dorsal/arxiv
View SchemaApplication of Coarse Integration to Bacterial Chemotaxis
| Authors | S. Setayeshgar, C. W. Gear, H. G. Othmer, I. G. Kevrekidis |
|---|---|
| Categories | |
| ArXiv ID | physics/0308040 |
| URL | https://arxiv.org/abs/physics/0308040 |
Abstract
We have developed and implemented a numerical evolution scheme for a class of stochastic problems in which the temporal evolution occurs on widely-separated time scales, and for which the slow evolution can be described in terms of a small number of moments of an underlying probability distribution. We demonstrate this method via a numerical simulation of chemotaxis in a population of motile, independent bacteria swimming in a prescribed gradient of a chemoattractant. The microscopic stochastic model, which is simulated using a Monte Carlo method, uses a simplified deterministic model for excitation/adaptation in signal transduction, coupled to a realistic, stochastic description of the flagellar motor. We show that projective time integration of ``coarse'' variables can be carried out on time scales long compared to that of the microscopic dynamics. Our coarse description is based on the spatial cell density distribution. Thus we are assuming that the system ``closes'' on this variable so that it can be described on long time scales solely by the spatial cell density. Computationally the variables are the components of the density distribution expressed in terms of a few basis functions, given by the singular vectors of the spatial density distribution obtained from a sample Monte Carlo time evolution of the system. We present numerical results and analysis of errors in support of the efficacy of this time-integration scheme.
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"abstract": "We have developed and implemented a numerical evolution scheme for a class of\nstochastic problems in which the temporal evolution occurs on widely-separated\ntime scales, and for which the slow evolution can be described in terms of a\nsmall number of moments of an underlying probability distribution. We\ndemonstrate this method via a numerical simulation of chemotaxis in a\npopulation of motile, independent bacteria swimming in a prescribed gradient of\na chemoattractant. The microscopic stochastic model, which is simulated using a\nMonte Carlo method, uses a simplified deterministic model for\nexcitation/adaptation in signal transduction, coupled to a realistic,\nstochastic description of the flagellar motor. We show that projective time\nintegration of ``coarse\u0027\u0027 variables can be carried out on time scales long\ncompared to that of the microscopic dynamics. Our coarse description is based\non the spatial cell density distribution. Thus we are assuming that the system\n``closes\u0027\u0027 on this variable so that it can be described on long time scales\nsolely by the spatial cell density. Computationally the variables are the\ncomponents of the density distribution expressed in terms of a few basis\nfunctions, given by the singular vectors of the spatial density distribution\nobtained from a sample Monte Carlo time evolution of the system. We present\nnumerical results and analysis of errors in support of the efficacy of this\ntime-integration scheme.",
"arxiv_id": "physics/0308040",
"authors": [
"S. Setayeshgar",
"C. W. Gear",
"H. G. Othmer",
"I. G. Kevrekidis"
],
"categories": [
"physics.comp-ph",
"physics.bio-ph",
"q-bio.CB"
],
"title": "Application of Coarse Integration to Bacterial Chemotaxis",
"url": "https://arxiv.org/abs/physics/0308040"
},
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