Noisy information processing through transcriptional regulation

Cells must respond to environmental changes to remain viable, yet the information they receive is often noisy. Through a biochemical implementation of Bayes's rule, we show that genetic networks can act as inference modules, inferring from intracellular conditions the likely state of the extracellular environment and regulating gene expression appropriately. By considering a two-state environment, either poor or rich in nutrients, we show that promoter occupancy is proportional to the (posterior) probability of the high nutrient state given current intracellular information. We demonstrate that single-gene networks inferring and responding to a high environmental state infer best when negatively controlled, and those inferring and responding to a low environmental state infer best when positively controlled. Our interpretation is supported by experimental data from the lac operon and should provide a basis for both understanding more complex cellular decision-making and designing synthetic inference circuits.

[1]  A. Novick,et al.  ENZYME INDUCTION AS AN ALL-OR-NONE PHENOMENON. , 1957, Proceedings of the National Academy of Sciences of the United States of America.

[2]  E. Sutherland,et al.  ADENOSINE 3',5'-PHOSPHATE IN ESCHERICHIA COLI. , 1965, The Journal of biological chemistry.

[3]  J. Changeux,et al.  ON THE NATURE OF ALLOSTERIC TRANSITIONS: A PLAUSIBLE MODEL. , 1965, Journal of molecular biology.

[4]  R. Thomas,et al.  Boolean formalization of genetic control circuits. , 1973, Journal of theoretical biology.

[5]  L. Glass.  Combinatorial and topological methods in nonlinear chemical kinetics , 1975 .

[6]  D. Gillespie.  Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[7]  B. Bainbridge,et al.  Genetics , 1981, Experientia.

[8]  M. A. Shea,et al.  The OR control system of bacteriophage lambda. A physical-chemical model for gene regulation. , 1985, Journal of molecular biology.

[9]  M A Savageau,et al.  Demand theory of gene regulation. II. Quantitative application to the lactose and maltose operons of Escherichia coli. , 1998, Genetics.

[10]  E. Davidson,et al.  Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene. , 1998, Science.

[11]  J. Mukhopadhyay,et al.  Functional roles of the two cyclic AMP‐dependent forms of cyclic AMP receptor protein from Escherichia coli , 1999, FEBS letters.

[12]  M. Saraste,et al.  FEBS Lett , 2000 .

[13]  M. Ptashne,et al.  Genes and Signals , 2001 .

[14]  J. Ferrell.  Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. , 2002, Current opinion in cell biology.

[15]  Ertugrul M. Ozbudak,et al.  Regulation of noise in the expression of a single gene , 2002, Nature Genetics.

[16]  P. Swain,et al.  Stochastic Gene Expression in a Single Cell , 2002, Science.

[17]  U. Alon,et al.  Detailed map of a cis-regulatory input function , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Mads Kærn,et al.  Noise in eukaryotic gene expression , 2003, Nature.

[19]  J. Raser,et al.  Control of Stochasticity in Eukaryotic Gene Expression , 2004, Science.

[20]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[21]  Yuhong Yang,et al.  Information Theory, Inference, and Learning Algorithms , 2005 .

[22]  A. L. Koch.  The protein burden oflac operon products , 2005, Journal of Molecular Evolution.

[23]  U. Alon,et al.  Optimality and evolutionary tuning of the expression level of a protein , 2005, Nature.

[24]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.