Application of information theory in systems biology

Uda, Shinsuke

doi:10.1007/s12551-020-00665-w

Cited by 45 publications

(34 citation statements)

References 47 publications

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“…(We also note that the value of ∆i s→d can also be obtained directly from experiments, see Refs. [72][73][74][75].) (III) Limits of sensing radius: By studying total entropy production as a function of cell sensing radius and the LEUP parameters, we have provided an understanding of how a cell regulates its sensing radius according to its microenvironment to ensure the thermodynamic robustness of differentiated tissue.…”

Section: Discussionmentioning

confidence: 99%

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Close to Optimal Cell Sensing Ensures the Robustness of Tissue Differentiation Process: The Avian Photoreceptor Mosaic Case

Barua

Beygi

Hatzikirou

2021

Entropy

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The way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility), but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic.

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Section: Discussionmentioning

confidence: 99%

“…(We also note that the value of

can also be obtained directly from experiments, see Refs. [ 72 , 73 , 74 , 75 ].)…”

Section: Discussionmentioning

confidence: 99%

Close to Optimal Cell Sensing Ensures the Robustness of Tissue Differentiation Process: The Avian Photoreceptor Mosaic Case

Barua

Beygi

Hatzikirou

2021

Entropy

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“…(We also note that the value of ∆i s→d can also be obtained directly from experiments, see Refs. [45][46][47][48]. )…”

Section: Discussionmentioning

confidence: 99%

Close to optimal cell sensing ensures the robustness of tissue differentiation process: the avian photoreceptor mosaic case

Barua

Beygi

Hatzikirou

2021

Preprint

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The way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility) but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic.

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“…Estimation of metrics such as interaction information relies on robust empirical estimations of multivariate distributions and joint probability mass functions with groups of random variables that are not straightforward at all, and more so, when data points are limited. Difficulties related to obtaining sufficient sample sizes and the computational burden associated with such estimations using high-dimensional and heterogeneous data often encountered in biology can result in bottlenecks in the application of information theory to systems biology [ 190 ]. Another set of issues to be tackled exist in the modeling front that include behavior of noise and robustness of models to imperfections and irregularities, a frequent occurrence in the experimental biological domain [ 188 , 191 ].…”

Section: Discussionmentioning

confidence: 99%

Information Theory in Computational Biology: Where We Stand Today

Chanda

Costa²,

et al. 2020

Entropy

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“A Mathematical Theory of Communication” was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon’s work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology—gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.

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Application of information theory in systems biology

Cited by 45 publications

References 47 publications

Close to Optimal Cell Sensing Ensures the Robustness of Tissue Differentiation Process: The Avian Photoreceptor Mosaic Case

Close to Optimal Cell Sensing Ensures the Robustness of Tissue Differentiation Process: The Avian Photoreceptor Mosaic Case

Close to optimal cell sensing ensures the robustness of tissue differentiation process: the avian photoreceptor mosaic case

Information Theory in Computational Biology: Where We Stand Today

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