Toward deciphering of cancer imbalances: Using information-theoretic surprisal analysis for understanding of cancer systems

Nataly Kravchenko-Balasha*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

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.

Original languageAmerican English
Title of host publicationAdvances in Info-Metrics
Subtitle of host publicationInformation and Information Processing across Disciplines
PublisherOxford University Press
Pages215-239
Number of pages25
ISBN (Electronic)9780190636685
DOIs
StatePublished - 1 Jan 2020

Bibliographical note

Publisher Copyright:
© Oxford University Press 2021.

Keywords

  • Information-theoretic approach
  • Intratumor and intertumor heterogeneity
  • Patient-specific signaling signatures
  • Personalized (precision) medicine
  • Single-cell analysis
  • Thermodynamic-based approach

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