the development of innovative modeling, algorithmic, and machine-learning methods, drawing broadly from techniques in mathematics, computer science, and physics. Research in the center is diverse but is permeated by the following four major themes: Gene Regulation, Evolutionary Genomics, Genomic Disease Research, and Genomic Technology.
Gene Regulation
Kinney and McCandlish are interested in developing both theoretical and experimental methods, along with computational and mathematical tools, for elucidating the relationship between biological sequences and biological functions ranging from gene expression to protein function.
Hammell studies several topics related to gene regulation, including the behavior of small non-coding RNAs, inference of gene regulatory networks, and the impact of transposable elements on gene expression. She has also developed methods for the analysis of single-cell RNA-seq data.
Siepel is broadly interested in modeling the regulation of gene expression in mammals, ranging from transcription factor binding and chromatin accessibility, to transcription initiation and elongation, to the determination of RNA stability.
Meyer studies central T cell tolerance induction and gene regulation in the thymus. Her work combines genomics studies with in silico models to understand the fundamental principles of thymus biology.
Koo studies the functional impact of genomic mutations through a computational lens using data-driven听 machine learning solutions. He is broadly interested in applications for studying gene regulation and protein (dys) function.
听
Evolutionary Genomics
McCandlish develops theory and mathematics to address a number of open questions in evolutionary genetics, including the dynamics of evolution when mutation is rate-limiting or exhibits biased patterns, and the evolutionary implications of epistasis, i.e. interactions between mutations or genes.
Siepel uses evolutionary methods to identify regulatory elements, to reconstruct early human history, including interbreeding events with Neandertals, and to estimate the fitness consequences of new mutations in the human genome. He is also applying similar methods to agriculturally important plants such as maize and rice.
In addition, Iossifov uses evolutionary signatures to aid in the identification of genes associated with autism spectrum disorder, and Krasnitz uses phylogenetic methods to study the evolution of tumors.
Navlakha works at the interface of theoretical computer science, machine learning, and systems biology. He primarily studies how collections of molecules, cells, and organisms process information and solve interesting computational problems critical for survival.
Genomic Disease Research
Iossifov aims to understand the genetics of autism spectrum disorder (ASD) through the analysis of large genomic data sets, in close collaboration with Mike Wigler鈥檚 research group and the New York Genome Center.
Krasnitz develops mathematical and statistical tools to characterize the cellular composition, genomic disruptions, evolutionary history, and invasive capacity of malignant tumors, often in collaboration with clinical oncologists.
Genomic Technology Development
Levy, Krasnitz, and Iossifov work closely with the Wigler laboratory in the development of new DNA and RNA sequencing methods, single-cell genomic technologies, and cancer diagnostics.
Kinney is a pioneer in the development of massively parallel reporter assays for characterizing the relationship between regulatory sequences and gene expression, including both transcription and RNA splicing.
More detailed information about research at the SCQB is available from the faculty websites of the SCQB members and associate members.
In addition to its research activities, the SCQB serves as a hub for education, training and research in the quantitative life sciences.
For more information please contact SCQB@cshl.edu.
Events
SCQB Seminar Series
The SCQB Seminar Series is a weekly symposium featuring a rotating roster of graduate students, postdocs and invited guests. Seminars are held most Wednesdays at noon during the academic calendar year.
QB Meetings and Conferences
Members and Associate Members of the SCQB faculty organize relevant QB Meetings and Conferences hosted at Laboratoryand around the NY area.
- Probabilistic Modeling in Genomics
- Biological Data Science
- NY Populations Genomics Workshop
QB Scientific Tea
The SCQB community which includes faculty, postdocs, graduate students, staff and special guests are invited to attend weekly catered informal gatherings to discuss their research and other relevant topics.
Journal Clubs
Members of the SCQB host a bi-weekly Sequence/Function Journal club and a monthly Deep Learning journal club during the academic calendar year.
Opportunities for Postdoctoral Researchers
The LaboratoryFellows Program
The LaboratoryFellows Program supports research fellows, who function independently but with mentoring from the senior faculty. The program is designed for exceptional quantitative biologists who have recently received their Ph.D. or M.D. degree and who are sufficiently talented and experienced to forgo standard postdoctoral training.
Interdisciplinary Scholars in Experimental and Quantitative Biology Program (ISEQB)
The Interdisciplinary Scholars in Experimental and Quantitative Biology (ISEQB) is an innovative funding opportunity for postdoctoral research open to applications in all areas of research at 麻豆传媒社区, including genetics, cancer, plant biology and neuroscience. The ISEQB is designed to help recruit new postdocs or fund existing Laboratorypostdocs who are interested in both wet-lab and dry-lab research. This program aims to catalyze collaborative research as well as promote the growth of the QB community at 麻豆传媒社区.
Course Work
School of Biological Sciences QB Bootcamp at 麻豆传媒社区
The School of Biological Sciences QB Bootcamp is a 2.5-day rapid introduction to Python and the computer cluster at Laboratorytaught each Fall by the SCQB faculty to provide incoming students with working knowledge in programming in preparation for the full-semester Specialized Discipline Course in Quantitative Biology.
Specialized Discipline Course in Quantitative Biology at 麻豆传媒社区
The Specialized Discipline Course in Quantitative Biology is a 16-week course that aims to equip incoming students with basic training in computer programming, modern statistical methods and physical biology. Using a probabilistic and Bayesian approach, the course covers probabilities, statistical fluctuations, Bayesian inference, significance testing, fluctuations, diffusion, information theory, neural signal processing, dimensional reduction, Monte Carlo methods, population genetics and DNA sequence analyses.
Advanced Coursework in Quantitative Biology
The Simons Center for Quantitative Biology (SCQB) provides Advanced Coursework in Quantitative Biology to graduate students, postdocs and scientific staff through independent study programs and online coursework.
Chen WC, Zhou J, Sheltzer JM, Kinney JB, McCandlish DM. Non-parametric Bayesian density estimation for biological sequence space with applications to pre-mRNA splicing and the karyotypic diversity of human cancer. bioRxiv, 2020.
Chorbadjiev, L., J. Kendall, J. Alexander, 鈥, A. Krasnitz (2020). 鈥淚ntegrated Computational Pipeline for Single-Cell Genomic Profiling.鈥 JCO Clin Cancer Inform 4: 464-471.
Hejase, H. A., A. Salman-Minkov, L. Campagna, 鈥, A. Siepel (2020). 鈥淕enomic islands of differentiation in a rapid avian radiation have been driven by recent selective sweeps.鈥 Proc Natl Acad Sci USA.
Hubisz, M. J., A. L. Williams and A. Siepel (2020). 鈥淢apping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph.鈥 PLoS Genet 16(8): e1008895.
Ireland, W. T., S. M. Beeler, E. Flores-Bautista, 鈥, J. B. Kinney and R. Phillips (2020). 鈥淒eciphering the regulatory genome of Escherichia coli, one hundred promoters at a time.鈥 Elife 9.
Koo PK, Ploenzke M. Improving representations of genomic sequence motifs in convolutional networks with exponential activations. Nat. Machine Intel. 3:258-266, 2021.
Koo PK, Ploenzke M. Deep learning for inferring transcription factor binding sites. Curr Opin Syst Biol. 19:16- 23, 2020
Li, S., J. Kendall, S. Park, 鈥, A. Krasnitz, D. Levy and M. Wigler (2020). 鈥淐opolymerization of single-cell nucleic acids into balls of acrylamide gel.鈥 Genome Res 30(1): 49-61.
McCandlish, DM and G. I. Lang (2020). 鈥淓volution of epistasis: small populations go their separate ways.鈥 J Mol Evol 88(5): 418-420.
Meyer, HV, T. J. W. Dawes, M. Serrani, W. Bai, et al. (2020). 鈥淕enetic and functional insights into the fractal structure of the heart.鈥 Nature 584(7822): 589-594.
O鈥橬eill K, Brocks D, Gale Hammell M. Mobile genomics: tools and techniques for tackling transposons. Philos Trans R Soc Lond B Biol Sci 375(1795):20190345, 2020.
Shen, Y., S. Dasgupta and S. Navlakha (2020). 鈥淗abituation as a neural algorithm for online odor discrimination.鈥 Proc Natl Acad Sci USA 117(22): 12402-12410.
Tam, O. H., N. V. Rozhkov, R. Shaw, 鈥, M. Gale Hammell (2019). 鈥淧ostmortem Cortex Samples Identify Distinct Molecular Subtypes of ALS: Retrotransposon Activation, Oxidative Stress, and Activated Glia.鈥 Cell Rep 29(5): 1164-1177.e1165.
Zhou, J. and D. M. McCandlish (2020). 鈥淢inimum epistasis interpolation for sequence-function relationships.鈥 Nat Commun 11(1): 1782.
Members of the SCQB have created a number of freely available software tools and web resources for the research community. Here is a list of all the available software tools.
Developed by the , SUFTware (Statistics Using Field Theory) provides fast and lightweight Python implementations of Bayesian Field Theory algorithms for low-dimensional statistical inference. SUFTware currently supports the one-dimensional density estimation algorithm DEFT (Density Estimation Using Field Theory)
Developed by the , MetaNeighbor allows users to quantify cell type replicability across single-cell RNA data sets. For a broader array of software (including those packages outside of R) from the Gillis lab please visit:
Alexander Dobin
Computational genomics; transcriptomics; epigenomics; gene regulation; big data; precision medicine
Tatiana Engel
Computational and theoretical neuroscience; machine learning; statistical physics
Jesse Gillis
Gene networks; gene function prediction; guilt by association; neuropsychiatric; hub genes; multifunctionality; computational genomics
Alexei Koulakov
Theoretical neurobiology; quantitative principles of cortical design; computer science; applied mathematics
Richard McCombie
Genomics of psychiatric disorders; genomics of cancer; computational genomics; plant genomics
Partha Mitra
Neuroscience and theoretical biology
Doreen Ware
Genomics; genome evolution; genetic diversity; gene regulation; plant biology; computational biology
Seungtai (Chris) Yoon
Autism, SFARI, AGRE, SNVs, CNVs, whole-genomeexome sequencing, single-cell sequencing and bulksingle RNA sequencing
Ivan Iossifov
Every gene has a job to do, but genes rarely act alone. Biologists have built models of molecular interaction networks that represent the complex relationships between thousands of different genes. I am using computational approaches to help define these relationships, work that is helping us to understand the causes of common diseases including autism, bipolar disorder, and cancer.
Justin Kinney
Research in the Kinney Lab combines mathematical theory, machine learning, and experiments in an effort to illuminate how cells control their genes. These efforts are advancing the fundamental understanding of biology and biophysics, as well as accelerating the discovery of new treatments for cancer and other diseases.
Peter Koo
Deep learning has the potential to make a significant impact in basic biology and cancer, but a major challenge is understanding the reasons behind their predictions. My research develops methods to interpret this powerful class of black box models, with a goal of elucidating data-driven insights into the underlying mechanisms of sequence-function relationships.
Alexander Krasnitz
Many types of cancer display bewildering intra-tumor heterogeneity on a cellular and molecular level, with aggressive malignant cell populations found alongside normal tissue and infiltrating immune cells. I am developing mathematical and statistical tools to disentangle tumor cell population structure, enabling an earlier and more accurate diagnosis of the disease and better-informed clinical decisions.
Dan Levy
We have recently come to appreciate that many unrelated diseases, such as autism, congenital heart disease and cancer, are derived from rare and unique mutations, many of which are not inherited but instead occur spontaneously. I am generating algorithms to analyze massive datasets comprising thousands of affected families to identify disease-causing mutations.
David McCandlish
Some mutations are harmful but others are benign. How can we predict the effects of mutations, both singly and in combination? Using data from experiments that simultaneously measure the effects of thousands of mutations, I develop computational tools to predict the functional impact of mutations and apply these tools to problems in protein design, molecular evolution, and cancer.
Hannah Meyer
A properly functioning immune system must be able to recognize diseased cells and foreign invaders among the multitude of healthy cells in the body. This ability is essential to both prevent autoimmune diseases and fight infections and cancer. We study how a specific type of immune cells, known as T cells, are educated to make this distinction during development.
Saket Navlakha
Biological systems must solve problems to survive, and their solutions can be viewed as “algorithms.” Our goal is to uncover these algorithms, translate them to improve computer science, and use them to spark new hypotheses about biological function and dysfunction.
Adam Siepel
I am a computer scientist who is fascinated by the challenge of making sense of vast quantities of genetic data. My research group focuses in particular on questions involving molecular evolution and transcriptional regulation, with applications to cancer and other diseases as well as to plant breeding and agriculture.