Main Projects

The HMS Laboratory of Systems Pharmacology – LSP (GM107618)

The Harvard Medical School (HMS) Laboratory of Systems Pharmacology (LSP) is a NIGMS Center for Systems Biology multi-disciplinary effort within HiTS to reinvent the fundamental science underlying the development of new medicines and their use in individual patients.

Every cell is an extensively interconnected ecosystem. Any given gene may influence dozens or hundreds of other genes. Systems Pharmacology investigates drug action at the level of entire biochemical and genetic networks, rather than looking at the effect of any single element. The LSP brings together investigators in mathematical and experimental disciplines from multiple academic institutions (Harvard, MIT, Tufts) and research hospitals (Dana Farber, MGH, BWH) and eventually visiting scientists from the FDA and local drug companies to integrate computational and systems approaches into all phases of drug discovery and development.

The HMS LINCS Center (HL127365)

The Harvard Medical School (HMS) LINCS Center is part of the NIH Library of Integrated Network-based Cellular Signatures (LINCS) Program. The overall goals of this program are to collect and disseminate data and analytical tools needed to understand how human cells respond to perturbation by drugs, the environment, and mutation.

The HMS LINCS Center aims to discover fundamental principals of cellular response to perturbation including the relationship between dose and response, the origin and significance of cell-to-cell variation, and the molecular basis of drug sensitivity and resistance. Data generated at HMS include multiplex biochemical, proteomic, and imaging assays for which dissemination standards are poorly developed; improving these and liberating all data, algorithms, and conclusions from our notebooks and papers is a key goal of the Center.

The HMS Center for Cancer Systems Pharmacology – CCSP (CA225088)

The HMS Center for Cancer Systems Pharmacology (CSP Center) is an NCI Cancer Systems Biology Consortium that constructs and applies network-level computational models to understand mechanisms of drug response, resistance and toxicity for targeted small molecule drugs and immune checkpoint inhibitors (ICIs). By systematically dissecting how resistance to targeted therapies and ICIs arises, we aim to understand and overcome resistance mechanisms using new drugs or drug combinations, while simultaneously predicting and balancing potential toxicities.

Communicating with Computers: Active Context (W911NF-15-1-0544)

The DARPA Communicating with Computers (CwC) program develops technologies for a new generation of human-machine interaction in which machines act as proactive collaborators rather than merely problem solving tools. We are developing an interactive dialogue system which allows scientists to interact with a computer partner – one that is able to harness knowledge extracted from the biomedical literature – to construct and test hypotheses about molecular systems. Listen to a podcast about our work, which appeared in The Guardian or check out the following article authored by the director of DARPA that appeared in WIRED.

World Modelers: Global Reading and Assembly for Semantic, Probabilistic World Models (W911NF-18-1-0014)

The DARPA World Modelers program aims to develop automated information collection and computational modeling techniques to understand the complex dynamics of global processes such as food security, migration and public health. We are developing the INDRA-GEM (Integrated Network and Dynamical Reasoning Assembler for Generalized Ensemble Modeling) automated model assembly system, which integrates information from diverse sources and implements novel probabilistic assembly techniques that can account for the uncertain nature of information in models.

An Automated Scientific Discovery Framework (ASDF) for Mechanistic Reasoning Across Complex Data (W911NF018-1-0124)

The DARPA Automated Scientific Discovery Framework program (ASDF) will develop algorithms and software for reasoning about complex mechanisms operating in the natural world, explaining large-scale data, assisting humans in generating actionable, model-based hypotheses and testing these hypotheses empirically.