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Metabolic genes for lactose usage in e coli the

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Metabolic genes for lactose usage in E. coli - the hydrogen atom of gene regulation.
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Can We Compute How Cells Decide? The level of gene expression is described by a function that depends upon parameters such as the number of repressors and activators. Key point: Systematic variation of parameters and examine the biological outcome. We are interested in the “fold-change” when parameters are tuned. The equations are falsifiable predictions for a wide variety of regulatory architectures.
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Statistical Mechanics to Estimate the Level of Gene Expression The real estate in the vicinity of the promoter is under the control of molecular bouncers: activators and repressors. ``Thermodynamic models’’ (Ackers & Shea, Buchler, Gerland & Hwa, Vilar & Leibler, etc.) permit us to compute the probability of promoter occupancy as a function of the many parameters that can be controlled quantitatively . Do such models make sense?
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Cells Decide: Where to Go (Berman et al.) The Hunters of the Immune Response There is another kind of rapid response to environmental cues that is much faster than gene regulation. The “decision” about where to go next is highly regulated and results in the synthesis of new cytoskeletal filaments at the leading edge of the cell. Once again, there is an interesting random walk story behind the scenes.
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Conclusions Strongly held opinion: quantitative data demands quantitative models. The traditional verbal and cartoon approach is incommensurate with the data. We are in the stage when often stick in the sand estimates or simple back of the envelope calculations suffice to provide the kinds of polarizing predictions referred to in the quote from Rayleigh. A Caltech qualifier story: “If you don’t know what to ask them, ask them about diffusion (i.e. random walks).” – John Hopfield – Random walks have great biological reach. This talk: use of random walks in thinking about cellular decision making.
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