Cooperative development of logical modelling standards and tools with CoLoMoTo

Naldi, Aurélien; Monteiro, Pedro T.; Müssel, Christoph; Kestler, Hans A.; Thieffry, Denis; Xénarios, Ioannis; Sáez-Rodríguez, Julio; Helikar, Tomáš; Chaouiya, Claudine

doi:10.1101/010504

Cited by 18 publications

(17 citation statements)

References 90 publications

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“…New packages can be developed independently, within dedicated communities, at a pace that suits them. This was the case for logical modeling with the CoLoMoTo community (Naldi et al, 2015), constraint-based modeling within the COBRA community (Heirendt et al, 2019), and rulebased modeling with a community of like-minded software creators (Faeder et al, 2009;Zhang et al, 2013;Palmisano et al, 2014;Boutillier et al, 2018).…”

Section: Sbml Level 3's Modularity and Breadthmentioning

confidence: 99%

“…This has led to better awareness and communication within groups interested in specific modeling frameworks. A good example is the CoLoMoTo effort mentioned above; it was launched by researchers who needed a format to exchange qualitative models between their software tools and developed the Qualitative Modeling package for SBML (Naldi et al, 2015) as the solution. Nevertheless, challenges remain, as discussed in the next section.…”

Section: Impact Of Sbmlmentioning

confidence: 99%

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SBMLLevel 3: an extensible format for the exchange and reuse of biological models

Keating¹,

Waltemath²,

König³

et al. 2020

Molecular Systems Biology

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238

202

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Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

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Section: Sbml Level 3's Modularity and Breadthmentioning

confidence: 99%

Section: Impact Of Sbmlmentioning

confidence: 99%

SBMLLevel 3: an extensible format for the exchange and reuse of biological models

Keating¹,

Waltemath²,

König³

et al. 2020

Molecular Systems Biology

Self Cite

238

202

View full text Add to dashboard Cite

show abstract

“…Different formalisms have been commonly used to model complex biological networks, with different levels of abstraction (de Jong, 2002 ; Karlebach and Shamir, 2008 ; Albert et al, 2013 ; Samaga and Klamt, 2013 ). Among these formalisms, the discrete, logical approach is particularly useful to model biological systems for which detailed kinetic data are lacking, which is often the case (Bornholdt, 2008 ; Wang et al, 2012 ; Naldi et al, 2014 ). Moreover, logical modeling allows the consideration and the dynamical analysis of comprehensive signaling/regulatory networks.…”

Section: Introductionmentioning

confidence: 99%

Model Checking to Assess T-Helper Cell Plasticity

Abou-Jaoudé

Monteiro

Naldi

et al. 2015

Front. Bioeng. Biotechnol.

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Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation. We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.

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“…Interaction between genes can then be mathematically modeled by Boolean functions. A set of such logical rules or functions, more exactly one Boolean function per considered gene defines a Boolean network (BN) [ 4 , 5 ]. Given some initial expression pattern, a BN computes the evolution of gene expression in discrete time steps.…”

Section: Introductionmentioning

confidence: 99%

Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty

et al. 2015

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Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene’s state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.

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Cooperative development of logical modelling standards and tools with CoLoMoTo

Cited by 18 publications

References 90 publications

SBMLLevel 3: an extensible format for the exchange and reuse of biological models

SBMLLevel 3: an extensible format for the exchange and reuse of biological models

Model Checking to Assess T-Helper Cell Plasticity

Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty

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