Research

Research Groups

Currently, there are six research groups at the Computational Biology Center. These groups are led by faculty members Grégoire Altan-Bonnet, John Chodera, Christina Leslie, Gunnar Rätsch, Chris Sander and Joao Xavier.

The  Altan-Bonnet Group studies the robustness and adaptability of self/non-self discrimination in the immune system through a combination of experimental and computational approaches. The group aims to understand how ligand discrimination by T cells is controlled by the dynamics of their signaling response and to provide a quantitative understanding of self/non-self discrimination by T cells to allow controlled modulation in clinical applications.

The Chodera Group uses computation and experiment to develop quantitative, multiscale models of the effects of small molecules on biomolecular macromolecules and cellular pathways. To do this, the group utilizes physical models and rigorous statistical mechanics, with overall goals of engineering novel therapeutics and tools for chemical biology, as well as understanding the physical driving forces behind the evolution of resistance mutations. The group makes use of advanced algorithms for molecular dynamics simulations on GPUs and distributed computing platforms, in addition to robot-driven moderate- and high-throughput experiments focusing on characterizing biophysical interactions between proteins and small molecules.

The Leslie Group develops machine learning algorithms to study the regulation of gene expression from a global and data-driven perspective. These algorithms “train” on diverse high-throughput molecular and genomic data to learn predictive computational models of regulatory processes. Current efforts include modeling cell-type specific transcriptional programs, deciphering microRNA-mediated gene regulation and alternative cleavage and polyadenylation, and cancer systems biology. The Leslie lab also pioneered the use of “k-mer” based string kernels for support vector machine classification of biological sequences.

The Rätsch Group is interested in modern machine learning techniques appropriate for the analysis of problems arising in genome biology. Particular areas of interest include computational transcriptomics, sequence variation and computational genome annotation. A major contribution by the Ratsch lab to the field of genomics has been the development of “mGene”, a very accurate, machine learning-based gene finding system.

The Sander Group’s primary research interests include identification of oncogenically altered pathways from genomic and molecular profiling in cancer, algorithms for the analysis of cancer genomics data, design of combinatorial cancer therapy, drug target identification, knowledge representation of biological pathways, and 3D protein structure prediction. The Sander Group leads a community effort to create an open-source information resource for biological pathways.

The Xavier Group uses quantitative experiments and develops computational models to study how social interactions govern complex behavior in cell populations. The group focuses on three systems of biomedical relevance: (1) evolutionary conflict and cooperation in microbial pathogens, (2) species interaction dynamics in the gut microbiome, and (3) cell-cell communication in cancer.

There is an active faculty recruitment effort currently underway. Any interested individuals should visit our Opportunities page.

Research Centers

CCSB at MSKCC

The Center for Cancer Systems Biology (CCSB) at MSKCC is investigating tumor cell heterogeneity, both within tumors and among patients, to gain insight needed for the development of subtype-specific and patient-specific cancer therapies. Our CCSB is one of 12 centers funded by the NCI’s Integrative Cancer Biology Program to study cancer as a complex biological system by integrating mathematical modeling with experimental biology approaches.

MSKCC Center for Translational Cancer Genomic Analysis

The MSKCC Center for Translational Cancer Genomic Analysis aims to develop novel integrative analysis methods for studying cancer genomic data, thereby enabling the translation of genomic insights into new clinical applications. As one of seven designated Genome Data Analysis Center (GDAC) for NCI’s The Cancer Genome Atlas (TCGA) program, our methods are integrated into the TCGA analytic pipelines. All data sets can be freely explored and analyzed using our cBio Cancer Genomics Portal.