Date Published: December 2, 2010
Publisher: Public Library of Science
Author(s): Matthew D. Egbert, Xabier E. Barandiaran, Ezequiel A. Di Paolo, Christopher V. Rao
Abstract: Since the pioneering work by Julius Adler in the 1960’s, bacterial chemotaxis has been predominantly studied as metabolism-independent. All available simulation models of bacterial chemotaxis endorse this assumption. Recent studies have shown, however, that many metabolism-dependent chemotactic patterns occur in bacteria. We hereby present the simplest artificial protocell model capable of performing metabolism-based chemotaxis. The model serves as a proof of concept to show how even the simplest metabolism can sustain chemotactic patterns of varying sophistication. It also reproduces a set of phenomena that have recently attracted attention on bacterial chemotaxis and provides insights about alternative mechanisms that could instantiate them. We conclude that relaxing the metabolism-independent assumption provides important theoretical advances, forces us to rethink some established pre-conceptions and may help us better understand unexplored and poorly understood aspects of bacterial chemotaxis.
Partial Text: Bacterial chemotaxis is one of the best known examples of adaptive unicellular motility. In particular, the mechanisms underlying chemotaxis in Escherichia coli have been studied in detail for the last 40 years (for recent, comprehensive reviews see e.g., –). Since the work of pioneers such as Adler , , Berg , Macnab , and Spudich , considerable advances continue to be made concerning the molecular structure of motors , , the structure of transmembrane receptors and their collective dynamics ,  and the details of a two component signal transduction system  that mediates between sensors and motors . Computer simulations of the underlying biochemical processes have helped to support and clarify the current model of chemotaxis mechanisms , .
We now describe five experimental scenarios where simulated bacteria are placed in environments containing different distributions of different chemicals compounds. At the start of each simulation, 100 simulated bacteria are distributed evenly around the unit square environment in a grid (indicated in the left-most plot of e.g. Figure 6B). Each iteration, the metabolism and position of each bacteria is updated according to the equations described earlier. Bacteria are all initiated with a low (0.05) concentration of their metabolites unless otherwise indicated. Except for Experiment 5, all environmental resources have a peak concentration of 1.0 and fall off with distance from their center according to the following equation where is the concentration of the relevant resources and is distance from the center: . Resources in the environment are always kept constant (i.e., there are no stigmergic effects – bacteria do not affect the concentration or distribution of resources in the environment).
Research in bacterial chemotaxis has operated largely under the assumption that the behavior is supported by transmembrane receptors and dedicated signalling pathways and that such pathways are metabolism-independent. Despite the growing body of evidence that in many species this might not always be the case (even for those largely thought to be so, such as E. coli) available simulation models of bacterial chemotaxis assume metabolism-independence. Here we have presented the first minimal model of metabolism-based chemotaxis. It recreates phenomena observed in bacteria and allows us to explore some potential consequences of metabolism-based behavior.