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Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Immunol. | doi: 10.3389/fimmu.2019.01927

From discrete to continuous modeling of lymphocyte development and plasticity in chronic diseases

  • 1Instituto Mexicano del Seguro Social, Biomedical Research Center of East (CIBIOR), Mexico
  • 2Programa de Posgrado en Ciencias Bioquímicas, Universidad Nacional Autónoma de México, Mexico
  • 3Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico
  • 4Institute of Physics, National Autonomous University of Mexico, Mexico

The molecular events leading to differentiation, development and plasticity of lymphoid cells have been subject of intense research due to their key roles in multiple pathologies such as lymphoproliferative disorders, tumor growth maintenance and chronic diseases. The emergent roles of lymphoid cells and the use of high-throughput technologies have led to an extensive accumulation of experimental data allowing the reconstruction of gene regulatory networks (GRN) by integrating biochemical signals provided by the microenvironment with transcriptional modules of lineage-specific genes. Computational modeling of GRN has been useful for the identification of molecular switches involved in lymphoid specification, prediction of microenvironment-dependent cell plasticity, and analyses of signaling events occurring downstream the activation of antigen recognition receptors.
Among most common modeling strategies to analyze the dynamical behavior of GRN, discrete dynamic models are widely used for their capacity to capture molecular interactions when a limited knowledge of kinetic parameters is present. However, they are less powerful when modeling complex systems sensitive to biochemical gradients. To compensate it, discrete models may be transformed into regulatory networks that includes state variables and parameters varying within a continuous range. This approach is based on a system of differential equations dynamics with regulatory interactions described by fuzzy logic propositions. Here, we discuss the applicability of this method on modeling of development and plasticity processes of adaptive lymphocytes, and its potential implications in the study of pathological landscapes associated to chronic diseases.

Keywords: Lymphocytes, Chronic Disease, Boolean, Fuzzy Logic, computational modeling

Received: 12 Dec 2018; Accepted: 30 Jul 2019.

Edited by:

Gennady Bocharov, Institute of Numerical Mathematics (RAS), Russia

Reviewed by:

Sylvain Cussat-Blanc, Université de Toulouse, France
Y-h. Taguchi, Chuo University, Japan  

Copyright: © 2019 Enciso, Pelayo and Villarreal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Carlos Villarreal, Institute of Physics, National Autonomous University of Mexico, Mexico City, México, Mexico, carlos@fisica.unam.mx