Control analysis of bacterial chemotaxis signaling.

Bacteria such as Escherichia coli demonstrate the remarkable ability to migrate up gradients of attractants and down gradients of repellents in a rapid and sensitive fashion. They employ a temporal sensing strategy in which they estimate the concentration of ligand at different time points and continue moving in the same direction if the concentration is increasing in time, and randomly reorient if the concentration is decreasing in time. The key to success is accurate sensing of ligand levels in the presence of extracellular and intracellular disturbances. Research from a control theory perspective has begun to characterize the robustness of the bacterial chemotaxis signal transduction system to these perturbations. Modeling and theory can describe the optimal performance of such a sensor and how it can be achieved, thereby illuminating the design of the network. This chapter describes some basic principles of control theory relevant to the analysis of this sensing system, including sensitivity analysis, Bode plots, integral feedback control, and noise filters (i.e., Kalman filters).

[1]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[2]  H. Berg.  Random Walks in Biology , 2018 .

[3]  John H. Milsum,et al.  Biological Control Systems Analysis , 1966 .

[4]  Pablo A. Iglesias,et al.  A general framework for achieving integral control in chemotactic biological signaling mechanisms , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[5]  R. Heinrich,et al.  The Regulation of Cellular Systems , 1996, Springer US.

[6]  H. Berg,et al.  Physics of chemoreception. , 1977, Biophysical journal.

[7]  C. Rao,et al.  Control, exploitation and tolerance of intracellular noise , 2002, Nature.

[8]  J. Stock,et al.  Systems biology of bacterial chemotaxis. , 2006, Current opinion in microbiology.

[9]  J. Doyle,et al.  Robust perfect adaptation in bacterial chemotaxis through integral feedback control. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[11]  U. Alon,et al.  Robustness in bacterial chemotaxis , 2022 .

[12]  Gene F. Franklin,et al.  Feedback Control of Dynamic Systems , 1986 .

[13]  Pablo A. Iglesias,et al.  Optimal Noise Filtering in the Chemotactic Response of Escherichia coli , 2006, PLoS Comput. Biol..

[14]  J. Timmer,et al.  Design principles of a bacterial signalling network , 2005, Nature.

[15]  James M. Bower,et al.  Computational modeling of genetic and biochemical networks , 2001 .

[16]  Steven H. Strogatz,et al.  Nonlinear Dynamics and Chaos , 2024 .

[17]  J. Stelling,et al.  Robustness properties of circadian clock architectures. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  M. Savageau,et al.  Parameter Sensitivity as a Criterion for Evaluating and Comparing the Performance of Biochemical Systems , 1971, Nature.

[19]  A. Saltelli,et al.  Sensitivity analysis for chemical models. , 2005, Chemical reviews.

[20]  Richard M. Murray,et al.  Feedback Systems An Introduction for Scientists and Engineers , 2007 .

[21]  Mats Jirstrand,et al.  Systems biology Systems Biology Toolbox for MATLAB : a computational platform for research in systems biology , 2006 .

[22]  Pablo A. Iglesias,et al.  Optimal noise filtering in the chemotactic response of E. coli , 2005 .

[23]  J. Stock,et al.  Bacterial chemotaxis and the molecular logic of intracellular signal transduction networks. , 1991, Annual review of biophysics and biophysical chemistry.

[24]  S. Leibler,et al.  Robustness in simple biochemical networks , 1997, Nature.

[25]  Neil Gershenfeld,et al.  The nature of mathematical modeling , 1998 .