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Bouguéon M, Legagneux V, Hazard O, Bomo J, Siegel A, Feret J, Théret N. A rule-based multiscale model of hepatic stellate cell plasticity: Critical role of the inactivation loop in fibrosis progression. PLoS Comput Biol 2024; 20:e1011858. [PMID: 39074160 PMCID: PMC11309422 DOI: 10.1371/journal.pcbi.1011858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 08/08/2024] [Accepted: 07/05/2024] [Indexed: 07/31/2024] Open
Abstract
Hepatic stellate cells (HSC) are the source of extracellular matrix (ECM) whose overproduction leads to fibrosis, a condition that impairs liver functions in chronic liver diseases. Understanding the dynamics of HSCs will provide insights needed to develop new therapeutic approaches. Few models of hepatic fibrosis have been proposed, and none of them include the heterogeneity of HSC phenotypes recently highlighted by single-cell RNA sequencing analyses. Here, we developed rule-based models to study HSC dynamics during fibrosis progression and reversion. We used the Kappa graph rewriting language, for which we used tokens and counters to overcome temporal explosion. HSCs are modeled as agents that present seven physiological cellular states and that interact with (TGFβ1) molecules which regulate HSC activation and the secretion of type I collagen, the main component of the ECM. Simulation studies revealed the critical role of the HSC inactivation process during fibrosis progression and reversion. While inactivation allows elimination of activated HSCs during reversion steps, reactivation loops of inactivated HSCs (iHSCs) are required to sustain fibrosis. Furthermore, we demonstrated the model's sensitivity to (TGFβ1) parameters, suggesting its adaptability to a variety of pathophysiological conditions for which levels of (TGFβ1) production associated with the inflammatory response differ. Using new experimental data from a mouse model of CCl4-induced liver fibrosis, we validated the predicted ECM dynamics. Our model also predicts the accumulation of iHSCs during chronic liver disease. By analyzing RNA sequencing data from patients with non-alcoholic steatohepatitis (NASH) associated with liver fibrosis, we confirmed this accumulation, identifying iHSCs as novel markers of fibrosis progression. Overall, our study provides the first model of HSC dynamics in chronic liver disease that can be used to explore the regulatory role of iHSCs in liver homeostasis. Moreover, our model can also be generalized to fibroblasts during repair and fibrosis in other tissues.
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Affiliation(s)
- Matthieu Bouguéon
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
| | | | - Octave Hazard
- École Polytechnique, Palaiseau, France
- DI-ENS (Inria, ÉNS, CNRS, PSL University), École normale supérieure, Paris, France
| | - Jérémy Bomo
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Jérôme Feret
- DI-ENS (Inria, ÉNS, CNRS, PSL University), École normale supérieure, Paris, France
- Team Antique, Inria, Paris, France
| | - Nathalie Théret
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
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Waites W, Mısırlı G, Cavaliere M, Danos V, Wipat A. A Genetic Circuit Compiler: Generating Combinatorial Genetic Circuits with Web Semantics and Inference. ACS Synth Biol 2018; 7:2812-2823. [PMID: 30408409 PMCID: PMC6305556 DOI: 10.1021/acssynbio.8b00201] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
A central strategy of synthetic biology is to understand the basic processes of living creatures through engineering organisms using the same building blocks. Biological machines described in terms of parts can be studied by computer simulation in any of several languages or robotically assembled in vitro. In this paper we present a language, the Genetic Circuit Description Language (GCDL) and a compiler, the Genetic Circuit Compiler (GCC). This language describes genetic circuits at a level of granularity appropriate both for automated assembly in the laboratory and deriving simulation code. The GCDL follows Semantic Web practice, and the compiler makes novel use of the logical inference facilities that are therefore available. We present the GCDL and compiler structure as a study of a tool for generating κ-language simulations from semantic descriptions of genetic circuits.
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Affiliation(s)
- William Waites
- School
of Informatics, University of Edinburgh, Edinburgh EH8 9YL, U.K.,E-mail:
| | - Göksel Mısırlı
- School
of Computing and Mathematics, Keele University, Newcastle ST5 5BG, U.K.
| | - Matteo Cavaliere
- School
of Computing & Mathematics, Manchester
Metropolitan University, Manchester M15 6BH, U.K.
| | - Vincent Danos
- School
of Informatics, University of Edinburgh, Edinburgh EH8 9YL, U.K.,École
Normale Supérieure, Paris, CNRS, 75005 Paris, France
| | - Anil Wipat
- School
of Computing Science, Newcastle University, Newcastle upon Tyne NE1
7RU, U.K.
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Forbes AG, Burks A, Lee K, Li X, Boutillier P, Krivine J, Fontana W. Dynamic Influence Networks for Rule-Based Models. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2018; 24:184-194. [PMID: 28866584 DOI: 10.1109/tvcg.2017.2745280] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.
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