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An Integrative Multi-Level Systems Biology Approach to Understanding Enterobacterial Responses to Oxygen


Escherichia coli is able to grow aerobically with oxygen as terminal electron acceptor or anaerobically with numerous alternative oxidants and adapts rapidly to changes in oxygen availability, allowing survival in diverse environments (the gut, water, industrial bioprocesses). Reductionist approaches have been highly successful in characterising the numerous components of the respiratory apparatus (low potential dehydrogenases, quinones, high potential oxidases and terminal reductases) and unravelling mechanisms of gene regulation that underpin respiratory adaptation. However, a holistic understanding of the responses to oxygen, incorporating post-genomic and modeling approaches, is lacking. <P>
We seek to describe via a transnational collaborative and interactive approach how functional network modules emerge from molecular interactions, how cellular behaviour emerges from the interplay of network modules, and how population behaviour emerges from the behaviour of a single cell.<P>
In Sheffield, we will obtain new internally consistent and quantitative time-resolved transcriptomic, proteomic, metabolomic and biochemical data sets under highly controlled growth conditions in a chemostat and investigate the consequences of mutations in key genes. <P>
In parallel, we will contribute to a multi-level mathematical modelling effort. We will develop a simple mathematical model of E. coli respiratory adaptation and enrich this model by incorporating new data. We will use agent-based models, in which each component of the system will be represented as an active agent that behaves according to a set of rules that are triggered by the conditions experienced at any moment in time. <P>
Our approach permits interoperability and data sharing with other model frameworks. We will integrate new data from our project partners in the Netherlands and Germany, as well as different hierarchical levels of agent-based, kinetic and reduced-order models.

University of Sheffield
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