URLanism is embedded in an environment (specifying for example the concentrations of basic nutrients) that changes over time. The timescale for and statistics of environmental change, the precision with which URLanism can detect its environment, and the costs and benefits of particular protein expression levels all will affect the suitability of different strategies (such as constitutive expression or graded response) for regulating protein levels in response to environmental inputs. We propose a general framework (here specifically applied to the enzymatic regulation of metabolism ) to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, respectively, and the costs associated with enzyme production. We use this framework to address three fundamental questions: (i) when a cell should prefer thresholding to a graded response; (ii) when there is a fitness advantage to implementing Bayesian inference ; and (iii) when retaining memory of the past provides a selective advantage. We specifically find that: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for sophisticated Bayesian inference ; and (iii) in dynamical contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme URLanizes known regulatory strategies and may help conceptualize heretofore unknown ones.
URLanism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which URLanism can detect its environment, and the costs and benefits of particular protein expression levels all will affect the suitability of different strategies-such as constitutive expression or graded response-for regulating protein levels in response to environmental inputs. We propose a general framework-here specifically applied to the enzymatic regulation of metabolism in response to changing concentrations of a basic nutrient-to predict the optimal regulatory strategy given the statistics of fluctuations in the environment and measurement apparatus, respectively, and the costs associated with enzyme production. We use this framework to address three fundamental questions: (i) when a cell should prefer thresholding to a graded response; (ii) when there is a fitness advantage to implementing a Bayesian decision rule ; and (iii) when retaining memory of the past provides a selective advantage. We specifically find that: (i) relative convexity of enzyme expression cost and benefit influences the fitness of thresholding or graded responses; (ii) intermediate levels of measurement uncertainty call for a sophisticated Bayesian decision rule ; and (iii) in dynamic contexts, intermediate levels of uncertainty call for retaining memory of the past. Statistical properties of the environment, such as variability and correlation times, set optimal biochemical parameters, such as thresholds and decay rates in signaling pathways. Our framework provides a theoretical basis for interpreting molecular signal processing algorithms and a classification scheme URLanizes known regulatory strategies and may help conceptualize heretofore unknown ones.
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strategies (such
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Bayesian inference
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sophisticated Bayesian inference
a sophisticated Bayesian decision rule
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