A fundamental systems-level challenge for living organisms is the regulation of cellular growth in a fluctuating environment. Sudden changes in nutrient availability in the environment or the presence of stress factors and chemical compounds typically require rapid adjustments of cellular growth. Growth control and other functions in the cell are instantiated by cascades of cellular events, represented as signaling networks. We will develop statistical methods and algorithms of direct utility to biologists for quantifying regulation mechanisms and dynamics specified by a known signaling network, for refining hypothesized signaling dynamics, and for making predictions. Our integrative approach quantifies the effects of signaling networks, by combining experimental studies of mechanisms that operate at complementary levels of regulation, and takes advantage of modern high-throughput and sequencing technologies. We will experimentally validate, refine and improve the portrait of regulation and signaling dynamics driving cellular growth. Our approach is generalizable to other model organisms, from bacteria to human, and to other functions and diseases.
Aim 1. Develop theory, models, and algorithms for quantifying the effects of known signaling networks at multiple levels of regulation, and for refining signaling dynamics from coordinated experimental data. (1a) De-noising of multiple coordinated cellular responses. (1b) Identification of coordinated dynamics of binding motifs, proteins and metabolites with a significant role in carrying molecular signals on known signaling and metabolic networks. (1c) Refinement of a given signaling network from data at multiple regulation levels. (1d) Consistent estimation based on biological constants. (1e) Open source software and web tools.
Aim 2. Develop methods to predict cellular proliferation in yeast and mammalian cancer systems. (2a) Quantification of coordinated regulation dynamics by combining time-courses on nucleosome movements (solexa-seq), protein-DNA binding (ChIP-seq), gene expression (RNA-seq), protein abundance (mass-spec). (2b) Map of binding motifs that predict coordinated transcription and translation, given DNA accessibility. (2c) Prediction of cellular growth dynamics due to environmental changes (nutrients, stress and drugs) by combining coordinated time-courses at four levels of regulation: gene expression (RNA-seq), protein abundance (mass-spec), protein-DNA binding (ChIP-seq), metabolite concentrations (turbidostat). (2d) Refinement of systems-level signaling mechanisms of cellular differentiation and growth in cancer stem cells.

Public Health Relevance

Cellular growth is highly conserved from unicellular to multicellular organisms. Its disruption in higher organisms plays a role in a variety of disorders from viral infection to cancer. We provide statistical methods to characterize growth in terms of coordinated mechanisms that operate at multiple levels of regulation. This mechanistic perspective will enable the design of perturbations that can induce cellular responses of interest, including reduced speed of cellular proliferation, and desired phenotypical traits of differentiating stem cells.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Special Emphasis Panel (ZGM1-CBCB-5 (BM))
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Brazhnik, Paul
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Harvard University
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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