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Advances in Info-MetricsInformation and Information Processing across Disciplines$
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Min Chen, J. Michael Dunn, Amos Golan, and Aman Ullah

Print publication date: 2020

Print ISBN-13: 9780190636685

Published to Oxford Scholarship Online: December 2020

DOI: 10.1093/oso/9780190636685.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 21 June 2021

Toward Deciphering of Cancer Imbalances: Using Information-Theoretic Surprisal Analysis for Understanding of Cancer Systems

Toward Deciphering of Cancer Imbalances: Using Information-Theoretic Surprisal Analysis for Understanding of Cancer Systems

Chapter:
(p.215) 8 Toward Deciphering of Cancer Imbalances: Using Information-Theoretic Surprisal Analysis for Understanding of Cancer Systems
Source:
Advances in Info-Metrics
Author(s):

Nataly Kravchenko-Balasha

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190636685.003.0008

Changes in free energy define the direction of spontaneous changes in chemistry, physics, and engineering. In this chapter, I show that, similar to systems in chemistry and physics, the interpretation of molecular alterations using a thermodynamic-based information-theoretic approach and quantifications of those alterations in the framework of free–energy changes allows the prediction and rational manipulation of biological phenotypes, such as the spatial distributions of aggressive brain tumor cells, the direction of cell–cell movement or cell response to drug treatments. Any physical system, including nonequilibrium systems, reaches a state of minimal free energy that is subject to constraints. Surprisal analysis, a thermodynamic-based information-theoretic algorithm, was developed with the purpose of quantifying the constraints, and thereby predict the direction of change, in molecular reactions. In biological systems, the numbers of transcript/protein molecules are not free to vary in the cells but rather are limited, or constrained, by regulatory processes. Thus, the physical framework of constraints that deviate the system from a state of minimum free energy (e.g. steady state) provides the predictive understanding of molecular changes in response to perturbations, such as drug treatments. The chapter discusses how surprisal analysis can be used to predict biological behaviors, including the further development and extension of the theory to the field of personalized cancer medicine.

Keywords:   information-theoretic approach, personalized (precision) medicine, patient-specific signaling signatures, intratumor and intertumor heterogeneity, single-cell analysis, thermodynamic- based approach

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