Quantitative Approaches to Cellular Information Processing and Metabolic Regulation

Abstract

Organisms of all levels of complexity must undertake complex information processing tasks. Diverse cellular and biochemical mechanisms are required to integrate multiple sources of information and to balance performance trade-offs, such as between speed and accuracy or robustness and fragility. This dissertation describes a series of quantitative analyses of cellular information processing, with particular attention given to the regulation of metabolism. Chapters 2 and 3 consider mechanisms for achieving concentration robustness in signal transduction. Chapter 2 develops a large compendium of reaction networks involving bifunctional enzymes, which are often positioned at key metabolic branch points and are experimentally associated with robust control. Using high-throughput algebraic analysis of this compendium, we demonstrate that bifunctional enzymes can implement five different forms of concentration robustness, and that the type of robustness is highly sensitive to biochemical details beyond bifunctionality. Chapter 3 identifies intermediate buffering in a three-component phospho-relay as a novel mechanism for concentration robustness and argues that such a mechanism accounts for robust inactivation of the yeast osmotic stress response. Chapter 4 reports an integrated computational and experimental analysis of production of the oncometabolite 2-hydroxyglutarate by mutant isocitrate dehydrogenase 1 (IDH1), which suggests that the clinically observed retention of a wild-type (WT) IDH1 allele in tumors is not due to a requirement for substrate channeling or substantial inter-subunit flux in WT/mutant IDH1 heterodimers. In Chapter 5 we examine the information processing capabilities of calcium/calmodulin signaling and show that a straightforward equilibrium binding analysis can clarify longstanding questions about the control of smooth muscle contraction. Finally, Chapter 6 reports an experimental approach to investigate the limits of complex information processing in single cells. Resurrecting a classical body of literature on the behavior of unicellular organisms, we demonstrate that the giant ciliate Stentor roeseli engages in multi-step hierarchical sequences of avoidance behaviors. The S. roeseli avoidance response is distinct from other primitive forms of learning such as habituation and conditioning and is suggestive of complex decision-making by the organism. Throughout the dissertation, a common theme is the use of mathematical modeling to link biochemical form to physiological function and to generate experimentally testable predictions that are independent of hard-to-measure parameter values.