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Dávila-Velderrain J, Caldú-Primo JL, Martínez-García JC, Álvarez-Buylla Roces ME. Gene Regulatory Network Dynamical Logical Models for Plant Development. Methods Mol Biol 2022; 2395:59-77. [PMID: 34822149 DOI: 10.1007/978-1-0716-1816-5_4] [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: 06/13/2023]
Abstract
Mathematical and computational approaches that integrate and model the concerted action of multiple genetic and nongenetic components holding highly nonlinear interactions are fundamental for the study of developmental processes. Among these, gene regulatory network (GRN) dynamical models are very useful to understand how diverse types of regulatory constraints restrict the multigene expression patterns that characterize different cell fates. In this chapter we present a hands-on approach to model GRN dynamics, taking as a working example a well-curated and experimentally grounded GRN developmental module proposed by our group: the flower organ specification gene regulatory network (FOS-GRN). We demonstrate how to build and analyze a GRN model according to the following steps: (1) integration of molecular genetic data and formulation of logical rules specifying the dynamic behavior of each gene; (2) determination of steady states (attractors) corresponding to each cell type; (3) validation of the GRN model; and (4) extension of the deterministic model with the inclusion of stochasticity in order to model cell-state transitions dependent on noise due to fluctuations of the involved gen products. The methodologies explained here in detail can be applied to any other developmental module.
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Affiliation(s)
- José Dávila-Velderrain
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - José Luis Caldú-Primo
- Laboratorio de Genética Molecular, Epigenética, Desarrollo y Evolución de Plantas, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad Universitaria, CDMX, Coyoacán, México
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad Universitaria, CDMX, México
| | | | - María Elena Álvarez-Buylla Roces
- Laboratorio de Genética Molecular, Epigenética, Desarrollo y Evolución de Plantas, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad Universitaria, CDMX, Coyoacán, México.
- Centro de Ciencias de la Complejidad (C3), Universidad Nacional Autónoma de México, Ciudad Universitaria, CDMX, México.
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On Computing Structural and Behavioral Complexities of Threshold Boolean Networks : Application to Biological Networks. Acta Biotheor 2020; 68:119-138. [PMID: 31446519 DOI: 10.1007/s10441-019-09358-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 08/08/2019] [Indexed: 10/26/2022]
Abstract
Various threshold Boolean networks (TBNs), a formalism used to model different types of biological networks (genes notably), can produce similar dynamics, i.e. share same behaviors. Among them, some are complex (according to Kolmogorov complexity), others not. By computing both structural and behavioral complexities, we show that most TBNs are structurally complex, even those having simple behaviors. For this purpose, we developed a new method to compute the structural complexity of a TBN based on estimates of the sizes of equivalence classes of the threshold Boolean functions composing the TBN.
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Marín de Mas I, Fanchon E, Papp B, Kalko S, Roca J, Cascante M. Molecular mechanisms underlying COPD-muscle dysfunction unveiled through a systems medicine approach. Bioinformatics 2016; 33:95-103. [PMID: 27794560 DOI: 10.1093/bioinformatics/btw566] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 08/26/2016] [Accepted: 08/29/2016] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION Skeletal muscle dysfunction is a systemic effect in one-third of patients with chronic obstructive pulmonary disease (COPD), characterized by high reactive-oxygen-species (ROS) production and abnormal endurance training-induced adaptive changes. However, the role of ROS in COPD remains unclear, not least because of the lack of appropriate tools to study multifactorial diseases. RESULTS We describe a discrete model-driven method combining mechanistic and probabilistic approaches to decipher the role of ROS on the activity state of skeletal muscle regulatory network, assessed before and after an 8-week endurance training program in COPD patients and healthy subjects. In COPD, our computational analysis indicates abnormal training-induced regulatory responses leading to defective tissue remodeling and abnormal energy metabolism. Moreover, we identified tnf, insr, inha and myc as key regulators of abnormal training-induced adaptations in COPD. The tnf-insr pair was identified as a promising target for therapeutic interventions. Our work sheds new light on skeletal muscle dysfunction in COPD, opening new avenues for cost-effective therapies. It overcomes limitations of previous computational approaches showing high potential for the study of other multi-factorial diseases such as diabetes or cancer. CONTACT jroca@clinic.ub.es or martacascante@ub.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Igor Marín de Mas
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Institute of Biomedicine of University of Barcelona (IBUB) and IDIBAPS, Diagonal 645, Barcelona 08028, Spain.,Institut d' Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona 08028, Spain.,Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Eric Fanchon
- Université Grenoble Alpes-CNRS, TIMC-IMAG UMR 5525, Faculté de Médecine, Grenoble 38041, France
| | - Balázs Papp
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Susana Kalko
- Bioinformatics Core Facility, IDIBAPS-CEK, Hospital Clínic, University de Barcelona, Barcelona 08036, Spain
| | - Josep Roca
- Institut d' Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona 08028, Spain.,Department of Pulmonary Medicine, Hospital Clínic, IDIBAPS, CIBERES, Universitat de Barcelona, Barcelona 08036, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Institute of Biomedicine of University of Barcelona (IBUB) and IDIBAPS, Diagonal 645, Barcelona 08028, Spain.,Institut d' Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona 08028, Spain
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Biane C, Delaplace F, Klaudel H. Networks and games for precision medicine. Biosystems 2016; 150:52-60. [PMID: 27543134 DOI: 10.1016/j.biosystems.2016.08.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 07/20/2016] [Accepted: 08/11/2016] [Indexed: 12/13/2022]
Abstract
Recent advances in omics technologies provide the leverage for the emergence of precision medicine that aims at personalizing therapy to patient. In this undertaking, computational methods play a central role for assisting physicians in their clinical decision-making by combining data analysis and systems biology modelling. Complex diseases such as cancer or diabetes arise from the intricate interplay of various biological molecules. Therefore, assessing drug efficiency requires to study the effects of elementary perturbations caused by diseases on relevant biological networks. In this paper, we propose a computational framework called Network-Action Game applied to best drug selection problem combining Game Theory and discrete models of dynamics (Boolean networks). Decision-making is modelled using Game Theory that defines the process of drug selection among alternative possibilities, while Boolean networks are used to model the effects of the interplay between disease and drugs actions on the patient's molecular system. The actions/strategies of disease and drugs are focused on arc alterations of the interactome. The efficiency of this framework has been evaluated for drug prediction on a model of breast cancer signalling.
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Affiliation(s)
- Célia Biane
- IBISC Laboratory, Evry Val d'Essonne University, Evry, France.
| | | | - Hanna Klaudel
- IBISC Laboratory, Evry Val d'Essonne University, Evry, France.
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Yordanov B, Dunn SJ, Kugler H, Smith A, Martello G, Emmott S. A Method to Identify and Analyze Biological Programs through Automated Reasoning. NPJ Syst Biol Appl 2016; 2. [PMID: 27668090 PMCID: PMC5034891 DOI: 10.1038/npjsba.2016.10] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich, but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function.
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Affiliation(s)
- Boyan Yordanov
- Microsoft Research, 21 Station Road, Cambridge, CB1 2FB, UK
| | - Sara-Jane Dunn
- Microsoft Research, 21 Station Road, Cambridge, CB1 2FB, UK
| | - Hillel Kugler
- Microsoft Research, 21 Station Road, Cambridge, CB1 2FB, UK.,Faculty of Engineering, Bar-Ilan University, Ramat Gan, 5290002, Israel
| | - Austin Smith
- Wellcome Trust Medical Research Council Cambridge Stem Cell Institute, University of Cambridge CB2 1QR, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Graziano Martello
- Dept. of Molecular Medicine, Complesso Vallisneri - 3 Piano Nord, University of Padua, Viale G. Colombo 3, 35131 Padua, Italy
| | - Stephen Emmott
- Microsoft Research, 21 Station Road, Cambridge, CB1 2FB, UK.,Faculty of Engineering Science, University College London, Torrington Place, London WC1E 7JE, UK
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Formal Methods for Hopfield-Like Networks. Acta Biotheor 2013; 61:21-39. [PMID: 23381497 DOI: 10.1007/s10441-013-9169-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Accepted: 01/07/2013] [Indexed: 10/27/2022]
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Carrillo M, Góngora PA, Rosenblueth DA. An overview of existing modeling tools making use of model checking in the analysis of biochemical networks. FRONTIERS IN PLANT SCIENCE 2012; 3:155. [PMID: 22833747 PMCID: PMC3400939 DOI: 10.3389/fpls.2012.00155] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2012] [Accepted: 06/24/2012] [Indexed: 05/24/2023]
Abstract
Model checking is a well-established technique for automatically verifying complex systems. Recently, model checkers have appeared in computer tools for the analysis of biochemical (and gene regulatory) networks. We survey several such tools to assess the potential of model checking in computational biology. Next, our overview focuses on direct applications of existing model checkers, as well as on algorithms for biochemical network analysis influenced by model checking, such as those using binary decision diagrams (BDDs) or Boolean-satisfiability solvers. We conclude with advantages and drawbacks of model checking for the analysis of biochemical networks.
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Affiliation(s)
| | | | - David A. Rosenblueth
- *Correspondence: David A. Rosenblueth, Departamento de Ciencias de la Computación, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Apdo. 20-726, 01000 México D.F., México. e-mail:
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Arellano G, Argil J, Azpeitia E, Benítez M, Carrillo M, Góngora P, Rosenblueth DA, Alvarez-Buylla ER. "Antelope": a hybrid-logic model checker for branching-time Boolean GRN analysis. BMC Bioinformatics 2011; 12:490. [PMID: 22192526 PMCID: PMC3316443 DOI: 10.1186/1471-2105-12-490] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2011] [Accepted: 12/22/2011] [Indexed: 01/30/2023] Open
Abstract
Background In Thomas' formalism for modeling gene regulatory networks (GRNs), branching time, where a state can have more than one possible future, plays a prominent role. By representing a certain degree of unpredictability, branching time can model several important phenomena, such as (a) asynchrony, (b) incompletely specified behavior, and (c) interaction with the environment. Introducing more than one possible future for a state, however, creates a difficulty for ordinary simulators, because infinitely many paths may appear, limiting ordinary simulators to statistical conclusions. Model checkers for branching time, by contrast, are able to prove properties in the presence of infinitely many paths. Results We have developed Antelope ("Analysis of Networks through TEmporal-LOgic sPEcifications", http://turing.iimas.unam.mx:8080/AntelopeWEB/), a model checker for analyzing and constructing Boolean GRNs. Currently, software systems for Boolean GRNs use branching time almost exclusively for asynchrony. Antelope, by contrast, also uses branching time for incompletely specified behavior and environment interaction. We show the usefulness of modeling these two phenomena in the development of a Boolean GRN of the Arabidopsis thaliana root stem cell niche. There are two obstacles to a direct approach when applying model checking to Boolean GRN analysis. First, ordinary model checkers normally only verify whether or not a given set of model states has a given property. In comparison, a model checker for Boolean GRNs is preferable if it reports the set of states having a desired property. Second, for efficiency, the expressiveness of many model checkers is limited, resulting in the inability to express some interesting properties of Boolean GRNs. Antelope tries to overcome these two drawbacks: Apart from reporting the set of all states having a given property, our model checker can express, at the expense of efficiency, some properties that ordinary model checkers (e.g., NuSMV) cannot. This additional expressiveness is achieved by employing a logic extending the standard Computation-Tree Logic (CTL) with hybrid-logic operators. Conclusions We illustrate the advantages of Antelope when (a) modeling incomplete networks and environment interaction, (b) exhibiting the set of all states having a given property, and (c) representing Boolean GRN properties with hybrid CTL.
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Affiliation(s)
- Gustavo Arellano
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 01000 México D.F., México
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Demongeot J, Elena A, Noual M, Sené S, Thuderoz F. "Immunetworks", intersecting circuits and dynamics. J Theor Biol 2011; 280:19-33. [PMID: 21439971 DOI: 10.1016/j.jtbi.2011.03.023] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2010] [Revised: 03/17/2011] [Accepted: 03/17/2011] [Indexed: 12/21/2022]
Abstract
This paper proposes a study of biological regulation networks based on a multi-level strategy. Given a network, the first structural level of this strategy consists in analysing the architecture of the network interactions in order to describe it. The second dynamical level consists in relating the patterns found in the architecture to the possible dynamical behaviours of the network. It is known that circuits are the patterns that play the most important part in the dynamics of a network in the sense that they are responsible for the diversity of its asymptotic behaviours. Here, we pursue further this idea and argue that beyond the influence of underlying circuits, intersections of circuits also impact significantly on the dynamics of a network and thus need to be payed special attention to. For some genetic regulation networks involved in the control of the immune system ("immunetworks"), we show that the small number of attractors can be explained by the presence, in the underlying structures of these networks, of intersecting circuits that "inter-lock".
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Affiliation(s)
- Jacques Demongeot
- Université Joseph Fourier de Grenoble, AGIM, CNRS FRE 3405, 38700 La Tronche, France
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Batt G, Page M, Cantone I, Goessler G, Monteiro P, de Jong H. Efficient parameter search for qualitative models of regulatory networks using symbolic model checking. ACTA ACUST UNITED AC 2010; 26:i603-10. [PMID: 20823328 PMCID: PMC2935427 DOI: 10.1093/bioinformatics/btq387] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Motivation: Investigating the relation between the structure and behavior of complex biological networks often involves posing the question if the hypothesized structure of a regulatory network is consistent with the observed behavior, or if a proposed structure can generate a desired behavior. Results: The above questions can be cast into a parameter search problem for qualitative models of regulatory networks. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark datasets. We test the consistency between IRMA and time-series expression profiles, and search for parameter modifications that would make the external control of the system behavior more robust. Availability: GNA and the IRMA model are available at http://ibis.inrialpes.fr/ Contact:gregory.batt@inria.fr Supplementary information:Supplementary data are available at Bioinformatics online.
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Corblin F, Fanchon E, Trilling L. Applications of a formal approach to decipher discrete genetic networks. BMC Bioinformatics 2010; 11:385. [PMID: 20646302 PMCID: PMC2918581 DOI: 10.1186/1471-2105-11-385] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2010] [Accepted: 07/20/2010] [Indexed: 11/25/2022] Open
Abstract
Background A growing demand for tools to assist the building and analysis of biological networks exists in systems biology. We argue that the use of a formal approach is relevant and applicable to address questions raised by biologists about such networks. The behaviour of these systems being complex, it is essential to exploit efficiently every bit of experimental information. In our approach, both the evolution rules and the partial knowledge about the structure and the behaviour of the network are formalized using a common constraint-based language. Results In this article our formal and declarative approach is applied to three biological applications. The software environment that we developed allows to specifically address each application through a new class of biologically relevant queries. We show that we can describe easily and in a formal manner the partial knowledge about a genetic network. Moreover we show that this environment, based on a constraint algorithmic approach, offers a wide variety of functionalities, going beyond simple simulations, such as proof of consistency, model revision, prediction of properties, search for minimal models relatively to specified criteria. Conclusions The formal approach proposed here deeply changes the way to proceed in the exploration of genetic and biochemical networks, first by avoiding the usual trial-and-error procedure, and second by placing the emphasis on sets of solutions, rather than a single solution arbitrarily chosen among many others. Last, the constraint approach promotes an integration of model and experimental data in a single framework.
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Affiliation(s)
- Fabien Corblin
- Laboratoire TIMC-IMAG, UMR CNRS/UJF 5525, Domaine de la Merci, 38710 La Tronche, France.
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