Using Entropy Leads to a Better Understanding of Biological Systems

Tseng, Chih‐Yuan; Tuszyński, Jack A.

doi:10.3390/e12122450

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…For a given system and function x (e.g., a folded RNA sequence that binds to guanosine triphosphate GTP), and degree of function E x (e.g., the RNA-GTP binding energy), functional information is I(E x ) = − log 2 [F(E x )], where F(E x ) is the fraction of all possible configurations of the system [14,[32][33][34]. Functional information, for example, analyzes RNA structures that bind target ligands and RNA structures that catalyze specific reactions.…”

Section: Information and Biological Systemsmentioning

confidence: 99%

Information in Biological Systems and the Fluctuation Theorem

Demi̇rel

2014

Entropy

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Some critical trends in information theory, its role in living systems and utilization in fluctuation theory are discussed. The mutual information of thermodynamic coupling is incorporated into the generalized fluctuation theorem by using information theory and nonequilibrium thermodynamics. Thermodynamically coupled dissipative structures in living systems are capable of degrading more energy, and processing complex information through developmental and environmental constraints. The generalized fluctuation theorem can quantify the hysteresis observed in the amount of the irreversible work in nonequilibrium regimes in the presence of information and thermodynamic coupling.

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Section: Information and Biological Systemsmentioning

confidence: 99%

Information in Biological Systems and the Fluctuation Theorem

Demi̇rel

2014

Entropy

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“…There are clearly similarities in the role of entropy in large ecosystems networks and the role of entropy in protein-protein interaction networks. Tseng and Tuszynski (2010) [11] propose that an understanding of the maximum entropy principle can lead to a better understanding of biological systems. Complex ecosystems maximize entropy to much higher degree and much more rapidly than simple ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Gibbs Free Energy of Protein-Protein Interactions reflects tumor stage

Rietman

Bloemendal

Platig

et al. 2015

Preprint

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The sequential changes occurring with cancer progression are now being harnessed with therapeutic intent. Yet, there is no understanding of the chemical thermodynamics of proteomic changes associated with cancer progression/ cancer stage. This manuscript reveals a strong correlation of a chemical thermodynamic measure (known as Gibbs free energy) of protein-protein interaction networks for several cancer types and 5-year overall survival and stage in patients with cancer. Earlier studies have linked degree entropy of signaling networks to patient survival data, but not with stage. It appears that Gibbs free energy is a more general metric and accounts better for the underlying energetic landscape of protein expression in cells, thus correlating with stage as well as survival.

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“…This behaviour of the tryptophan is used to get structural informations on the intermediate forms of proteins that characterise their folding process from an extended random polypeptide chain. In complex situations, such as those encountered even in small proteins, the rate of energy transfer and thus the intra-molecular distances can be described by a continuum distribution due to heterogeneity of the structure that turns out to a continuum lifetime distribution for the fluorescence decay [9–12]. In these cases, the analysis for recovering the distribution takes advantages of a “regularizing function” in addition to the chi-squared statistic for forcing the data to choose one member out of the set of the feasible distributions [13].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Simulated Fluorescence Intensities Decays by a New Maximum Entropy Method Algorithm

2012

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A new algorithm for the Maximum Entropy Method (MEM) is proposed for recovering the lifetime distribution in time-resolved fluorescence decays. The procedure is based on seeking the distribution that maximizes the Skilling entropy function subjected to the chi-squared constraint χ2 ~ 1 through iterative linear approximations, LU decomposition of the Hessian matrix of the lagrangian problem and the Golden Section Search for backtracking. The accuracy of this algorithm has been investigated through comparisons with simulated fluorescence decays both of narrow and broad lifetime distributions. The proposed approach is capable to analyse datasets of up to 4,096 points with a discretization ranging from 100 to 1,000 lifetimes. A good agreement with non linear fitting estimates has been observed when the method has been applied to multi-exponential decays. Remarkable results have been also obtained for the broad lifetime distributions where the position is recovered with high accuracy and the distribution width is estimated within 3 %. These results indicate that the procedure proposed generates MEM lifetime distributions that can be used to quantify the real heterogeneity of lifetimes in a sample.

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Using Entropy Leads to a Better Understanding of Biological Systems

Cited by 6 publications

References 44 publications

Information in Biological Systems and the Fluctuation Theorem

Information in Biological Systems and the Fluctuation Theorem

Gibbs Free Energy of Protein-Protein Interactions reflects tumor stage

Analysis of Simulated Fluorescence Intensities Decays by a New Maximum Entropy Method Algorithm

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