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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM096193-02
Application #
8118615
Study Section
Special Emphasis Panel (ZGM1-CBCB-5 (BM))
Program Officer
Brazhnik, Paul
Project Start
2010-08-01
Project End
2015-07-31
Budget Start
2011-08-01
Budget End
2012-07-31
Support Year
2
Fiscal Year
2011
Total Cost
$217,762
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
082359691
City
Cambridge
State
MA
Country
United States
Zip Code
02138
Shakya, Holly B; Stafford, Derek; Hughes, D Alex et al. (2017) Exploiting social influence to magnify population-level behaviour change in maternal and child health: study protocol for a randomised controlled trial of network targeting algorithms in rural Honduras. BMJ Open 7:e012996
Franks, Alexander; Airoldi, Edoardo; Slavov, Nikolai (2017) Post-transcriptional regulation across human tissues. PLoS Comput Biol 13:e1005535
Klionsky, Daniel J (see original citation for additional authors) (2016) Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition). Autophagy 12:1-222
Zhou, Xu; Blocker, Alexander W; Airoldi, Edoardo M et al. (2016) A computational approach to map nucleosome positions and alternative chromatin states with base pair resolution. Elife 5:
Airoldi, Edoardo M; Miller, Darach; Athanasiadou, Rodoniki et al. (2016) Steady-state and dynamic gene expression programs in Saccharomyces cerevisiae in response to variation in environmental nitrogen. Mol Biol Cell 27:1383-96
Solís, Eric J; Pandey, Jai P; Zheng, Xu et al. (2016) Defining the Essential Function of Yeast Hsf1 Reveals a Compact Transcriptional Program for Maintaining Eukaryotic Proteostasis. Mol Cell 63:60-71
Csárdi, Gábor; Franks, Alexander; Choi, David S et al. (2015) Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast. PLoS Genet 11:e1005206
Wallace, Edward W J; Kear-Scott, Jamie L; Pilipenko, Evgeny V et al. (2015) Reversible, Specific, Active Aggregates of Endogenous Proteins Assemble upon Heat Stress. Cell 162:1286-98
Katz, Yarden; Wang, Eric T; Silterra, Jacob et al. (2015) Quantitative visualization of alternative exon expression from RNA-seq data. Bioinformatics 31:2400-2
Airoldi, Edoardo M; Toulis, Panos (2015) Scalable estimation strategies based on stochastic approximations: Classical results and new insights. Stat Comput 25:781-795

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