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.
The big problem of small data: A new approach
October 18, 2018
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 new way to analyze small...
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...
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.
CSHL is searching at the Assistant Professor level for highly talented individuals to join its Simons Center for Quantitative Biology (SCQB). Specific research areas of interest include, but are not limited to, modeling and analysis of transcriptional regulation and epigenomics, sequence assembly and variant calling for emerging technologies, evolutionary and population genomics, single-cell analysis, and cancer genomics. Successful candidates will have an outstanding record of research achievement and the ability to attract significant extramural research support. This position is for candidates focused on dry-lab research (experimental space is limited).
Send a Cover Letter, CV, Description of Research Accomplishments and Future Research Plans; and Three Letters of Reference in PDF format to QBjobs2018@cshl.edu with subject line QB Faculty Search. Applications will be accepted until November 1, 2018 and reviewed on a rolling basis.
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: firstname.lastname@example.org. 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.
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.