A key to understanding complex biological systems such as cancer is to understand how networks of molecular interactions function in normal cells, and how they becomeperturbed in disease states. This endeavor demands sophisticated computational tools to derive and analyze molecular regulatory networks. Such tools need to integrate multiple types of data from high-throughput experiments to derive specific predictions - a systems biology approach. In this proposal we describe the development of novel machine learning methods to infer regulatory networks, that integrate known biology (prior information) with multiple types of high-throughput data. Our methods for inferring regulatory networks will be a key component of each biological project, and there will be an iterative feedback between the experimental and computational components to refine models and suggest new rounds of experiments. We will also develop computationally executable biological models that allow us to pose hypothetical """"""""what if"""""""" questions to predict how a system will respond if we perturb it in a specific way, such as a therapeutic intervention. Phenotypic responses in cells can often be attained via more than one route, which is particularly problematic in cancer. A therapeutic regime may target a particular weakness in tumor cells, only for them to acquire additional mutations that elude the drug intervention by employing alternative pathways to achieve the same outcomes of survival and proliferation. We will develop multiscale models of cancer that estimate cellular level dynamics from tumor growth kinetics. Systems biology has benefited from utilizing methods originally developed in other disciplines for very different reasons. Major contributions have been made by applying methodologies from mathematics, statistics, computer science, engineering, and physics to biological questions. We plan to extend this interdisciplinary reach by adopting and developing ideas from the mathematics of geometry and topology in the identification of underlying patterns of progression in cancer, and to the analysis of large molecular networks.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA149145-05
Application #
8628776
Study Section
Special Emphasis Panel (ZCA1-SRLB-C)
Project Start
Project End
Budget Start
2014-03-01
Budget End
2015-02-28
Support Year
5
Fiscal Year
2014
Total Cost
$381,091
Indirect Cost
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Angst, Martin S; Fragiadakis, Gabriela K; Gaudillière, Brice et al. (2016) In Reply. Anesthesiology 124:1414-5
Li, Yulin; Deutzmann, Anja; Choi, Peter S et al. (2016) BIM mediates oncogene inactivation-induced apoptosis in multiple transgenic mouse models of acute lymphoblastic leukemia. Oncotarget 7:26926-34
Aghaeepour, Nima; Chattopadhyay, Pratip; Chikina, Maria et al. (2016) A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A 89:16-21
Li, Yang I; van de Geijn, Bryce; Raj, Anil et al. (2016) RNA splicing is a primary link between genetic variation and disease. Science 352:600-4
Behbehani, Gregory K; Samusik, Nikolay; Bjornson, Zach B et al. (2015) Mass Cytometric Functional Profiling of Acute Myeloid Leukemia Defines Cell-Cycle and Immunophenotypic Properties That Correlate with Known Responses to Therapy. Cancer Discov 5:988-1003
Gentles, Andrew J; Newman, Aaron M; Liu, Chih Long et al. (2015) The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med 21:938-45
O'Gorman, William E; Hsieh, Elena W Y; Savig, Erica S et al. (2015) Single-cell systems-level analysis of human Toll-like receptor activation defines a chemokine signature in patients with systemic lupus erythematosus. J Allergy Clin Immunol 136:1326-36
Jung, Namyoung; Dai, Bo; Gentles, Andrew J et al. (2015) An LSC epigenetic signature is largely mutation independent and implicates the HOXA cluster in AML pathogenesis. Nat Commun 6:8489
Zunder, Eli R; Lujan, Ernesto; Goltsev, Yury et al. (2015) A continuous molecular roadmap to iPSC reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell 16:323-37
Goodson 3rd, William H; Lowe, Leroy; Carpenter, David O et al. (2015) Assessing the carcinogenic potential of low-dose exposures to chemical mixtures in the environment: the challenge ahead. Carcinogenesis 36 Suppl 1:S254-96

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