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A mathematical model for Escherichia coli chemotaxis to competing stimuli
Biotechnology and Bioengineering ( IF 3.5 ) Pub Date : 2021-08-31 , DOI: 10.1002/bit.27930 Scott A Middlebrooks 1 , Xueying Zhao 1 , Roseanne M Ford 1 , Peter T Cummings 1, 2
Biotechnology and Bioengineering ( IF 3.5 ) Pub Date : 2021-08-31 , DOI: 10.1002/bit.27930 Scott A Middlebrooks 1 , Xueying Zhao 1 , Roseanne M Ford 1 , Peter T Cummings 1, 2
Affiliation
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Chemotactic bacteria sense and respond to temporal and spatial gradients of chemical cues in their surroundings. This phenomenon plays a critical role in many microbial processes such as groundwater bioremediation, microbially enhanced oil recovery, nitrogen fixation in legumes, and pathogenesis of the disease. Chemical heterogeneity in these natural systems may produce numerous competing signals from various directions. Predicting the migration behavior of bacterial populations under such conditions is necessary for designing effective treatment schemes. In this study, experimental studies and mathematical models are reported for the chemotactic response of Escherichia coli to a combination of attractant (α-methylaspartate) and repellent (NiCl2), which bind to the same transmembrane receptor complex. The model describes the binding of chemoeffectors and phosphorylation of the kinase in the signal transduction mechanism. Chemotactic parameters of E. coli (signaling efficiency 
, stimuli sensitivity coefficient 
, and repellent sensitivity coefficient 
) were determined by fitting the model with experimental results for individual stimuli. Interestingly, our model naturally identifies NiCl2 as a repellent for 
. The model is capable of describing quantitatively the response to the individual attractant and repellent, and correctly predicts the change in direction of bacterial population migration for competing stimuli with a twofold increase in repellent concentration.
中文翻译：
大肠杆菌对竞争刺激趋化的数学模型
趋化细菌感知并响应其周围环境中化学线索的时间和空间梯度。这种现象在许多微生物过程中起着至关重要的作用，例如地下水生物修复、微生物提高采油率、豆科植物的固氮和疾病的发病机制。这些自然系统中的化学异质性可能会从各个方向产生许多竞争信号。预测这种条件下细菌种群的迁移行为对于设计有效的处理方案是必要的。在这项研究中，实验研究和数学模型报告了大肠杆菌对引诱剂（α-甲基天冬氨酸）和驱虫剂（NiCl 2)，它们与相同的跨膜受体复合物结合。该模型描述了信号转导机制中化学效应子的结合和激酶的磷酸化。大肠杆菌的趋化参数（信号效率、刺激敏感系数和驱避敏感系数）通过将模型与单个刺激的实验结果拟合来确定。有趣的是，我们的模型自然地将 NiCl 2识别为. 该模型能够定量描述对个体引诱剂和驱虫剂的反应，并正确预测细菌种群迁移方向的变化，以应对竞争刺激，驱虫剂浓度增加两倍。
更新日期：2021-08-31
, stimuli sensitivity coefficient , and repellent sensitivity coefficient ) were determined by fitting the model with experimental results for individual stimuli. Interestingly, our model naturally identifies NiCl2 as a repellent for . The model is capable of describing quantitatively the response to the individual attractant and repellent, and correctly predicts the change in direction of bacterial population migration for competing stimuli with a twofold increase in repellent concentration.
中文翻译：
大肠杆菌对竞争刺激趋化的数学模型
趋化细菌感知并响应其周围环境中化学线索的时间和空间梯度。这种现象在许多微生物过程中起着至关重要的作用，例如地下水生物修复、微生物提高采油率、豆科植物的固氮和疾病的发病机制。这些自然系统中的化学异质性可能会从各个方向产生许多竞争信号。预测这种条件下细菌种群的迁移行为对于设计有效的处理方案是必要的。在这项研究中，实验研究和数学模型报告了大肠杆菌对引诱剂（α-甲基天冬氨酸）和驱虫剂（NiCl 2)，它们与相同的跨膜受体复合物结合。该模型描述了信号转导机制中化学效应子的结合和激酶的磷酸化。大肠杆菌的趋化参数（信号效率
、刺激敏感系数和驱避敏感系数）通过将模型与单个刺激的实验结果拟合来确定。有趣的是，我们的模型自然地将 NiCl 2识别为. 该模型能够定量描述对个体引诱剂和驱虫剂的反应，并正确预测细菌种群迁移方向的变化，以应对竞争刺激，驱虫剂浓度增加两倍。
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