Responding to chemical gradients: bacterial chemotaxis

Abstract

Chemotaxis allows bacteria to follow gradients of nutrients and other environmental stimuli. The bacterium Escherichia coli performs chemotaxis via a run-and-tumble strategy in which sensitive temporal comparisons lead to a biased random walk, with longer runs in the preferred gradient direction. The chemotaxis network of E. coli has developed over the years into one of the most thoroughly studied model systems for signal transduction and behaviour, yielding general insights into such properties of cellular networks as signal amplification, signal integration, and robustness. Despite its relative simplicity, the operation of the E. coli chemotaxis network is highly refined and evolutionarily optimized at many levels. For example, recent studies revealed that the network adjusts its signaling properties dependent on the extracellular environment, apparently to optimize chemotaxis under particular conditions. The network can even utilize potentially detrimental stochastic fluctuations in protein levels and reaction rates to maximize the chemotactic performance of the population.

Fast, high-throughput measurement of collective behaviour in a bacterial population

“…This is currently explained through the cooperativity of the receptors, which react in clusters of dimers to the binding of a ligand [20][21][22][23], and of the molecules in the ring controlling the flagellar motor rotation [24,25], which can cooperatively rearrange to induce a motor switch in response to a single CheY binding event. The response is also subject to adaptation: the MCPs are desensitized by successive methylations (governed by the proteins CheR and CheB) [17], so that the activity of CheA, and hence the tumbling rate, finally adapts to a new background concentration of the chemoattractant. For methylaspartate, the molecule we used, this adaptation is perfect-the activity always resets to the same value in homogeneous environments.…”

“…The chemotactic behaviour of the model bacterium Escherichia coli is one of the best understood biological systems (see, for example, Refs. [17,18] for recent reviews). These bacteria swim in a series of straight 'runs' lasting around a second, separated by roughly 0.1 s 'tumble' events.…”

Thermodynamics of Statistical Inference by Cells

“…Indeed, we will show that thermodynamics places fundamental constraints on the ability of biochemical networks to perform statistical inference. More generally, statistical inference is intimately tied to the manipulation of information and hence offers a rich setting to study the relationship between information and thermodynamics [15][16][17][18][19].In order for a cell to formulate an appropriate response to an environmental signal, it must first estimate the concentration of an external signaling molecule using membrane bound receptors [1][2][3][4][5][6]20]. The biophysics and biochemistry of cellular receptors is highly variable.…”

“…In order for a cell to formulate an appropriate response to an environmental signal, it must first estimate the concentration of an external signaling molecule using membrane bound receptors [1][2][3][4][5][6]20]. The biophysics and biochemistry of cellular receptors is highly variable.…”

“…These computations are implemented using elaborate biochemical networks that may operate out of equilibrium and consume energy [1][2][3][4][5][6][7]. Given that energetic costs place important constraints on the design of physical computing devices [8] and neural computing architectures [9], one may conjecture that thermodynamic constraints also influence the design of cellular information processing networks.…”