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Simons Center for Quantitative Biology

simons graphs intropictThe Simons Center for Quantitative Biology is dedicated to the development of new mathematical tools and techniques for the analysis of biological data. Researchers at the Center work on a wide variety of applications, including autism, cancer, neuroscience, plant biology, gene regulation and evolution.

Research at the Simons Center for Quantitative Biology is funded by a major donation from the Simons Foundation as well as gifts from the Starr Foundation and Lavinia and Landon Clay.

External Advisory Committee

Quantitative Biology Fellows Program

graph abouttab topAs technologies for data generation have become steadily more efficient and inexpensive, the interpretation of vast quantities of biological data has emerged as a rate-limiting step in advances in the biological sciences. This challenge cuts across research areas, from genomics, neuroscience, and human diseases to the plant sciences. Making sense of the “big data” that is now ubiquitous in biology requires the development of innovative new quantitative tools and techniques, grounded in classical theory yet adapted for powerful modern technologies. The central idea behind the Simons Center for Quantitative Biology is to place researchers trained in mathematics, physics, computer science, and other quantitative fields on the front lines in biology, working shoulder to shoulder with experimentalists. In addition to collaborative work, SCQB researchers pursue independent research in algorithms, machine learning, statistical genetics, evolution, and other areas. The ultimate goal of the Center is to promote interdisciplinary approaches that can break new ground on problems of central importance in both fundamental biology and applications in human health, agriculture, and the environment.


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siepel RGB thumbI’d like to welcome you warmly to the Simons Center for Quantitative Biology (SCQB), Cold Spring Harbor Laboratory's home for mathematical, computational, and theoretical research in biology. The SCQB is a blend of old and new: it extends a long history of quantitative research at CSHL, yet leverages new technologies and ideas from the quantitative sciences to enable groundbreaking research across a wide variety of biological domains, including human genetics, cancer, plant biology, and neuroscience. Members of the SCQB maintain close collaborative ties across CSHL as well as with many other groups in the New York area, including Computer Science and Applied Mathematics at Stony Brook University and the New York Genome Center. Enabled by generous donations from the Simons Foundation and other sources, the SCQB is currently undergoing a major expansion, with several new hires of faculty and staff. I am deeply honored by the opportunity to lead this unique center of excellence in quantitative biology, embedded in the world-class research environment of Cold Spring Harbor Laboratory.

Adam Siepel, Chair
February, 2015


qb welcome collage

Researchers at Cold Spring Harbor Laboratory have long been interested in the use of quantitative methods in genetics, biophysics, neuroscience and other areas, but for most of the history of the Laboratory, quantitative biology was not considered a distinct area of focus. These circumstances changed with the creation of a Center for Quantitative Biology in 2008. Not long afterward, the Center was renamed the Simons Center for Quantitative Biology in recognition of generous donations from the Simons Foundation. The Simons Foundation made additional donations in 2014, enabling further growth of the Center.

During the long history of the Laboratory, a number of prominent quantitative biologists have been associated with CSHL, either as permanent staff members or as summer visitors. Examples include Charles Davenport, Sewall Wright, Max Delbruck, Bruce Wallace, and Claude Shannon. Additional details can be found in this Short History of Quantitative Biology at Cold Spring Harbor Laboratory.

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(left to right) Sewall Wright, Claude Shannon, Max Delbruck and Salvador Luria, and the modern Hillside Campus at CSHL, which is home to the Simons Center for Quantitative Biology


qb history collage

Core, L. J., Martins, A. L., Danko, C. G., Waters, C. T., Siepel, A., Lis, J. T. (2014) Analysis of nascent RNA identifies a unified architecture of initiation regions at mammalian promoters and enhancers. Nat Genet, 46(12) pp. 1311-20.

Gulko, B., Hubisz, M. J., Gronau, I., Siepel, A. (2015) A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat Genet, 47, 276-83.

Iossifov I, O'Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, Stessman HA, Witherspoon KT, Vives L, Patterson KE, Smith JD, Paeper B, Nickerson DA, Dea J, Dong S, Gonzalez LE, Mandell JD, Mane SM, Murtha MT, Sullivan CA, Walker MF, Waqar Z, Wei L, Willsey AJ, Yamrom B, Lee YH, Grabowska E, Dalkic E, Wang Z, Marks S, Andrews P, Leotta A, Kendall J, Hakker I, Rosenbaum J, Ma B, Rodgers L, Troge J, Narzisi G, Yoon S, Schatz MC, Ye K, McCombie WR, Shendure J, Eichler EE, State MW, Wigler M. (2014) The contribution of de novo coding mutations to autism spectrum disorder. Nature.Nov13;515(7526):216-21.

Kinney JB, Atwal GS. (2014) Equitability, mutual information, and the maximal information coefficient. Proc Natl Acad Sci U S A. Mar 4;111(9):3354-9.

Kinney JB, Atwal GS. (2014) Parametric inference in the large data limit using maximally informative models. Neural Comput.  Apr;26(4):637-53.

Krasnitz A, Sun G, Andrews P, Wigler M. (2013) Target inference from collections of genomic intervals. Proc Natl Acad Sci U S A. Jun 18;110(25):E2271-8.

Levy D, Wigler M.(2014) Facilitated sequence counting and assembly by template mutagenesis. Proc Natl Acad Sci U S A. Oct 28;111(43):E4632-7.

Narzisi, G, O'Rawe, JA, Iossifov, I, Fang, H, Lee, YH, Wang, Z, Wu, Y, Lyon, G, Wigler, M, Schatz MC (2014) Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Nature Methods, 11:1033-36

Rasmussen, M. D. and Hubisz, M. J. and Gronau, I. and Siepel, A. (2014) Genome-wide inference of ancestral recombination graphs. PLoS Genetics, 10(5) pp. e1004342.

Schatz MC, Maron, LG, Stein, JC, Wences, AH, Gurtowski, J, Biggers, E, Lee, H, Kramer, M, Antoniou, E, Ghiban, E, Wright, MH, Chia, JM, Ware, D, McCouch, S, McCombie, WR. Whole genome de novo assemblies of three divergent strains of rice (O. sativa) documents novel gene space of aus and indica. (2014) Genome Biology 15:506

Wei Y, Koulakov AA. Long-term memory stabilized by noise-induced rehearsal. (2014) J Neurosci. Nov 19;34(47):15804-15.

Game of Genomes, Episode Eleven: The Neanderthal Inside (by Carl Zimmer)
July 26, 2016

Neanderthals mated with modern humans much earlier than previously thought, study finds
February 17, 2016

Genetic analysis supports prediction that spontaneous rare mutations cause half of autism
September 22, 2015

The biggest beast in the Big Data forest? One field's astonishing growth is, well, 'genomical'!
July 7, 2015

CSHL Quantitative Biologist Michael Schatz awarded 2015 Sloan Foundation Research Fellow
February 23, 2015

Harnessing data from Nature's great evolutionary experiment
January 21, 2015

Re-learning how to read a genome
November 10, 2014

New study casts sharpest light yet on genetic mysteries of autism
October 29, 2014

A shift in the code: new method reveals hidden genetic landscape
August 18, 2014

CSHL quantitative biologist Michael Schatz wins prestigious NSF Early CAREER Award
July 29, 2014

CSHL receives $50 million to establish Simons Center for Quantitative Biology
July 7, 2014

Researchers propose new way to make sense of 'Big Data'
February 15, 2014

Dr. Adam Siepel, Cornell University - Similarity in Primate DNA
August 26, 2013

Analysis of 26 networked autism genes suggests functional role in the cerebellum
July 17, 2013

Mathematical technique de-clutters cancer-cell data, revealing tumor evolution, treatment leads
June 5, 2013

Genome sequencing's big fix
Harbor Transcript, Spring 2012

An error-eliminating fix overcomes big problem in '3rd-gen' genome sequencing
June 29, 2012

A striking link is found between the Fragile-X gene and mutations that cause autism
April 25, 2012

Autism study validates importance of spontaneous casual mutations and sheds new light on gender skew
June 8, 2011

Fellow positions

Quantitative Biology Fellow. CSHL is accepting nominations for its SCQB Fellows Program.  This program is designed for exceptional scientists who have recently received their Ph.D. or M.D. degree and who are sufficiently talented and experienced to forgo standard postdoctoral training to move directly into this semi-independent research position.

There is no deadline. Nominations and Applications are accepted throughout the year.

Interested candidates are encouraged to inquire with Dr. Adam Siepel, Ph.D., Professor and Chair at  Please send a CV with your email.

The full application will include a CV, Research Statement, Letter of Nomination from Ph.D. Advisor, and the Names of Two Other References.


Postdoctoral positions

Computational Postdocs (Iossifov Lab).  Two positions available. Position 1: Study genetic variants causing autism through whole-exome & whole-genome sequencing datasets over large collection of families. Position 2: Develop flexible tools for information extraction from the bio-medical literature of diverse relations, such as those between a gene and a disease, connections between brain regions, and protein-to-protein interactions. In addition, develop methods for integrating the extracted knowledge with experimental datasets to improve our ability to make inferences and generate novel hypothesis. For more information email Dr. Iossifov: iossifov@cshl.eduApply Now

Computational Postdoc (Atwal Lab). The Atwal Lab is looking for quantitative/computational postdoctoral fellows to work at the exciting interface of cancer genomics, immunology, and mathematics. Our lab blends computation and theory in close collaboration with experimentalists and clinicians, developing machine learning approaches and statistical models of next-generation sequencing data. Current projects include quantitative models of the tumor microenvironment, the adaptive immune repertoire, and tumor evolution. For more information email Dr. Atwal: Apply Now

Postdoctoral Fellow (Kinney Lab).  The Kinney Lab is looking to hire a talented and motivated Postdoctoral Fellow to participate in one of a variety of mathematical, computational, and experimental projects focused on using ultra-high-throughput DNA sequencing to measure and model quantitative sequence-function relationships. Applicants with PhD-level experience in physics, computer science, statistics, machine learning, molecular biology, and/or biochemistry are encouraged to apply. For more information email Dr. Kinney: jkinney@cshl.eduApply Now

Computational Postdoc (Siepel Lab).  The Siepel Lab specializes in the development of probabilistic models, algorithms for inference, prediction methods, and application of these methods in large-scale genomic data analysis. Of particular interest is research relevant to existing projects, including demography inference using Bayesian coalescent-based methods, inference of natural selection on regulatory and other noncoding sequences, and prediction of fitness consequences for noncoding mutations. For more information email Dr. Siepel: asiepel@cshl.eduApply Now

CSHL is an EO/AA Employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, national origin, age, disability or protected veteran status.

VEVRAA Federal Contractor
Gurinder Atwal - Associate Professor

Applies insights from the physical and computational sciences to the study of population genetics, evolution, and disease. Recent work on the evolution of genetic variants identified a role of p53 tumor suppressor in female fertility. The lab also analyzes comparative genomics and physical organization of cancer related genes, and their role in mediating tumorigenesis across numerous tissue types. 
Ivan Iossifov - Assistant Professor

Applies computational methods to improve conventional genetic analyses to detect correlations between specific alleles and common complex hereditary disorders such as schizophrenia, bipolar disorder and autism.
Justin Kinney - Assistant Professor

Combines theory, computation, and experiment to quantitatively define relationships between sequence and function in molecular biology. Current research focuses on developing next-generation sequencing as a tool for dissecting the biophysical basis of transcriptional regulation.
Alexei Koulakov - Professor

Applies methods from theoretical physics, machine learning, and mathematics to study how neurons process information, how brain networks assemble during development, and how brain architecture evolved to facilitate its function. His work has generated robust theoretical models of visual and olfactory neural circuits.
Alexander Krasnitz - Associate Professor

Develops and applies statistical methods to understand how cancers evolve. His lab has designed a novel, comprehensive methodology to discover recurrent genomic aberrations in cancer genomes and has used it to analyze multiple data sets in breast, liver, ovarian, and prostate cancer. More recently, he used his computational tools to reveal how genomically distinct cell populations evolve in individual malignancies.
Dan Levy - Assistant Professor

Develops algorithms to identify mutations associated with various diseases, including cancers and autism, from large, complex data sets. His work focuses on using targeted sequence data to identify copy number variants and multiscale genomic rearrangements, including most recently analysis of data obtained from single cells.
David McCandlish - Assistant Professor

Develops computational and mathematical tools to interpret data from high-throughput experiments. His work is currently focused on predicting the effects of mutations in protein coding sequences and integrating high-throughput measurements into models of molecular evolution.
Partha Mitra - Professor

Combines theoretical, computational and experimental approaches to study complex biological systems, focusing on neurobiological questions; also a lead organizer of the Brain Architecture Project, a collaborative effort to produce a comprehensive draft of the connectivity patterns of the human brain.
Michael Schatz - Adjunct Associate Professor

Adam Siepel - Professor

Uses mathematical analysis and computer science to study evolution of populations, species, and individual genes. Employs these methods to understand transcriptional regulation and evolution.