Environmental statistics and optimal regulation

Any organism is embedded in an environment that changes over time. The timescale for and statistics of environmental change, the precision with which the organism 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 that organizes known regulatory strategies and may help conceptualize heretofore unknown ones.Comment: 21 pages, 7 figure

Allocating and splitting free energy to maximize molecular machine flux

Biomolecular machines transduce between different forms of energy. These machines make directed progress and increase their speed by consuming free energy, typically in the form of nonequilibrium chemical concentrations. Machine dynamics are often modeled by transitions between a set of discrete metastable conformational states. In general, the free energy change associated with each transition can increase the forward rate constant, decrease the reverse rate constant, or both. In contrast to previous optimizations, we find that in general flux is neither maximized by devoting all free energy changes to increasing forward rate constants nor by solely decreasing reverse rate constants. Instead the optimal free energy splitting depends on the detailed dynamics. Extending our analysis to machines with vulnerable states (from which they can break down), in the strong driving corresponding to in vivo cellular conditions, processivity is maximized by reducing the occupation of the vulnerable state.Comment: 22 pages, 7 figure

Has motorization in the U.S. peaked? Part 6: relationship between road transportation and economic activity

In the previous five reports in this series, I examined recent changes in the number of registered light-duty vehicles (cars, SUVs, pickups, and vans), and the corresponding changes in distance driven and fuel consumed. The units of the analyses were both the absolute numbers and the rates per person, per driver, per household, and (where appropriate) per vehicle. The main finding of those reports was that the respective rates all reached their maxima around 2004. I argued that, because the onsets of the reductions in these rates preceded the onset of the recession (in 2008), the reductions in these rates likely reflect fundamental, noneconomic changes in society. Therefore, these maxima have a reasonable chance of being long-term peaks as well. The present report examines the relationship between road transportation and economic activity since the end of the Second World War. The two measures of interest were distance driven by all vehicles per inflation-adjusted GDP and fuel consumed by all vehicles per inflation-adjusted GDP. The main finding is that distance driven per GDP reached its highest values in a broad plateau from the early 1970s through the early 1990s, and then decreased steadily. By 2012, the value of this measure decreased by 22% from its absolute maximum, which was reached in 1977. Some of the factors that likely contributed to the recent decline in the value of this measure are the decreased amount of personal transportation, decreased contribution to GDP of truck transportation, and the increased contribution to GDP of data services, information processing, and e-commerce. The amount of fuel consumed per GDP peaked in the early 1970s, and then decreased by 47% by 2012. The relatively steep decline in the value of this measure reflects the added contribution of the improvement in vehicle fuel economy from the 1970s on.The University of Michigan Sustainable Worldwide Transportationhttp://deepblue.lib.umich.edu/bitstream/2027.42/110116/1/103145.pd

Time step rescaling recovers continuous-time dynamical properties for discrete-time Langevin integration of nonequilibrium systems

When simulating molecular systems using deterministic equations of motion (e.g., Newtonian dynamics), such equations are generally numerically integrated according to a well-developed set of algorithms that share commonly agreed-upon desirable properties. However, for stochastic equations of motion (e.g., Langevin dynamics), there is still broad disagreement over which integration algorithms are most appropriate. While multiple desiderata have been proposed throughout the literature, consensus on which criteria are important is absent, and no published integration scheme satisfies all desiderata simultaneously. Additional nontrivial complications stem from simulating systems driven out of equilibrium using existing stochastic integration schemes in conjunction with recently-developed nonequilibrium fluctuation theorems. Here, we examine a family of discrete time integration schemes for Langevin dynamics, assessing how each member satisfies a variety of desiderata that have been enumerated in prior efforts to construct suitable Langevin integrators. We show that the incorporation of a novel time step rescaling in the deterministic updates of position and velocity can correct a number of dynamical defects in these integrators. Finally, we identify a particular splitting that has essentially universally appropriate properties for the simulation of Langevin dynamics for molecular systems in equilibrium, nonequilibrium, and path sampling contexts.Comment: 15 pages, 2 figures, and 2 table