Recent extensive studies of Escherichia coli （E. coli） chemotaxis have achieved a deep understanding of its mi- croscopic control dynamics. As a result, various quantitatively predictive models have been developed t...Recent extensive studies of Escherichia coli （E. coli） chemotaxis have achieved a deep understanding of its mi- croscopic control dynamics. As a result, various quantitatively predictive models have been developed to describe the chemotactic behavior of E. coli motion. However, a population-level partial differential equation （PDE） that rationally incorporates such microscopic dynamics is still insufficient. Apart from the traditional Keller-Segel （K-S） equation, many existing population-level models developed from the microscopic dynamics are integro-PDEs. The difficulty comes mainly from cell tumbles which yield a velocity jumping process. Here, we propose a Langevin approximation method that avoids such a difficulty without appreciable loss of precision. The resulting model not only quantitatively repro- duces the results of pathway-based single-cell simulators, but also provides new inside information on the mechanism of E. coli chemotaxis. Our study demonstrates a possible alternative in establishing a simple population-level model that allows for the complex microscopic mechanisms in bacterial chemotaxis.展开更多
What makes biological systems different from man-made systems?One distinction is explored in this paper:Biological systems achieve reliable functions through randomness,i.e.,by both mitigating and exploiting the effec...What makes biological systems different from man-made systems?One distinction is explored in this paper:Biological systems achieve reliable functions through randomness,i.e.,by both mitigating and exploiting the effects of randomness.The fundamental reason for biological systems to take such a random approach is the randomness of the microscopic world,which is dramatically different from the macroscopic world we are familiar with.To substantiate the idea,bacterial chemotaxis is used as an example.展开更多
文摘Recent extensive studies of Escherichia coli （E. coli） chemotaxis have achieved a deep understanding of its mi- croscopic control dynamics. As a result, various quantitatively predictive models have been developed to describe the chemotactic behavior of E. coli motion. However, a population-level partial differential equation （PDE） that rationally incorporates such microscopic dynamics is still insufficient. Apart from the traditional Keller-Segel （K-S） equation, many existing population-level models developed from the microscopic dynamics are integro-PDEs. The difficulty comes mainly from cell tumbles which yield a velocity jumping process. Here, we propose a Langevin approximation method that avoids such a difficulty without appreciable loss of precision. The resulting model not only quantitatively repro- duces the results of pathway-based single-cell simulators, but also provides new inside information on the mechanism of E. coli chemotaxis. Our study demonstrates a possible alternative in establishing a simple population-level model that allows for the complex microscopic mechanisms in bacterial chemotaxis.
文摘What makes biological systems different from man-made systems?One distinction is explored in this paper:Biological systems achieve reliable functions through randomness,i.e.,by both mitigating and exploiting the effects of randomness.The fundamental reason for biological systems to take such a random approach is the randomness of the microscopic world,which is dramatically different from the macroscopic world we are familiar with.To substantiate the idea,bacterial chemotaxis is used as an example.
