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.
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.
|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|
|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|
|Franks, Alexander M; CsÃ¡rdi, GÃ¡bor; Drummond, D Allan et al. (2015) Estimating a structured covariance matrix from multi-lab measurements in high-throughput biology. J Am Stat Assoc 110:27-44|
|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|
|CsÃ¡rdi, GÃ¡bor; 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|
|Katz, Yarden; Li, Feifei; Lambert, Nicole J et al. (2014) Musashi proteins are post-transcriptional regulators of the epithelial-luminal cell state. Elife 3:e03915|
|Airoldi, Edoardo M; Costa, Thiago; Bassetti, Federico et al. (2014) Generalized species sampling priors with latent Beta reinforcements. J Am Stat Assoc 109:1466-1480|
|Slavov, Nikolai; Budnik, Bogdan A; Schwab, David et al. (2014) Constant growth rate can be supported by decreasing energy flux and increasing aerobic glycolysis. Cell Rep 7:705-14|
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