Cells distinguish a large number of internal and external states to which they respond in a context- dependent and history-biased manner affecting fundamental processes such as division, repair and cell death. To reconcile the potentially large number of internal and external states worth distinguishing with the comparably small pool of components from which most intracellular signaling systems are assembled, requires considerable network plasticity which is made possible through the sharing of components or their reuse in multiple complexes. I will explore by means of models whether this architecture, suggested by empirical studies, is sufficient for enabling signals to actually induce and shape the networks that end up processing them. I envision this to occur by a dynamics of competitive recruiting of shared signaling components, leading to autocatalytic feedbacks that lock in a """"""""winner network"""""""". Such a scenario is in marked contrast to a view in which pre-configured networks stand ready to process signals to which they are dedicated. I believe it is important to understand signaling dynamics in the context of a feedback loop with gene expression dynamics. Signaling networks control the expression of genes, which control the protein levels in these very signaling networks. This overall feedback comprises slow and fast time scales (gene expression and signaling, respectively). Such separation of time scales may underlie simple forms of intracellular learning and memory. Extensive pleiotropy of molecular components conveys advantages of plasticity, but may also limit the accuracy by which proteins recognize each other. Reduced protein recognition specificity causes network error, that is, fluctuations in the network structure itself. I will develop an understanding of how errors in protein-protein recognition affect cellular responses. I will accomplish the proposed aims through the mathematical and numerical investigation of simple models that capture component reuse, complex formation and protein-protein recognition. My proposal falls strongly within the mission of the NIH, since the normal physiology of tissues and many of their systemic pathologies hinges on the response of cells to a variety of growth factors, cytokines, hormones and other primary signals. One of the greatest challenges underlying the understanding and treatment of many human diseases is obtaining a fundamental picture of how cells react to their environment. As our detailed knowledge of the inner workings of cellular signal processing increases, such an understanding will allow us to develop more and more effective strategies for combating and curing diseases by providing a clear portrait of what goes wrong in particular diseases and how human intervention can overcome and circumvent such errors. ? ? ?
| Deeds, Eric J; Krivine, Jean; Feret, Jérôme et al. (2012) Combinatorial complexity and compositional drift in protein interaction networks. PLoS One 7:e32032 |
| Savage, Van M; Deeds, Eric J; Fontana, Walter (2008) Sizing up allometric scaling theory. PLoS Comput Biol 4:e1000171 |
