The Simons Center for Quantitative Biology (SCQB) is Cold Spring Harbor Laboratory’s 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 Simons Foundation. Additional funding has been provided by the Starr Foundation and Lavinia and Landon Clay.
We are a growing group with positions at various levels.
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 CSHL and with many other New York area groups, including Stony Brook University and the New York Genome Center.
Adam Siepel, Ph.D.
Sr. Scientific Administrator & Assistant to the Chair
Sr. Scientific Administrator
QB Science Manager
Quantitative Biology External Advisory Committee
This Simons Center for Quantitative Biology External Advisory Committee meets annually to provide strategic advice and general guidance.
Andrew G. Clark, Ph.D.
Professor, Department of Molecular Biology and Genetics
David L. Donoho, Ph.D.
Professor, Department of Statistics
Molly Przeworski, Ph.D.
Professor, Biological Sciences
Eric D. Siggia, Ph.D.
Head of Laboratory of Theoretical Condensed Matter Physics
The Rockefeller University
Eero P. Simoncelli, Ph.D.
Investigator, Howard Hughes Medical Institute
Silver Professors, New York University
Steven L. Salzberg, Ph.D.
Professor of Biomedical Engineering, Computer Science and Biostatistics
Johns Hopkins School of Medicine
Molly Hammell wins CZI award for ALS study
December 5, 2018
Associate Professor Molly Hammell has been awarded the Chan Zuckerberg Initiative (CZI) Ben Barres Early Career Acceleration Award for her proposed work on amyotrophic lateral sclerosis, better know by the acronym ALS or Lou Gehrig’s disease. ALS is a neurodegenerative disease that causes complete paralysis and eventual death due to the rapid and progressive loss...
The big problem of small data: A new approach
October 18, 2018
Cold Spring Harbor, NY — Big Data is all the rage today, but Small Data matters too! Drawing reliable conclusions from small datasets, like those from clinical trials for rare diseases or in studies of endangered species, remains one of the trickiest obstacles in statistics. Now, Cold Spring Harbor Laboratory (CSHL) researchers have developed a...
Portrait of a Neuroscience Powerhouse
April 27, 2018
At noon every Tuesday from September through June, scenes from a revolution in neuroscience are playing out at Cold Spring Harbor Laboratory. Week after week, over 100 scientists cram themselves into a ground-floor meeting room in the Beckman Laboratory. It’s standing-room only as everyone in the Neuroscience Program settles in to hear details of the...
Evolving sets of gene regulators explain some of our differences from other primates
January 29, 2018
Cold Spring Harbor, NY — Today, biologists add an important discovery to a growing body of data explaining why we’re different from chimps and other primate relatives, despite the remarkable similarity of our genes. The new evidence has to do with the way genes are regulated. It’s the result of a comprehensive genome-wide computational analysis...
New method can more precisely determine when a cell has ‘cashed’ RNA ‘checks’ written by active genes
January 26, 2018
Cold Spring Harbor, NY — DNA has often been called “the book of life,” but this popular phrase makes some biologists squirm a bit. True, DNA bears our genes, which spell out the instructions our cells use to make proteins—those workhorse molecules that make just about everything in life possible. But the precise relationship between...
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 Cold Spring Harbor, NY — Neuroscientists at Cold Spring Harbor Laboratory (CSHL) have mobilized advanced imaging and computational methods to comprehensively map—“count”—the total populations of specific types of cells throughout the mouse brain. In a study published today in...
Neuron types in the brain are defined by gene activity that shapes their communication patterns
September 21, 2017
Families of genes encoding proteins involved in communication across synapses define neurons by determining which cells they connect with and how they communicate Cold Spring Harbor, NY — In a major step forward in research, scientists at Cold Spring Harbor Laboratory (CSHL) today publish in Cell a discovery about the molecular-genetic basis of neuronal cell...
What Silicon Valley and biology research share
September 15, 2017
Base Pairs podcast In the age of big data, theoretical physicist and Associate Professor Gurinder “Mickey” Atwal recognized the need for data analysis whizzes in biology research as well as Silicon Valley. Hear more from him, and how he’s using his skills to advance cancer research, in episode 11 of Base Pairs, “Biology, behind the...
Biology, behind the screens
August 15, 2017
Base Pairs podcast The tale of many biologists is one of long hours peering through a microscope in pursuit of answers. But what if we told you that there are scientists making huge waves in the life sciences that have never professionally used a microscope? CSHL’s Mickey Atwal sits down with Base Pairs co-hosts Brian...
New statistical method finds shared ancestral gene variants involved in autism’s cause
June 21, 2017
Cold Spring Harbor, NY — The way you measure things has a lot to do with the value of the results you get. If you want to know how much a blueberry weighs, don’t use a bathroom scale; it isn’t sensitive enough to register a meaningful result. While much more is at stake, the same...
Math teacher wins school popularity contest (again)
June 7, 2017
LabDish blog Math isn’t exactly known as “everyone’s favorite subject,” yet Associate Professor Mickey Atwal has won the Watson School of Biological Science’s teaching award by popular vote for the third time this year. How does he make the math in his quantitative biology course come to life? It’s not uncommon to feel some sort of...
Newly discovered mutations impair key cell pathways in pancreatic cancer
May 8, 2017
Cold Spring Harbor, NY — By closely studying a part of the human genome that has not yet been carefully scrutinized in studies of cancer, researchers at Cold Spring Harbor Laboratory (CSHL) have found important new clues to the development of pancreatic cancer. The researchers looked exclusively at small segments of DNA called promoters in...
Neanderthals mated with modern humans much earlier than previously thought, study finds
February 12, 2016
First genetic evidence of modern human DNA in a Neanderthal individual Cold Spring Harbor, NY — Using several different methods of DNA analysis, an international research team has found what they consider to be strong evidence of an interbreeding event between Neanderthals and modern humans that occurred tens of thousands of years earlier than any...
The biggest beast in the Big Data forest? One field’s astonishing growth is, well, ‘genomical’!
July 6, 2015
Cold Spring Harbor, NY — Who’s about to become the biggest beast in the Big Data forest? A group of math and computing experts have arrived at what they say is a clear answer. It’s not YouTube or Twitter, social media sites that gobble up awesome quantities of bandwidth and generate hard-to-grasp numbers of electronic...
New senior faculty join CSHL
November 10, 2014
LabDish blog This fall, the Lab welcomes six new faculty members. They’re a diverse group—a mix of junior and senior investigators, with research spanning across Biology. Want to know a little more? We’ll be featuring brief profiles all week. So check back for more! Professor Douglas Fearon, Cancer Where are you from? The University of...
Researchers propose new way to make sense of ‘Big Data’
February 15, 2014
Justin Kinney and Gurinder Atwal collaborated to show how a fundamental mathematical quantity called “mutual information” can be used to detect and quantify relationships between variables in large, noisy datasets.
Autism study validates importance of spontaneous causal mutations and sheds new light on gender skew
June 8, 2011
Genetic causation of ASD appears to be highly diverse; Thoughts on why fewer girls have autism Cold Spring Harbor, NY — A clinically extensive and mathematically powerful study of 1000 families with one autistic child and one unaffected sibling has validated a controversial theory of autism’s complex genetic causation. The study for the first time...
2002 CSHL Symposium focuses on cardiovascular system
May 28, 2002
The Cold Spring Harbor Symposium on Quantitative Biology is a premier annual international scientific meeting during which researchers gather for in-depth discussions of carefully selected topics of current interest. The Symposium has been held at Cold Spring Harbor Laboratory every year since 1933 (save 1943-1945). The first public description of the structure of DNA was...
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 “big data” 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.
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.
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.
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’s 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.
Hammell studies the role of transposable element activation in neurodegenerative diseases, particularly amyotrophic lateral sclerosis (ALS) and fronto-temporal dementia (FTD).
Atwal is interested in diverse modeling and statistical inference problems having to do with cancer and immunology, often through consideration of single-cell sequencing data.
Genomic Technology Development
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.
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.
See a list of previously invited speakers (pdf).
QB Meetings and Conferences
Members and Associate Members of the SCQB faculty organize relevant QB Meetings and Conferences hosted at CSHL and around the NY area.
- Probabilistic Modeling in Genomics (ProbGen), November 4-7, 2018
- Biological Data Science, November 7-10, 2018
- NY Populations Genomics Workshop (NY popgen), January 7, 2019
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.
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 Simons Center for Quantitative Biology Fellows Program
The CSHL Simons Center for Quantitative Biology Fellows 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 CSHL, including genetics, cancer, plant biology and neuroscience. The ISEQB is designed to help recruit new postdocs or fund existing CSHL postdocs 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 CSHL. For details on how to apply, affiliates of CSHL can visit the ISEQB flyer.
Watson School QB Bootcamp at CSHL
The Watson School QB Bootcamp is a 2.5-day rapid introduction to Python and the computer cluster at CSHL taught each Fall by the SCQB faculty to provide incoming Watson School 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 CSHL
The Specialized Discipline Course in Quantitative Biology is a 16-week course that aims to equip incoming Watson School 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. With support from the Watson School for Biological Sciences, we are currently offering Coursera’s Machine Learning through a Massive Open Online Course with onsite teaching assistance provided by SCQB postdoctoral researchers
Alexander J, Kendall J, McIndoo J, Rodgers L, Aboukhalil R, Levy D, Stepansky A, Sun G, Chobardjiev L, Riggs M, Cox H, Hakker I, Nowak DG, Laze J, Llukani E, Srivastava A, Gruschow S, Yadav SS, Robinson B, Atwal G, Trotman L, Lepor H, Hicks J, Wigler M, Krasnitz A. Utility of single-cell genomics in diagnostic evaluation of prostate cancer. Cancer Res. 78(2):348-358, 2018.
Buja A, Volfovsky N, Krieger AM, Lord C, Lash AE, Wigler M, Iossifov I. Damaging de novo mutations diminish motor skills in children on the autism spectrum. Proc. Natl. Acad. Sci. U.S.A., 115(8):E1859-E1866, 2018.
Danko CG, Choate LA, Marks BA, Rice EJ, Zhong W, Chu T, Martins AL, Dukler N, Coonrod SA, Wojno EDT, Lis JT, Kraus WL, Siepel A. Dynamic evolution of regulatory element ensembles in primate CD4+ T-cells. Nat. Ecol. Evol., 2(3):537-548, 2018.
Bienvenu F, Akcay E, Legendre S, and McCandlish DM. The genealogical decomposition of a matrix population model with applications to the aggregation of stages. Theor. Popul. Biol., 115:69–80, 2017.
Huang Y-F, Gulko B, Siepel A. Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data. Nat. Genet. 49(4):618–624, 2017.
Kato M, Vasco DA, Sugino R, Narushima D, Krasnitz A. Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer. R. Soc. Open Sci., 4(9):171060, 2017.
Kumar V, Rosenbaum J, Zihua W, Forcier T, Ronemus M, Wigler M, Levy D. Partial bisulfite conversion for unique template sequencing. Nucleic Acids Res., 46(2):e10, 2017.
Stoltzfus A and McCandlish DM. Mutational Biases Influence Parallel Adaptation. Mol. Biol. Evol., 34(9):2163–2172, 2017.
Adams RM, Mora T*, Walczak AM*, Kinney JB*. Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves. eLife 5:e23156, 2016.
Atwal GS, Kinney JB. Learning quantitative sequence–function relationships from massively parallel experiments. J Stat Phys. 162(5):1203–1243, 2016.
Kuhlwilm M, Gronau I, Hubisz MJ, de Filippo C, Prado J, Kircher M, Fu Q, Burbano HA, Lalueza-Fox C, de la Rasilla M, Rosas A, Rudan P, Brajkovic D, Kucan Z, Gusic I, Marques-Bonet T, Andres AM, Viola B, Paabo S, Meyer M, Siepel A, and Castellano S. Ancient gene flow from early modern humans into Eastern Neanderthals. Nature, 530(7591):429–433, 2016.
Wang Z, Andrews P, Kendall J, Ma B, Hakker I, Rodgers L, Ronemus M, Wigler M, Levy D. SMASH, a fragmentation and sequencing method for genomic copy number analysis. Genome Res. 26(6):844–851, 2016.
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 Kinney lab, 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)
Computational genomics; transcriptomics; epigenomics; gene regulation; big data; precision medicine
Computational and theoretical neuroscience; machine learning; statistical physics
Gene networks; gene function prediction; guilt by association; neuropsychiatric; hub genes; multifunctionality; computational genomics
Theoretical neurobiology; quantitative principles of cortical design; computer science; applied mathematics
Genomics of psychiatric disorders; genomics of cancer; computational genomics; plant genomics
Neuroscience and theoretical biology
Computational genomics; genome assembly; transcriptomics; comparative genomics
Genomics; genome evolution; genetic diversity; gene regulation; plant biology; computational biology
The biological landscape is made up of millions of variables that interact in complex and often seemingly random ways. I am applying principles from physical and computational sciences to the study of biology to find patterns in these interactions, to obtain insight into population genetics, human evolution, and diseases including cancer.
To ensure that cells function normally, tens of thousands of genes must be turned on or off together. To do this, regulatory molecules - transcription factors and non-coding RNAs – simultaneously control hundreds of genes. My group studies how the resulting gene networks function and how they can be compromised in human disease.
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.
From regulating gene expression to fighting off pathogens, biology uses DNA sequence information in many different ways. My research combines theory, computation, and experiment in an effort to better understand the quantitative relationships between DNA sequence and biological function. Much of my work is devoted to developing new methods in statistics and machine learning.
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.
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.
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 in protein coding sequences.
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 human evolution and transcriptional regulation.