Systems Approaches to Reverse Engineering Stochastic Biological Oscillators

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In mammals, environmental cues such as light and food are processed by different tissues in the body. The SCN (suprachiasmatic nucleus) serves as the central pacemaker, coordinating activity rhythms for the entire body. Peripheral tissues, such as the liver or adipose tissue, also contain cellular clocks which help translate circadian phase into metabolic regimes. Our project seeks to better understand the connections between circadian rhythms and metabolic cycles. With a better understanding of peripheral clock oscillators, we may be able to develop behavioral or pharmacological therapies to mitigate negative physiological effects resulting from out-of-phase light or food.

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Principles extracted from biological networks have broad ramifications in engineered network design. We study mechanisms of robustness of biological networks in their abilities both to maintain operation when isolated from their natural environments and to respond appropriately to cues within their natural environments. An important example is the circadian clock in mammals. Despite high levels of stochasticity in sub-processes, intra- and inter-cellular signaling give rise to a reliable process. To understand these non-intuitive properties, we develop new sensitivity measures and stochastic simulation methods. These allow us to study the response of a process to noise inherent in the system as well as to external assaults. These also give us guidance on the reverse engineering of synchrony strategies for wireless sensor networks. The framework for stochastic simulation, including spatial stochastic and rare events, addresses a problem that is at the core of many systems biology computations.

University: 

UCSB

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