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      A Minimal Model of Metabolism-Based Chemotaxis

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          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.

          Author Summary

          Traditionally, bacterial chemotaxis has been treated as metabolism-independent. Under this assumption, dedicated chemotaxis signalling pathways operate independently of metabolic processes. There is however, in various strains of bacteria, growing evidence of metabolism- dependent chemotaxis where metabolism modulates behavior. In this vein, we present the first model of metabolism-based chemotaxis that accomplishes chemotaxis without transmembrane receptors or signal transduction proteins, through the direct modulation of flagellar rotation by metabolite concentrations. The minimal model recreates chemotactic patterns found in bacteria, including: 1) chemotaxis towards metabolic resources and 2) away from metabolic inhibitors, 3) inhibition of chemotaxis in the presence of abundant resources, 4) cessation of chemotaxis to a resource due to inhibition of the metabolism of that resource, 5) sensitivity to metabolic and behavioral history and 6) integration of simultaneous complex environmental “stimuli”. The model demonstrates the substantial adaptability provided by the simple metabolism-based mechanism in the form of an ongoing, contextualized and integrative evaluation of the environment. Fumarate is identified as possibly playing a role in metabolism-based chemotaxis in bacteria, and some consequences of relaxing the metabolism-independent assumption are considered, causing us to reconsider the categorization of environmental compounds into “attractants” or “repellents” based solely on their binding properties.

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          Most cited references36

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          Two-component signal transduction.

          Most prokaryotic signal-transduction systems and a few eukaryotic pathways use phosphotransfer schemes involving two conserved components, a histidine protein kinase and a response regulator protein. The histidine protein kinase, which is regulated by environmental stimuli, autophosphorylates at a histidine residue, creating a high-energy phosphoryl group that is subsequently transferred to an aspartate residue in the response regulator protein. Phosphorylation induces a conformational change in the regulatory domain that results in activation of an associated domain that effects the response. The basic scheme is highly adaptable, and numerous variations have provided optimization within specific signaling systems. The domains of two-component proteins are modular and can be integrated into proteins and pathways in a variety of ways, but the core structures and activities are maintained. Thus detailed analyses of a relatively small number of representative proteins provide a foundation for understanding this large family of signaling proteins.
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            Making sense of it all: bacterial chemotaxis.

            Bacteria must be able to respond to a changing environment, and one way to respond is to move. The transduction of sensory signals alters the concentration of small phosphorylated response regulators that bind to the rotary flagellar motor and cause switching. This simple pathway has provided a paradigm for sensory systems in general. However, the increasing number of sequenced bacterial genomes shows that although the central sensory mechanism seems to be common to all bacteria, there is added complexity in a wide range of species.
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              Stochastic simulation of chemical reactions with spatial resolution and single molecule detail.

              Methods are presented for simulating chemical reaction networks with a spatial resolution that is accurate to nearly the size scale of individual molecules. Using an intuitive picture of chemical reaction systems, each molecule is treated as a point-like particle that diffuses freely in three-dimensional space. When a pair of reactive molecules collide, such as an enzyme and its substrate, a reaction occurs and the simulated reactants are replaced by products. Achieving accurate bimolecular reaction kinetics is surprisingly difficult, requiring a careful consideration of reaction processes that are often overlooked. This includes whether the rate of a reaction is at steady-state and the probability that multiple reaction products collide with each other to yield a back reaction. Inputs to the simulation are experimental reaction rates, diffusion coefficients and the simulation time step. From these are calculated the simulation parameters, including the 'binding radius' and the 'unbinding radius', where the former defines the separation for a molecular collision and the latter is the initial separation between a pair of reaction products. Analytic solutions are presented for some simulation parameters while others are calculated using look-up tables. Capabilities of these methods are demonstrated with simulations of a simple bimolecular reaction and the Lotka-Volterra system.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                December 2010
                December 2010
                2 December 2010
                09 December 2010
                : 6
                : 12
                : e1001004
                Affiliations
                [1 ]Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton, United Kingdom
                [2 ]Ikerbasque: Basque Foundation for Science, Department of Logic and Philosophy of Science, University of the Basque Country, San Sebastián, Spain
                University of Illinois at Urbana-Champaign, United States of America
                Author notes

                Conceived and designed the experiments: MDE XEB EADP. Performed the experiments: MDE. Analyzed the data: MDE. Contributed reagents/materials/analysis tools: MDE. Wrote the paper: MDE XEB. Designed and implemented the computational model: MDE. Literature Review: MDE XEB. Interpreted the results: XEB EADP. Reviewed the manuscript: EADP.

                Article
                10-PLCB-RA-2267R2
                10.1371/journal.pcbi.1001004
                3000427
                21170312
                dbdd1783-7d92-48bf-97a1-80a2ed99b763
                Egbert et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 19 May 2010
                : 21 October 2010
                Page count
                Pages: 17
                Categories
                Research Article
                Cell Biology/Chemical Biology of the Cell
                Computational Biology/Metabolic Networks
                Computational Biology/Systems Biology
                Microbiology/Microbial Physiology and Metabolism

                Quantitative & Systems biology
                Quantitative & Systems biology

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