Simons Center for Quantitative Biology
The 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.
As 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.
I’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
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.
(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
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.
Genetic analysis supports prediction that spontaneous rare mutations cause half of autism
September 22, 2015
Harnessing data from Nature's great evolutionary experiment
Re-learning how to read a genome
New study casts sharpest light yet on genetic mysteries of autism
A shift in the code: new method reveals hidden genetic landscape
Researchers propose new way to make sense of 'Big Data'
Dr. Adam Siepel, Cornell University - Similarity in Primate DNA
Genome sequencing's big fix
Fellow positionsQuantitative 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 email@example.com. 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 positionsComputational 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: firstname.lastname@example.org. Apply Now
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: email@example.com. 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: firstname.lastname@example.org. Apply Now
Computational Postdoc (Krasnitz Lab). Available immediately for the study of cancer genomics; joint appointment with the CSHL Cancer Center. Research will focus on in-depth genomic and transcriptional study of pancreatic cancer, with the ultimate goal of improving patient outcomes via individualized therapies. The computational component of the project involves analysis and interpretation of massive sequencing data to be generated from organoid cultures alongside with high-throughput drug screening. For more information email Dr. Krasnitz: email@example.com. Apply 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: firstname.lastname@example.org. Apply Now
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