Guest editorial: Integrative data mining in systems biology: from text to network mining
Artificial Intelligence in Medicine
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Objective: Cell motility and chemotaxis play a role in the virulence of pathogenic bacteria, such as escape from host immune responses. Escherichia coli chemotaxis provides a well-characterized model system for the bacterial chemotaxis network. Two features of E. coli chemotaxis include signal amplification and robustly accurate adaptation. Recent simulation studies with models considering the effects of other receptors have suggested possible mechanisms for signal amplification. Although precise adaptation to aspartate has been explained by conventional kinetic models, the adaptation behavior of models incorporating the effects of other receptors remains unclear. Methods: We concentrated on how receptor crosstalk affects minimization of adaptation error and compared models in which the contribution of other receptors varied. Results: We demonstrated that the model is adaptable to attractant concentrations ranging from 0.1@mM to 10mM with a decreased error rate (from 8% to 2%) when the kinetic constant of CheA and phosphorylated CheY dissociation is increased. Conclusion: The results suggest that accurate adaptation is maintained through control of both the interaction of cytoplasmic Che proteins and the activity of the receptor complex.