Living systems are distinguished in nature by their ability to maintain stable, ordered states far from equilibrium. This is despite constant buffeting by thermodynamic forces that, if unopposed, will inevitably increase disorder. Cells maintain a steep transmembrane entropy gradient by continuous application of information that permits cellular components to carry out highly specific tasks that import energy and export entropy. Thus, the study of information storage, flow and utilization is critical for understanding first principles that govern the dynamics of life. Initial biological applications of information theory (IT) used Shannon’s methods to measure the information content in strings of monomers such as genes, RNA, and proteins. Recent work has used bioinformatic and dynamical systems to provide remarkable insights into the topology and dynamics of intracellular information networks. Novel applications of Fisher-, Shannon-, and Kullback–Leibler informations are promoting increased understanding of the mechanisms by which genetic information is converted to work and order. Insights into evolution may be gained by analysis of the the fitness contributions from specific segments of genetic information as well as the optimization process in which the fitness are constrained by the substrate cost for its storage and utilization. Recent IT applications have recognized the possible role of nontraditional information storage structures including lipids and ion gradients as well as information transmission by molecular flux across cell membranes. Many fascinating challenges remain, including defining the intercellular information dynamics of multicellular organisms and the role of disordered information storage and flow in disease.
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Adami, C., Ofria, C., Collier, T.C., 2000. Evolution of biological complexity. Proc. Natl. Acad. Sci. 97, 4463–4468.
Albert, R., Barabasi, A.-L., 2002. Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97.
Albert, R., Jeong, H., Barabasi, A.-L., 2000. Error and attack tolerance of complex networks. Nature 406, 378–382.
Alberts, B., 1998. The cell as a collection of protein machines:preparing the next generation of molecular biologists. Cell 92, 291–294.
Alberts, B., Bray, D., Lewis, J., Raff, M., Roberts, K., Watson, J.D., 1994. Molecular Biology of the Cell. Garland Publishing Inc., New York.
Brooks, D.R., Leblond, P.H., Cumming, D.D., 1984. Information and entropy in a simple evolution model. J. Theor. Biol. 109, 77–93.
Callaway, D.S., Hopcraft, J.E., Kleinberg, J.M., Newman, M.E., Strogatz, S.H., 2000. Network robustness and fragility: Percolation on random graphs. Phys. Rev. Lett. 85, 5468–5471.
Chamaraux, F., Fache, S., Bruckert, F., Fourcade, B., 2005. Kinetics of cell spreading. Phys. Rev. Lett. 94, 158102–158110.
Dehnert, M., Helm, W.E., Hutt, M.-T., 2005. Information theory reveals large-scale synchronization of statistical correlations in eukaryote genomes. Gene 345, 81–90.
Dockery, J.D., Keener, J.P., 2001. A mathematical model for quorum sensing in Pseudomonas aeruginosa. Bull. Math. Biol. 63, 95–116.
Ebeling, W., Frommel, C., 1998. Entropy and predictability of information carriers. Biosystem 46, 47–55.
Eigen, M., Schuster, P., 1977. The hypercycle. A principle of natural self-organization. Part A: Emergence of the hypercycle. Naturwissenschaften 64, 541–565.
Fath, B.D., Cabezas, H., Pawlowski, W., 2003. Regime changes in ecological systems: an information theory approach. J. Theor. Biol. 222, 517–530.
Fisher, R.A., 1925. Theory of statistical estimation. Proc. Cambridge Phil. Soc. 22, 700–725.
Fisher, R.A., 1959. Statistical methods and scientific inference, 2nd edition. Oliver and Boyd, London, pp. 1–112
Franca-Koh, J., Devreotes, P.N., 2004. Moving forward: Mechanisms of chemoattractant gradient sensing. Physiology 19, 300–308.
Frieden, B.R., 2001. Probability, Statistical Optics and Data Testing, 3rd edition. Springer-Verlag, Berlin.
Frieden, B.R., 2004. Science from Fisher Information, 2nd edition. Cambridge University Press, Cambridge, U.K.
Garcia, S.B., Novelli, M., Wright, N.A., 2000. The clonal origin and clonal evolution of epithelial tumors. Int. J. Exp. Path. 81, 89–116.
Gatenby, R.A., Frieden, B.R., 2002. Application of information theory and extreme physical information to carcinogenesis. Cancer Res. 62, 3675–3684.
Gatenby, R.A., Frieden, B.R., 2005a. Information dynamics in carcinogenesis and tumor growth. Mutat. Res. 568(2), 259–227.
Gatenby, R.A., Frieden, B.R., 2005b. The role of non-genomic information in maintaining thermodynamic stability in living systems. Math. Biosci. Eng. 2(1), 43–51.
Gilbert, E.N., 1966. Information theory after 18 years. Science 152, 320–326.
Grunenfelder, B., Winzeler, E.A., 2002. Treasures and traps in genome-wide data sets: Case examples from yeast. Nat. Rev. Genet. 3, 653–661.
Han, J-D., et al., 2004. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430, 88–93.
Hariri, A., Weber, B., Olmsted, J., 1990. On the validity of Shannon-information calculations for molecular biological sequences. J. Theor. Biol. 147, 235–254.
Jeong, H., et al., 2001. Lethality and centrality in protein networks. Nature 411, 41–45.
Jeong, H., et al., 2000. The large-scale organization of metabolic networks. Nature 407, 651–654.
Johnson, H.A., 1970. Information theory in biology after 18 years. Science 168, 1545–1550.
Kaiser, D., 2001. Building a multicellular organism. Annu. Rev. Genet. 35, 103–123.
Keener, J.P., 2005. A model for length control of flagellar hooks of Salmonella typhimurium. J. Theor. Biol. 234, 263–275.
Kendal, W.S., 1990. The use of information theory to analyze genomic changes in neoplasia. Math. Biosc. 100, 143–159.
Kullback, S., 1959 Information Theory and Statistics. Wiley, New York.
Lahoz-Beltra, R., 1997. Molecular automata assembly: Principles and simulation of bacterial membrane construction. Biosys. 44, 209–229.
Li, S., Armstrong, C.M., Bertin, N., et al., 2004. A map of the interactome network of the metazoan C. elegans. Nature 303, 540–543.
Loeb, L.A., 2001. A mutator phenotype in cancer. Cancer Res. 61, 3230–3239.
Luscombe, N.M., Babu, M.M., Yu, H., Snyder, M., Teichmann, S.A., Gerstein, M., 2004. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312.
Maxwell, J.C., 1880. Theory of Heat, 6th edition. D. Appleton Co., New York.
Morowitz, H.J., 1955. Some order-disorder considerations in living systems. Bull. Math. Biophys. 17, 81–86.
Morris, J.A., 2001. Information theory: A guide to the investigation of disease. J. Biosci. 26, 15–23.
Pierce, J.R., 1980. Information theory and physics, in introduction to information theory. symbols, signals, and noise. 2nd edition. Dover Publications, New York, pp. 184–207.
Prigogine, I., 1965. Steady states and entropy production. Physica 31, 719–724.
Reza, F.M., 1961. An Introduction to Information Theory. McGraw-Hill, New York.
Schneider, T.D., 2003. Evolution of biological information. Nucleic Acids Res. 28, 2794–2785.
Schneider, T.D., 1997. Information content of individual genetic sequences. J. Theor. Biol. 189, 427–441.
Schneider, T.D., 1991a. Theory of molecular machines. I. Channel capacity of molecular machines. J. Theor. Biol. 148, 83–123.
Schneider, T.D., 1991b. Theory of molecular machines II. Energy dissipation from molecular machines. J. Theor. Biol. 148, 125–137.
Schrodinger, E., 1944. What is Life? Cambridge University Press, Cambridge, U.K.
Segre, D., Ben-Eli, D., Lancet, D., 2000. Compositional genomes: Prebiotic information transfer in mutually catalytic non-covalent assemblies. Proc. Natl Acad. Sci. 97, 4112–4117.
Shannon, C.E., 1948. A mathematical theory of communication. Bell System Tech. J. 27, 379–623.
Sole, R.V., Deisboeck, T.S., 2004. An error catastrophe in cancer? J. Theor. Biol. 228, 47–54.
Strait, B.J., Dewey, T.G., 1996. The Shannon information entropy of protein sequences. J. Biophys. 71, 148–155.
Surette, M.G., Miller, M.B., Bassler, B.L., 1999. Quorum sensing in Escherichia coli, Salmonella typhimurium, and Vibrio harveyi: A new family of genes responsible for autoinducer production. Proc. Natl. Acad Sci. 96, 1639–1644.
Szilard, L., 1929. On the decrease of entropy in a thermodynamic system by the intervention of intelligent beings. Z. Physik. 53, 840–848.
Taga, M.E., Bassler, B.L., 2003. Chemical communication among bacteria. Proc. Natl. Acad. Sci. 100, 14549–14554.
Trincher, K.S., 1965. Biology and Information: Elements of Biological Thermodynamics. Consultants Bureau, New York.
Ulanowicz, R.E., 2001. Information theory in ecology. Comput. Chem. 25, 393–399.
von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S., Bork, P., 2002. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399–403.
Wagner, A., Fell, D., 2000. Technical Report No. 00-07-041. Santa Fe Inst.
Wallace, R., Wallace, R.G., 1998. Information theory, scaling laws and the thermodynamics of evolution. J. Theor. Biol. 192, 545–559.
Weiss, O., Jimenez-Montano, M.A., Herzel, H., 2000. Information content of protein sequence. J. Theor. Biol. 206, 379–386.
Yeger-Lotem, E., Sattath, S., Kashtan, N., Itzkovitz, S., Milo, R., Pinter, R.Y., Alon, U., Margalit, H., 2004. Network motifs in integrated cellular networks of transcription and protein-protein interactions. Proc. Nat. Acad. Sci. 101, 5934–5939.
Yook, S-H., Oltvai, Z.N., Barabasi, A.-L., 2004. Functional and topological characterization of protein interaction networks. Proteomics 4, 928–942.
Zhang, L.-H., Dong, Y.-H., 2004. Quorum sensing and signal interference:diverse implications. Mol. Micro. 53, 1563–1571.
Zeeberg, B., 2002. Shannon information theoretic computation of synonymous codon usage biases in coding regions of human and mouse genomes. Genome. Res. 1944–1955.
Zhao, L., Park, K., Lai, Y.-C., 2004. Attack vulnerability of scale-free networks due to cascading breakdown. Phys. Rev. E. 70, 1–4.
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Gatenby, R.A., Frieden, B.R. Information Theory in Living Systems, Methods, Applications, and Challenges. Bull. Math. Biol. 69, 635–657 (2007). https://doi.org/10.1007/s11538-006-9141-5