Department of Physics and Astrophysics, Delhi University, St. Stephen's College, Delhi 110007, India.
J Theor Biol. 2010 Sep 7;266(1):99-106. doi: 10.1016/j.jtbi.2010.06.012. Epub 2010 Jun 15.
The bacterium Escherichia coli (E. coli) moves in its natural environment in a series of straight runs, interrupted by tumbles which cause change of direction. It performs chemotaxis towards chemo-attractants by extending the duration of runs in the direction of the source. When there is a spatial gradient in the attractant concentration, this bias produces a drift velocity directed towards its source, whereas in a uniform concentration, E. coli adapts, almost perfectly in case of methyl aspartate. Recently, microfluidic experiments have measured the drift velocity of E. coli in precisely controlled attractant gradients, but no general theoretical expression for the same exists. With this motivation, we study an analytically soluble model here, based on the Barkai-Leibler model, originally introduced to explain the perfect adaptation. Rigorous mathematical expressions are obtained for the chemotactic response function and the drift velocity in the limit of weak gradients and under the assumption of completely random tumbles. The theoretical predictions compare favorably with experimental results, especially at high concentrations. We further show that the signal transduction network weakens the dependence of the drift on concentration, thus enhancing the range of sensitivity.
在其自然环境中,细菌大肠杆菌(E. coli)以一系列直线运动移动,运动过程中会被翻滚打断,从而导致方向发生变化。它通过在化学引诱剂源的方向上延长奔跑的时间来执行趋化作用。当引诱剂浓度存在空间梯度时,这种偏差会产生朝向其源的漂移速度,而在均匀浓度下,大肠杆菌会适应,在甲基天冬氨酸的情况下几乎是完美适应。最近,微流控实验已经在精确控制的引诱剂梯度下测量了大肠杆菌的漂移速度,但目前还没有针对这种情况的一般理论表达式。基于 Barkai-Leibler 模型,我们在这里研究了一个可分析求解的模型,该模型最初被引入以解释完美适应现象。我们在弱梯度极限和完全随机翻滚的假设下,得到了趋化响应函数和漂移速度的严格数学表达式。理论预测与实验结果非常吻合,特别是在高浓度下。我们进一步表明,信号转导网络减弱了漂移对浓度的依赖性,从而提高了灵敏度范围。