Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

Abstract

The eukaryotic cell cycle is robustly designed, with interacting molecules organized within a definite topology that ensures temporal precision of its phase transitions. Its underlying dynamics are regulated by molecular switches, for which remarkable insights have been provided by genetic and molecular biology efforts. In a number of cases, this information has been made predictive, through computational models. These models have allowed for the identification of novel molecular mechanisms, later validated experimentally. Logical modeling represents one of the youngest approaches to address cell cycle regulation. We summarize the advances that this type of modeling has achieved to reproduce and predict cell cycle dynamics. Furthermore, we present the challenge that this type of modeling is now ready to tackle: its integration with intracellular networks, and its formalisms, to understand crosstalks underlying systems level properties, ultimate aim of multi-scale models. Specifically, we discuss and illustrate how such an integration may be realized, by integrating a minimal logical model of the cell cycle with a metabolic network.

A Mechanistic Computational Model Reveals That Plasticity of CD4+ T Cell Differentiation Is a Function of Cytokine Composition and Dosage

“…Here, we explored the effect of the interplay among extracellular cytokines on differentiation of T cells and their plasticity. We have developed a logic-based computational model ( Helikar and Rogers, 2009 ; Helikar et al, 2012a , b , 2013 ; Naldi et al, 2015 ; Abou-Jaoudé et al, 2016 ; Barberis et al, 2017 ; Linke et al, 2017 ) of a signal transduction network that regulates the differentiation process of naive T cells to Th1, Th2, Th17, and iTreg cells and analyzed its dynamics. Local protein–protein regulatory information was manually curated to construct the mechanistic model that contains lineage-specifying TFs (Tbet, GATA3, RORγt, and Foxp3), various signal transducers and activators of transcription (STATs), and other signaling molecules.…”

Artificial Intelligence and Bioinformatics

“…Cyclic attractors have been shown in the cell cycle networks of Yeast or Mammals (see e.g. [Barberis et al, 2017;Traynard et al, 2016]). Attractors provide, more generally speaking, pointers to the possible steady functioning modes of the studied systems, either in normal conditions or under degraded conditions.…”

Quantitative Systems Biology to decipher design principles of a dynamic cell cycle network: the “Maximum Allowable mammalian Trade–Off–Weight” (MAmTOW)

“…Modeling of cell cycle regulation has a long tradition, and kinetic models for the cell cycle in yeasts are particularly advanced. 40 , 61 , 68 However, in this organism, alternative approaches such as qualitative modeling (also called Boolean models) have been employed to simulate cell cycle dynamics 67 , 69 – 71 , also examining the timing robustness of the process with respect to checkpoint conditions. 72 By comparison, fewer models exist that simulate the mammalian cell cycle 21 , 73 – 76 , for which the availability of quantitative data about protein concentrations, localization and kinetics is still a challenge.…”