Information and fitness

The growth rate of organisms depends both on external conditions and on internal states, such as the expression levels of various genes. We show that to achieve a criterion mean growth rate over an ensemble of conditions, the internal variables must carry a minimum number of bits of information about those conditions. Evolutionary competition thus can select for cellular mechanisms that are more efficient in an abstract, information theoretic sense. Estimates based on recent experiments suggest that the minimum information required for reasonable growth rates is close to the maximum information that can be conveyed through biologically realistic regulatory mechanisms. These ideas are applicable most directly to unicellular organisms, but there are analogies to problems in higher organisms, and we suggest new experiments for both cases.

[1]  F. Attneave Some informational aspects of visual perception. , 1954, Psychological review.

[2]  John L. Kelly,et al.  A new interpretation of information rate , 1956, IRE Trans. Inf. Theory.

[3]  Richard E. Blahut,et al.  Computation of channel capacity and rate-distortion functions , 1972, IEEE Trans. Inf. Theory.

[4]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[5]  Shirley Dex,et al.  JR 旅客販売総合システム(マルス)における運用及び管理について , 1991 .

[6]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[7]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[8]  Daniel M. Wolpert,et al.  Making smooth moves , 2022 .

[9]  A. U.S.,et al.  Predictability , Complexity , and Learning , 2002 .

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

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

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

[13]  P. Swain,et al.  Gene Regulation at the Single-Cell Level , 2005, Science.

[14]  S. Leibler,et al.  Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments , 2005, Science.

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

[16]  Naftali Tishby,et al.  Efficient representation as a design principle for neural coding and computation , 2006, 2006 IEEE International Symposium on Information Theory.

[17]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[18]  The effects of molecular noise and size control on variability in the budding yeast cell cycle , 2007, Nature.

[19]  I. Nemenman,et al.  Optimal Signal Processing in Small Stochastic Biochemical Networks , 2006, PloS one.

[20]  W. Bialek,et al.  Probing the Limits to Positional Information , 2007, Cell.

[21]  Frederick R. Cross,et al.  The effects of molecular noise and size control on variability in the budding yeast cell cycle , 2007, Nature.

[22]  W. Bialek,et al.  The Role of Input Noise in Transcriptional Regulation , 2006, PloS one.

[23]  W. Bialek,et al.  Information flow and optimization in transcriptional regulation , 2007, Proceedings of the National Academy of Sciences.

[24]  Gasper Tkacik,et al.  Information capacity of genetic regulatory elements. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Jeffrey W. Smith,et al.  Stochastic Gene Expression in a Single Cell , 2022 .