What is a Cell Cycle Checkpoint? The TotemBioNet Answer

Boyenval, Déborah; Bernot, Gilles; Collavizza, Hélène; Comet, Jean‐Paul

doi:10.1007/978-3-030-60327-4_21

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…In this work, we made use of two software programs dedicated to qualitative modeling of regulation networks: TotemBioNet [11,12] (Available now at https://gitlab.com/ totembionet/totembionet, accessed on 30 May 2021)) and DyMBioNet [13]. Both inherit from SMBioNet [6] as they rely on intensive CTL model checking to validate the parameter settings with respect to the phenotypic knowledge (and they additionally handle fair-path CTL, another temporal logic better suited for biological knowledge of global behavior).…”

Section: Software Platforms For Validationmentioning

confidence: 99%

“…We made use of two software programs dedicated to qualitative modeling of regulation networks: TotemBioNet [11,12] and DyMBioNet [13]. Both inherit from SMBioNet [6] as they rely on intensive model checking to validate parameter settings with respect to phenotypic knowledge (and they additionally handle fair-path CTL, as described in Section 6).…”

Section: Computer-aided Validation Of the Model Dynamicsmentioning

confidence: 99%

“…We take benefit of the power of coupling, on the one hand, an extension of the R. Thomas' modeling approach [8,9] with the additional expressiveness of multiplexes [10], and on the other hand, formal methods from computer science. This power is made effective through the associated platforms TotemBioNet [11,12] and DyMBioNet [13]. Here, this approach is used for the first time to design and formally validate a regulation model of the cell energetic metabolism, even if a precursory model has been introduced earlier in [14].…”

Section: Introductionmentioning

confidence: 99%

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Regulation of Eukaryote Metabolism: An Abstract Model Explaining the Warburg/Crabtree Effect

et al. 2021

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Adaptation of metabolism is a response of many eukaryotic cells to nutrient heterogeneity in the cell microenvironment. One of these adaptations is the shift from respiratory to fermentative metabolism, also called the Warburg/Crabtree effect. It is a response to a very high nutrient increase in the cell microenvironment, even in the presence of oxygen. Understanding whether this metabolic transition can result from basic regulation signals between components of the central carbon metabolism are the the core question of this work. We use an extension of the René Thomas modeling framework for representing the regulations between the main catabolic and anabolic pathways of eukaryotic cells, and formal methods for confronting models with known biological properties in different microenvironments. The formal model of the regulation of eukaryote metabolism defined and validated here reveals the conditions under which this metabolic phenotype switch occurs. It clearly proves that currently known regulating signals within the main components of central carbon metabolism can be sufficient to bring out the Warburg/Crabtree effect. Moreover, this model offers a general perspective of the regulation of the central carbon metabolism that can be used to study other biological questions.

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Section: Software Platforms For Validationmentioning

confidence: 99%

Section: Computer-aided Validation Of the Model Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regulation of Eukaryote Metabolism: An Abstract Model Explaining the Warburg/Crabtree Effect

et al. 2021

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“…In fact, the dynamical properties do not only depend on the topology of the influence graph, but also depend on the dynamical model used to model the gene regulatory network. In the literature, different modeling frameworks have been applied to model gene regulatory networks, mainly continuous models [3,4,13], discrete models [14,32,33,15,6,7] and hybrid models [5,10,31,9]. This work is based on Thomas' discrete modeling framework of gene regulatory networks [32,33], where the continuous expression values of all genes in the system are abstracted by a vector of integers, called discrete state, describing the discrete expression levels of all genes, and the system's dynamics are then captured by the transitions between discrete states.…”

Section: Introductionmentioning

confidence: 99%

Condition for Periodic Attractor in 4-Dimensional Repressilators

Sun,

Folschette,

Magnin

2023

Lecture Notes in Computer Science

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One of the key questions about gene regulatory networks is how to predict complex dynamical properties based on the influence graph's topology. Earlier theoretical studies have identified conditions for complex dynamical properties, like multistability or oscillations, based on topological features, like the presence of a positive (negative) feedback loop. This work follows this path and aims to find a sufficient and necessary condition for the existence of a periodic attractor in 4-dimensional (4 genes) repressilators based on a discrete modeling framework under some dynamical assumptions. These networks are extensions of the widely studied 3-dimensional repressilator, which has been used in synthetic biology to produce synthetic oscillations. While other researchers have explored specific extensions of the 3-dimensional repressilator to improve synthetic oscillation control, our work investigates all 4-dimensional networks with only inhibitions. By uncovering new insights about periodic attractors in these small networks, our findings could aid the design of new synthetic oscillations. We search for condition for period attractor in an exhaustive manner with the guide of a decision tree model. Our major contributions include: 1) discovering that, with one exception, the relations between gene regulation thresholds do not impact the existence of periodic attractors in any of the influence graphs considered in this study; 2) identifying a sufficient and necessary condition of simple form for the existence of a periodic attractor when the exception is ignored; 3) identifying new topological features of influence graphs that are necessary for predicting the existence of periodic attractor in 4-dimensional repressilators.

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