Excerpt

Biological substrates for computation have been considered since before the advent of modern deterministic computers (McCulloch and Pitts 1943; von Neumann 1956; Bennett 1982). Technological advances in measuring cellular responses to molecular signals have again raised the question of how stochastic networks compute.

Signaling pathways enable living cells to process responses to stimuli from the extracellular environment. The uncertainty of signal transmission in a single cell has prompted various research efforts to quantify how much a cell knows about its environment. Advances in nonequilibrium thermodynamics have arrived alongside analyses of biological signaling. Often, models of signaling that consider only the time scale of molecular fluctuations have been considered (see, among others, Cheong et al. 2011; Barato, Hartich, and Seifert 2014; Govern and ten Wolde 2014; Bo, Del Giudice, and Celani 2015; Hartich, Barato, and Seifert 2016), especially in relation to the bacterial chemotactic response (Lan et al. 2012).

We suggest that an important time scale for biological signaling should be on the order of gene expression (in the case of bacteria, potentially multiple generations). Growing cells invest energy to grow and divide, thereby diluting the results of previous computations. Because the remnants of previous responses are reduced but not necessarily completely erased, gradual dilution imparts a memory effect: a daughter cell is predisposed to respond in a qualitatively similar manner to its mother cell. Quantifying thermodynamic costs of molecular receptor signaling on short time scales reveals much about the extreme limits of the biological cost of computation, but such energy use is ultimately minor compared to the massive costs of gene expression that can arise as a result of such a signal. Here we seek to explore the effects of those costs on cellular information processing.

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