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
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.
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 firstname.lastname@example.org. 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.
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: email@example.com. Apply 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.
Tumor microenvironment; intravital imaging; tumor-associated myeloid cells; breast cancer
Gene networks; gene function prediction; guilt by association; neuropsychiatric; hub genes; multifunctionality; computational genomics
Gene regulatory networks; integrated genomic analysis; bioinformatics; RNA biology; small RNAs
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.
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.
The complexity of the mammalian brain challenges our ability to explain it. My group applies methods from mathematics and theoretical physics to understand the brain. We are generating novel ideas about neural computation and brain development, including how neurons process information, how brain networks assemble during development, and how brain architecture evolved to facilitate its function.
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.