The studies in Aim 4 represent stretch goals for the LSP in which we attempt to advance therapeutic discovery by mining electronic medical records (EMRs) using machine learning and causal reasoning systems, dissect the druggable process of cell-to-cell communication in inflammation and fibrosis, and use structure- guided discovery to identify new compounds regulating the SHP2 phosphatase. These are studies that link together investigators who are experts in their respective fields, but who have not previously worked together on these topics;the sub-aims therefore have less project-specific preliminary data than those in Aims 1-3. However, one of the key goals of the LSP is to bring systems pharmacology approaches to new areas of research, and this Aim fulfills that goal as well as addressing areas of weakness identified in the A0 review.
AIM 4. 1 will join HMS systems biologists Chen, Marks and Sorger and BWH clinician Loscalzo in using machine learning and causal reasoning systems to identify key regulatory networks in Asthma. Using data in the large AsthmaBRIDGE clinical database, we aim to subdivide heterogeneous patient populations by molecular subtypes to improve disease management, identify opportunities for drug repurposing and discover new targets that could be advanced into clinical development.
Aim 4. 1.1 will apply machine learning methods with input from the OpenBEL Pulmonary knowledge base to identify relationships between patient drug response and clinical and molecular phenotype.
Aim 4. 1.2 will apply reverse causal reasoning to clinical data to generate candidate lists of important networks and potential targets.
Aim 4. 1.3 will use patient-derived B- cell and alveolar cell culture models to test hypotheses derived from analysis of AsthmaBRIDGE.
AIM 4. 2 will join Broad genomicist Hacohen, systems biologists Sorger and Yaffe, and clinician Loscalzo in an analysis of the molecular basis of inflammatory and fibrotic disease.
Aim 4. 2.1 will take a systems approach to inflammation, with a focus on innate immunity. This is an area with many potentially druggable targets (e.g. kinases and cell surface receptors) but in which poor understanding of networks makes it difficult to match targets to indications.
Aim 4. 2.2 will tackle the related problem of fibrotic disease, with a key advance being a new approach to disease definition based on network state rather than end-stage phenotype.
AIM 4. 3 will join structural biologist Blacklow, chemist Gray and systems biologists Sorger and Lauffenburger in an attempt to identify allosteric modulators of the SHP2 protein phosphatase;such molecules would represent a new approach to a classically undruggable target.

Agency
National Institute of Health (NIH)
Type
Specialized Center (P50)
Project #
1P50GM107618-01A1
Application #
8769538
Study Section
Special Emphasis Panel (ZGM1)
Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Harvard Medical School
Department
Type
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
02115
AlQuraishi, Mohammed; Koytiger, Grigoriy; Jenney, Anne et al. (2014) A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks. Nat Genet 46:1363-71