The system comprised of the bacterium Escherichia coli and its virus, bacteriophage lambda, serves as the basic paradigm for many aspects of gene regulation, ranging in scale from the molecular to the organismic level. Questions asked within the lambda system often yield insights relevant to """"""""higher"""""""" eukaryotic systems. The lambda system has been extensively characterized using the traditional tools of genetics and biochemistry, enabling the formation of an elegant and seemingly complete narrative of the observed phenomenology in terms of the microscopic interactions in the cell. However, from the point of view of a quantitative scientist there is an immense gap of understanding between the genetic and biochemical knowledge on the one hand, and the observed population phenotype on the other. This gap manifests itself in the poor predictive powers of mathematical models of the system. To try and bridge the knowledge gap it is required to """"""""deconstruct"""""""" the life cycle of bacteriophage lambda by studying the events comprising this life cycle in real-time, in individual living cells, quantifying the intracellular dynamics with sufficient resolution to describe individual events in space and time. In this proposal we suggest to characterize gene regulation during the lambda life cycle, concentrating on the following aims: (1) Characterizing the function of the lysis/lysogeny switch during the maintenance of the lysogenic (dormant) state as well as the induction of the lytic (virulent) pathway following cell damage. (2) Elucidating spatiotemporal aspects affecting the life cycle, for example how the different stages of the lytic pathway genome replication, gene expression, capsid self-assembly and lysis are organized in space and time. (3) Distinguishing precision versus stochasticity in the lambda life cycle, by separating """"""""real"""""""" stochasticity one resulting from actual sources of uncontrolled variability from """"""""apparent"""""""" stochasticity, resulting from our own inability to detect and measure differences in physiological parameters between individual cells.

Public Health Relevance

Filling the knowledge gap at the single-event level will in turn bring us closer to a quantitative understanding of whole-system (organism) characteristics in terms of the microscopic constituents, in the vain of """"""""systems biology"""""""". Achieving this goal in a simple model system such as lambda can then be followed by similar endeavors in higher organisms.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Lyster, Peter
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Baylor College of Medicine
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