Directional persistence and the optimality of run-and-tumble chemotaxis
Directional persistence and the optimality of run-and-tumble chemotaxis
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DOI：
10.1016/j.compbiolchem.2009.06.003
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发表时间：
2009-08-01
影响因子：
3.1
通讯作者：
Maini, Philip K.
中科院分区：
文献类型：
--
作者：
Nicolau, Dan V., Jr.;Armitage, Judith P.;Maini, Philip K.
E.大肠杆菌通过执行由交替的游泳（奔跑）和重新定向（翻滚）周期组成的有偏随机行走来进行趋化性。翻滚通常被建模为完全定向随机化，但已知在野生型E。大肠杆菌中，连续的运行方向实际上是弱相关的，平均方向差异类似于63度。我们最近提出了一个模型的细菌中的趋化性游泳策略的演变，这是能够定量再现这种相关性的出现。模型和实验之间的一致性表明，定向持续性可能具有某种功能，这一假设得到了早期模型结果的支持。在这里，我们调查的持久性对趋化效率的影响，使用空间蒙特卡罗模型的细菌游泳的梯度，结合模拟的自然选择的基础上趋化效率。参数空间的直接搜索揭示了两个吸引剂梯度制度，（a）低梯度制度，其中效率不受定向持久性和（B）高梯度制度，其中持久性可以提高趋化效率。使这种效应最大化的持久性参数的值与实验观察到的值非常接近。这一结果与模型细菌种群中方向记忆进化的独立模拟相匹配，该模拟也预测了高梯度条件下持久性的出现。在不同环境中的最优性和持久性之间的关系可能反映了随机行走觅食算法的普遍性质，它必须在两个相互竞争的目标之间达成妥协：探索和利用。我们还提出了一种新的图形方式来说明一个特定的性状在人口中的演变，在一个可进化的参数的变化。(C)2009爱思唯尔有限公司保留所有权利。
E. coli does chemotaxis by performing a biased random walk composed of alternating periods of swimming (runs) and reorientations (tumbles). Tumbles are typically modelled as complete directional randomisations but it is known that in wild type E. coli, successive run directions are actually weakly correlated, with a mean directional difference of similar to 63 degrees. We recently presented a model of the evolution of chemotactic swimming strategies in bacteria which is able to quantitatively reproduce the emergence of this correlation. The agreement between model and experiments suggests that directional persistence may serve some function, a hypothesis supported by the results of an earlier model. Here we investigate the effect of persistence on chemotactic efficiency, using a spatial Monte Carlo model of bacterial swimming in a gradient, combined with simulations of natural selection based on chemotactic efficiency. A direct search of the parameter space reveals two attractant gradient regimes, (a) a low-gradient regime, in which efficiency is unaffected by directional persistence and (b) a high-gradient regime, in which persistence can improve chemotactic efficiency. The value of the persistence parameter that maximises this effect corresponds very closely with the value observed experimentally. This result is matched by independent simulations of the evolution of directional memory in a population of model bacteria, which also predict the emergence of persistence in high-gradient conditions. The relationship between optimality and persistence in different environments may reflect a universal property of random-walk foraging algorithms, which must strike a compromise between two competing aims: exploration and exploitation. We also present a new graphical way to generally illustrate the evolution of a particular trait in a population, in terms of variations in an evolvable parameter. (C) 2009 Elsevier Ltd. All rights reserved.