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Karlebach G, Robinson PN. Computing Minimal Boolean Models of Gene Regulatory Networks. J Comput Biol 2024; 31:117-127. [PMID: 37889991 DOI: 10.1089/cmb.2023.0122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/29/2023] Open
Abstract
Models of gene regulatory networks (GRNs) capture the dynamics of the regulatory processes that occur within the cell as a means to understanding the variability observed in gene expression between different conditions. Arguably the simplest mathematical construct used for modeling is the Boolean network, which dictates a set of logical rules for transition between states described as Boolean vectors. Due to the complexity of gene regulation and the limitations of experimental technologies, in most cases knowledge about regulatory interactions and Boolean states is partial. In addition, the logical rules themselves are not known a priori. Our goal in this work is to create an algorithm that finds the network that fits the data optimally, and identify the network states that correspond to the noise-free data. We present a novel methodology for integrating experimental data and performing a search for the optimal consistent structure via optimization of a linear objective function under a set of linear constraints. In addition, we extend our methodology into a heuristic that alleviates the computational complexity of the problem for datasets that are generated by single-cell RNA-Sequencing (scRNA-Seq). We demonstrate the effectiveness of these tools using simulated data, and in addition a publicly available scRNA-Seq dataset and the GRN that is associated with it. Our methodology will enable researchers to obtain a better understanding of the dynamics of GRNs and their biological role.
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Affiliation(s)
- Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
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Yordanov B, Dunn SJ, Gravill C, Arora H, Kugler H, Wintersteiger CM. The Reasoning Engine: A Satisfiability Modulo Theories-Based Framework for Reasoning About Discrete Biological Models. J Comput Biol 2023; 30:1046-1058. [PMID: 37733940 DOI: 10.1089/cmb.2023.0117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023] Open
Abstract
We present a framework called the Reasoning Engine, which implements Satisfiability Modulo Theories (SMT)-based methods within a unified computational environment to address diverse biological analysis problems. The Reasoning Engine was used to reproduce results from key scientific studies, as well as supporting new research in stem cell biology. The framework utilizes an intermediate language for encoding partially specified discrete dynamical systems, which bridges the gap between high-level domain-specific languages and low-level SMT solvers. We provide this framework as open source together with various biological case studies, illustrating the synthesis, enumeration, optimization, and reasoning over models consistent with experimental observations to reveal novel biological insights.
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Affiliation(s)
| | | | | | - Himanshu Arora
- Bar-Ilan University, Faculty of Engineering, Ramat Gan, Israel
| | - Hillel Kugler
- Bar-Ilan University, Faculty of Engineering, Ramat Gan, Israel
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Amar A, Hubbard EJA, Kugler H. Modeling the C. elegans germline stem cell genetic network using automated reasoning. Biosystems 2022; 217:104672. [PMID: 35469833 PMCID: PMC9142837 DOI: 10.1016/j.biosystems.2022.104672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 03/31/2022] [Accepted: 04/04/2022] [Indexed: 11/18/2022]
Abstract
Computational methods and tools are a powerful complementary approach to experimental work for studying regulatory interactions in living cells and systems. We demonstrate the use of formal reasoning methods as applied to the Caenorhabditis elegans germ line, which is an accessible system for stem cell research. The dynamics of the underlying genetic networks and their potential regulatory interactions are key for understanding mechanisms that control cellular decision-making between stem cells and differentiation. We model the “stem cell fate” versus entry into the “meiotic development” pathway decision circuit in the young adult germ line based on an extensive study of published experimental data and known/hypothesized genetic interactions. We apply a formal reasoning framework to derive predictive networks for control of differentiation. Using this approach we simultaneously specify many possible scenarios and experiments together with potential genetic interactions, and synthesize genetic networks consistent with all encoded experimental observations. In silico analysis of knock-down and overexpression experiments within our model recapitulate published phenotypes of mutant animals and can be applied to make predictions on cellular decision-making. A methodological contribution of this work is demonstrating how to effectively model within a formal reasoning framework a complex genetic network with a wealth of known experimental data and constraints. We provide a summary of the steps we have found useful for the development and analysis of this model and can potentially be applicable to other genetic networks. This work also lays a foundation for developing realistic whole tissue models of the C. elegans germ line where each cell in the model will execute a synthesized genetic network.
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Affiliation(s)
- Ani Amar
- The Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel.
| | - E Jane Albert Hubbard
- Skirball Institute of Biomolecular Medicine, Department of Cell Biology, Department of Pathology, NYU Grossman School of Medicine, 540 First Avenue, New York, NY 10016, United States of America.
| | - Hillel Kugler
- The Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel.
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Mishra A, Oulès B, Pisco AO, Ly T, Liakath-Ali K, Walko G, Viswanathan P, Tihy M, Nijjher J, Dunn SJ, Lamond AI, Watt FM. A protein phosphatase network controls the temporal and spatial dynamics of differentiation commitment in human epidermis. eLife 2017; 6:27356. [PMID: 29043977 PMCID: PMC5667932 DOI: 10.7554/elife.27356] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Accepted: 10/10/2017] [Indexed: 12/12/2022] Open
Abstract
Epidermal homeostasis depends on a balance between stem cell renewal and terminal differentiation. The transition between the two cell states, termed commitment, is poorly understood. Here, we characterise commitment by integrating transcriptomic and proteomic data from disaggregated primary human keratinocytes held in suspension to induce differentiation. Cell detachment induces several protein phosphatases, five of which - DUSP6, PPTC7, PTPN1, PTPN13 and PPP3CA – promote differentiation by negatively regulating ERK MAPK and positively regulating AP1 transcription factors. Conversely, DUSP10 expression antagonises commitment. The phosphatases form a dynamic network of transient positive and negative interactions that change over time, with DUSP6 predominating at commitment. Boolean network modelling identifies a mandatory switch between two stable states (stem and differentiated) via an unstable (committed) state. Phosphatase expression is also spatially regulated in vivo and in vitro. We conclude that an auto-regulatory phosphatase network maintains epidermal homeostasis by controlling the onset and duration of commitment.
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Affiliation(s)
- Ajay Mishra
- Centre for Stem Cells and Regenerative Medicine, King's College London, London, United Kingdom.,Department of Chemical Engineering and Biotechnology, Cambridge Infinitus Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Bénédicte Oulès
- Centre for Stem Cells and Regenerative Medicine, King's College London, London, United Kingdom
| | - Angela Oliveira Pisco
- Centre for Stem Cells and Regenerative Medicine, King's College London, London, United Kingdom
| | - Tony Ly
- Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom.,Wellcome Centre for Cell Biology, Institute of Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Gernot Walko
- Centre for Stem Cells and Regenerative Medicine, King's College London, London, United Kingdom
| | | | - Matthieu Tihy
- Centre for Stem Cells and Regenerative Medicine, King's College London, London, United Kingdom.,Laboratory of Cerebral Physiology, Université Paris Descartes, Paris, France
| | - Jagdeesh Nijjher
- Centre for Stem Cells and Regenerative Medicine, King's College London, London, United Kingdom
| | - Sara-Jane Dunn
- Microsoft Research, Cambridge, United Kingdom.,Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Angus I Lamond
- Centre for Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, United Kingdom
| | - Fiona M Watt
- Centre for Stem Cells and Regenerative Medicine, King's College London, London, United Kingdom
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