Lessons and perspectives for applications of stochastic models in biological and cancer research

Authors

  • Alan U. Sabino Universidade de Sao Paulo. Escola de Artes Cieˆncias e Humanidades
  • Miguel FS Vasconcelos Universidade de Sao Paulo. Escola de Artes Cieˆncias e Humanidades
  • Misaki Yamada Sittoni Universidade de Sao Paulo. Escola de Artes Cieˆncias e Humanidades
  • Willian W. Lautenschlager Universidade de Sao Paulo. Escola de Artes Cieˆncias e Humanidades
  • Alexandre S. Queiroga Universidade de Sao Paulo. Faculdade de Medicina. Hospital das Clinicas. Instituto do Cancer do Estado de Sao Paulo
  • Mauro CC Morais Universidade de Sao Paulo. Faculdade de Medicina. Instituto do Cancer do Estado de Sao Paulo
  • Alexandre F. Ramos Universidade de Sao Paulo. Faculdade de Medicina. Instituto do Cancer do Estado de Sao Paulo

DOI:

https://doi.org/10.6061/clinics/2018/e536s

Keywords:

Markov Chains, Models, Theoretical, Stochastic Processes, Regulation, Gene Expression, Contact Inhibition

Abstract

The effects of randomness, an unavoidable feature of intracellular environments, are observed at higher hierarchical levels of living matter organization, such as cells, tissues, and organisms. Additionally, the many compounds interacting as a well-orchestrated network of reactions increase the difficulties of assessing these systems using only experiments. This limitation indicates that elucidation of the dynamics of biological systems is a complex task that will benefit from the establishment of principles to help describe, categorize, and predict the behavior of these systems. The theoretical machinery already available, or ones to be discovered to help solve biological problems, might play an important role in these processes. Here, we demonstrate the application of theoretical tools by discussing some biological problems that we have approached mathematically: fluctuations in gene expression and cell proliferation in the context of loss of contact inhibition. We discuss the methods that have been employed to provide the reader with a biologically motivated phenomenological perspective of the use of theoretical methods. Finally, we end this review with a discussion of new research perspectives motivated by our results.

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Published

2019-02-15

Issue

Section

Review Articles

How to Cite

Lessons and perspectives for applications of stochastic models in biological and cancer research. (2019). Clinics, 73(Suppl. 1), e536s. https://doi.org/10.6061/clinics/2018/e536s