Information Theory in Biology after 18 Years

Johnson, Harvey L.

doi:10.1126/science.168.3939.1545

Cited by 80 publications

(53 citation statements)

References 8 publications

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“…Using the divergence formulation in (8) above, the mutual information can be written in terms of the input p.m.f variable as follows: (11) Hence, the channel capacity among all possible is obtained by (12) Some straightforward algebra leads to the optimal (13) in which (14) Therefore, the channel capacity is (15) …”

Section: The Capacity Of a Binary Non-symmetric Channelmentioning

confidence: 99%

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An Information Theoretical Analysis of Kinase Activated Phosphorylation Dephosphorylation Cycle

Qian

Roy

2012

IEEE Trans.on Nanobioscience

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Signal transduction, the information processing mechanism in biological cells, is carried out by a network of biochemical reactions. The dynamics of driven biochemical reactions can be studied in terms of nonequilibrium statistical physics. Such systems may also be studied in terms of Shannon's information theory. We combine these two perspectives in this study of the basic units (modules) of cellular signaling: the phosphorylation dephosphorylation cycle (PdPC) and the guanosine triphosphatase (GTPase). We show that the channel capacity is zero if and only if the free energy expenditure of biochemical system is zero. In fact, a positive correlation between the channel capacity and free energy expenditure is observed. In terms of the information theory, a linear signaling cascade consisting of multiple steps of PdPC can function as a distributed "multistage code." With increasing number of steps in the cascade, the system trades channel capacity with the code complexity. Our analysis shows that while a static code can be molecular structure based, a biochemical communication channel has to have energy expenditure.

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Section: The Capacity Of a Binary Non-symmetric Channelmentioning

confidence: 99%

“…For the PdPC, the optimal usage is when the upstream kinase with probability being inactive, and being active, where is given in (13). It is useful to determine the value of in the limit for a fixed as shown in Fig.…”

Section: A Channel Capacity and Aos Of Pdpcmentioning

confidence: 99%

An Information Theoretical Analysis of Kinase Activated Phosphorylation Dephosphorylation Cycle

Qian

Roy

2012

IEEE Trans.on Nanobioscience

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“…In a 1970 review (Johnson, 1970), Johnson characterized information theory (IT) as a "general calculus for biology." It is clear that life without matter and energy is impossible.…”

Section: Introductionmentioning

confidence: 99%

Information Theory in Living Systems, Methods, Applications, and Challenges

Gatenby

Frieden

2006

Bull. Math. Biol.

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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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“…We ground our purposes in the observation that conceptualizing information in biology generally has attracted attention from scientists and philosophers, specifically those interested in genetics, genomics, development, and evolution [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Information in phylogenetic systematic analysis particularly has been quantified and used as a measure for analyzing characters [18], gauging cladogram resolution [19,20], assessing cladogram topologies [21][22][23][24], proposing that evolution is a nonequilibrium thermodynamic process [25][26][27][28], evaluating classifications [29,30], preferring one among multiple equally parsimonious cladograms [31], combining multiple independent cladograms into a single cladogram, or "supertree", through matrix representation [32][33][34][35], quantifying diversity among supraspecific taxa [36], and comparing molecular and morphological data [37].…”

Section: Statement Of Purposementioning

confidence: 99%

Information and Phylogenetic Systematic Analysis

Craig

Stone

2015

Information

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Information in phylogenetic systematic analysis has been conceptualized, defined, quantified, and used differently by different authors. In this paper, we start with the Shannon Uncertainty Measure information measure I, applying it to cladograms containing only consistent character states. We formulate a general expression for I, utilizing a standard format for taxon-character matrices, and investigate the effect that adding data to an existing taxon-character matrix has on I. We show that I may increase when character vectors that encode autapomorphic or synapomorphic character states are added. However, as added character vectors accumulate, I tends to a limit, which generally is less than the maximum I. We show computationally and analytically that limc→  I = 2 log t , in which t enumerates taxa and c enumerates characters. For any particular t, upper and lower bounds in I exist. We use our observations to suggest several interpretations about the relationship between information and phylogenetic systematic analysis that have eluded previous, precise recognition.

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Information Theory in Biology after 18 Years

Cited by 80 publications

References 8 publications

An Information Theoretical Analysis of Kinase Activated Phosphorylation Dephosphorylation Cycle

An Information Theoretical Analysis of Kinase Activated Phosphorylation Dephosphorylation Cycle

Information Theory in Living Systems, Methods, Applications, and Challenges

Information and Phylogenetic Systematic Analysis

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