Center for Computational Biology

Simons Foundation

The Center for Computational Biology develops new and innovative methods of examining data in the biological sciences whose scale and complexity have historically resisted analysis.

CCB’s mission is to develop modeling tools and theory for understanding biological processes and to create computational frameworks that will enable the analysis of the large, complex data sets being generated by new experimental technologies.

Groups

Biophysical Modeling The Biophysical Modeling group focuses on the modeling and simulation of complex systems that arise in biology and soft condensed matter physics. Read More

Genomics An immensely complex molecular network of interactions forms the foundation of human biology and disease. Genomic approaches provide a particularly illuminating window to biological systems, and when combined with advanced analysis allow us to learn and model this complexity. Read More

Neuroscience Our goal is to understand how the brain analyzes large and complex datasets streamed by sensory organs in order to aid efforts at building artificial neural systems and treating mental illness. Read More

Numerical Algorithms The Numerical Algorithms group works on the development of new computational and statistical methods for analyzing large, complex datasets. Read More

Systems Biology At the Systems Biology group, we are developing new methods to learn the networks that run life’s program. Read More

News & Announcements

Neurons in Cerebellum Show Surprising Activity During Learning

CCB’s Olga Troyanskaya Named a Fellow of the International Society for Computational Biology

New Tool Predicts Autism Risk Genes

Research Highlights

August 7, 2017

Bistability in the synchronization of actuated microfilaments

H. Guo, L. Fauci, M. Shelley, E. Kanso

Cilia and flagella are essential building blocks for biological fluid transport and locomotion at the micron scale. They often beat…

arXiv:1708.02181

July 12, 2017

Flexibly imposing periodicity in kernel independent FMM: A Multipole-To-Local operator approach

Wen Yan, M. Shelley

An important but missing component in the application of the kernel independent fast multipole method (KIFMM) is the capability for…

arXiv

July 6, 2017

Use of imaging software for assessment of the associations among zona pellucida thickness variation, assisted hatching, and implantation of day 3 embryos

E. Lewis, R. Farhadifar , L. Farland, D. Needleman , S. Missmer , C. Racowsky

PURPOSE: The aim of this study was to determine if zona pellucida thickness variation (ZPTV) is associated with implantation and…

Journal of Assisted Reproduction and Genetics

Director

Leslie Greengard, M.D., Ph.D.

Director, CCB

Leslie Greengard joined the Simons Foundation in 2013 as founding director of the Simons Center for Data Analysis, now called the Center for Computational Biology. Prior to that, Greengard served as director of the Courant Institute of Mathematical Sciences at New York University.