1
|
Thomas C, Cosme M, Gaucherel C, Pommereau F. Model-checking ecological state-transition graphs. PLoS Comput Biol 2022; 18:e1009657. [PMID: 35666771 PMCID: PMC9203009 DOI: 10.1371/journal.pcbi.1009657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 06/16/2022] [Accepted: 05/08/2022] [Indexed: 11/18/2022] Open
Abstract
Model-checking is a methodology developed in computer science to automatically assess the dynamics of discrete systems, by checking if a system modelled as a state-transition graph satisfies a dynamical property written as a temporal logic formula. The dynamics of ecosystems have been drawn as state-transition graphs for more than a century, ranging from state-and-transition models to assembly graphs. Model-checking can provide insights into both empirical data and theoretical models, as long as they sum up into state-transition graphs. While model-checking proved to be a valuable tool in systems biology, it remains largely underused in ecology apart from precursory applications. This article proposes to address this situation, through an inventory of existing ecological STGs and an accessible presentation of the model-checking methodology. This overview is illustrated by the application of model-checking to assess the dynamics of a vegetation pathways model. We select management scenarios by model-checking Computation Tree Logic formulas representing management goals and built from a proposed catalogue of patterns. In discussion, we sketch bridges between existing studies in ecology and available model-checking frameworks. In addition to the automated analysis of ecological state-transition graphs, we believe that defining ecological concepts with temporal logics could help clarify and compare them.
Collapse
Affiliation(s)
- Colin Thomas
- IBISC, Univ. Évry, Univ. Paris-Saclay, 91020 Évry-Courcouronne, France
- AMAP, Univ. Montpellier, INRAE, CIRAD, CNRS, IRD, Montpellier, France
| | - Maximilien Cosme
- AMAP, Univ. Montpellier, INRAE, CIRAD, CNRS, IRD, Montpellier, France
| | - Cédric Gaucherel
- AMAP, Univ. Montpellier, INRAE, CIRAD, CNRS, IRD, Montpellier, France
| | - Franck Pommereau
- IBISC, Univ. Évry, Univ. Paris-Saclay, 91020 Évry-Courcouronne, France
| |
Collapse
|
2
|
Gouveia F, Lynce I, Monteiro PT. Revision of Boolean Models of Regulatory Networks Using Stable State Observations. J Comput Biol 2020; 27:144-155. [PMID: 31794671 DOI: 10.1089/cmb.2019.0289] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Models of biological regulatory networks are essential to understand cellular processes. However, the definition of such models is still mostly manually performed, and consequently prone to error. Moreover, as new experimental data are acquired, models need to be revised and updated. Here, we propose a model revision procedure and associated tool, capable of providing the set of minimal repairs to render a model consistent with a set of experimental observations. We consider four possible repair operations, using a lexicographic optimization criterion, giving preference to function repairs over topological ones. Also, we consider observations at stable state discarding the model dynamics. In this article, we extend our previous work to tackle the problem of repairing nodes with multiple reasons of inconsistency. We evaluate our tool on five publicly available logical models. We perform random changes considering several parameter configurations to assess the tool repairing capabilities. Whenever a model is repaired under the time limit, the tool successfully produces the optimal solutions to repair the model. Instances were generated without the previous limitation to validate this extended approach.
Collapse
Affiliation(s)
- Filipe Gouveia
- Department of Computer Science and Engineering, INESC-ID/Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Inês Lynce
- Department of Computer Science and Engineering, INESC-ID/Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Pedro T Monteiro
- Department of Computer Science and Engineering, INESC-ID/Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| |
Collapse
|
3
|
Liang Y, Kelemen A. Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications. Brief Bioinform 2019; 19:1051-1068. [PMID: 28430854 DOI: 10.1093/bib/bbx036] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Indexed: 12/23/2022] Open
Abstract
Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited. Urgently needed are rapid and inexpensive computational and statistical methods that can identify interacting candidate disease genes or drug targets out of thousands that can be further investigated or validated by experimentations. Moreover, identifying biological dynamic systems, and simultaneously estimating the important kinetic structural and functional parameters, which may not be experimentally accessible could be important directions for drug-disease-gene network studies. In this article, we present an overview and comparison of recent developments of dynamic modeling and network approaches for time-course omics data, and their applications to various biological systems, health conditions and disease statuses. Moreover, various data reduction and analytical schemes ranging from mathematical to computational to statistical methods are compared including their merits, drawbacks and limitations. The most recent software, associated web resources and other potentials for the compared methods are also presented and discussed in detail.
Collapse
Affiliation(s)
- Yulan Liang
- Department of Family and Community Health, University of Maryland, Baltimore, MD, USA
| | - Arpad Kelemen
- Department of Family and Community Health, University of Maryland, Baltimore, MD, USA
| |
Collapse
|
4
|
Saeed MT, Ahmad J, Baumbach J, Pauling J, Shafi A, Paracha RZ, Hayat A, Ali A. Parameter estimation of qualitative biological regulatory networks on high performance computing hardware. BMC SYSTEMS BIOLOGY 2018; 12:146. [PMID: 30594246 PMCID: PMC6311083 DOI: 10.1186/s12918-018-0670-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 12/04/2018] [Indexed: 12/28/2022]
Abstract
BACKGROUND Biological Regulatory Networks (BRNs) are responsible for developmental and maintenance related functions in organisms. These functions are implemented by the dynamics of BRNs and are sensitive to regulations enforced by specific activators and inhibitors. The logical modeling formalism by René Thomas incorporates this sensitivity with a set of logical parameters modulated by available regulators, varying with time. With the increase in complexity of BRNs in terms of number of entities and their interactions, the task of parameters estimation becomes computationally expensive with existing sequential SMBioNET tool. We extend the existing sequential implementation of SMBioNET by using a data decomposition approach using a Java messaging library called MPJ Express. The approach divides the parameters space into different regions and each region is then explored in parallel on High Performance Computing (HPC) hardware. RESULTS The performance of the parallel approach is evaluated on BRNs of different sizes, and experimental results on multicore and cluster computers showed almost linear speed-up. This parallel code can be executed on a wide range of concurrent hardware including laptops equipped with multicore processors, and specialized distributed memory computer systems. To demonstrate the application of parallel implementation, we selected a case study of Hexosamine Biosynthetic Pathway (HBP) in cancer progression to identify potential therapeutic targets against cancer. A set of logical parameters were computed for HBP model that directs the biological system to a state of recovery. Furthermore, the parameters also suggest a potential therapeutic intervention that restores homeostasis. Additionally, the performance of parallel application was also evaluated on a network (comprising of 23 entities) of Fibroblast Growth Factor Signalling in Drosophila melanogaster. CONCLUSIONS Qualitative modeling framework is widely used for investigating dynamics of biological regulatory networks. However, computation of model parameters in qualitative modeling is computationally intensive. In this work, we presented results of our Java based parallel implementation that provides almost linear speed-up on both multicore and cluster platforms. The parallel implementation is available at https://psmbionet.github.io .
Collapse
Affiliation(s)
- Muhammad Tariq Saeed
- Research Centre for Modeling and Simulation (RCMS), NUST, Islamabad, 44000, Pakistan
| | - Jamil Ahmad
- Research Centre for Modeling and Simulation (RCMS), NUST, Islamabad, 44000, Pakistan. .,UNIVERSITY OF MALAKAND, Chakdara, Khyber Pakhtunkhwa, 18000, Pakistan.
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Maximus-von-Imhof-Forum 3, Freising, 85354, Germany
| | - Josch Pauling
- Computational Lipidomics group, Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Maximus-von-Imhof-Forum 3, 85354, Freising, Germany
| | - Aamir Shafi
- Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, Pakistan
| | - Rehan Zafar Paracha
- Research Centre for Modeling and Simulation (RCMS), NUST, Islamabad, 44000, Pakistan
| | - Asad Hayat
- Research Centre for Modeling and Simulation (RCMS), NUST, Islamabad, 44000, Pakistan
| | - Amjad Ali
- Atta-ur-Rahman School of Applied Bio sciences (ASAB), NUST, Islamabad, 44000, Pakistan
| |
Collapse
|
5
|
Bakir ME, Konur S, Gheorghe M, Krasnogor N, Stannett M. Automatic selection of verification tools for efficient analysis of biochemical models. Bioinformatics 2018; 34:3187-3195. [PMID: 29688313 PMCID: PMC6137970 DOI: 10.1093/bioinformatics/bty282] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 04/20/2018] [Indexed: 11/14/2022] Open
Abstract
Motivation Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed; however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this article is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest. Results We provide a system that can automatically predict the fastest model checking tool for a given biological model. Our results show that one can make predictions of high confidence, with over 90% accuracy. This implies significant performance gain in verification time and substantially reduces the 'usability barrier' enabling biologists to have access to this powerful computational technology. Availability and implementation SMC Predictor tool is available at http://www.smcpredictor.com. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Mehmet Emin Bakir
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Savas Konur
- School of Electrical Engineering & Computer Science, University of Bradford, Bradford, UK
| | - Marian Gheorghe
- School of Electrical Engineering & Computer Science, University of Bradford, Bradford, UK
| | - Natalio Krasnogor
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle, UK
| | - Mike Stannett
- Department of Computer Science, University of Sheffield, Sheffield, UK
| |
Collapse
|
6
|
Liang Y, Kelemen A. Computational dynamic approaches for temporal omics data with applications to systems medicine. BioData Min 2017. [PMID: 28638442 PMCID: PMC5473988 DOI: 10.1186/s13040-017-0140-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.
Collapse
Affiliation(s)
- Yulan Liang
- Department of Family and Community Health, University of Maryland, Baltimore, MD 21201 USA
| | - Arpad Kelemen
- Department of Organizational Systems and Adult Health, University of Maryland, Baltimore, MD 21201 USA
| |
Collapse
|
7
|
Abstract
The cell division cycle is controlled by a complex regulatory network which ensures that the phases of the cell cycle are executed in the right order. This regulatory network receives signals from the environment, monitors the state of the DNA, and decides timings of cell cycle events. The underlying transcriptional and post-translational regulatory interactions lead to complex dynamical responses, such as the oscillations in the levels of cell cycle proteins driven by intertwined biochemical reactions. A cell moves between different phases of its cycle similar to a dynamical system switching between its steady states. The complex molecular network driving these phases has been investigated in previous computational systems biology studies. Here, we review the critical physiological and molecular transitions that occur in the cell cycle and discuss the role of mathematical modeling in elucidating these transitions and understand cell cycle synchronization.
Collapse
|
8
|
Schivo S, Scholma J, van der Vet PE, Karperien M, Post JN, van de Pol J, Langerak R. Modelling with ANIMO: between fuzzy logic and differential equations. BMC SYSTEMS BIOLOGY 2016; 10:56. [PMID: 27460034 PMCID: PMC4962523 DOI: 10.1186/s12918-016-0286-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 06/02/2016] [Indexed: 11/23/2022]
Abstract
BACKGROUND Computational support is essential in order to reason on the dynamics of biological systems. We have developed the software tool ANIMO (Analysis of Networks with Interactive MOdeling) to provide such computational support and allow insight into the complex networks of signaling events occurring in living cells. ANIMO makes use of timed automata as an underlying model, thereby enabling analysis techniques from computer science like model checking. Biology experts are able to use ANIMO via a user interface specifically tailored for biological applications. In this paper we compare the use of ANIMO with some established formalisms on two case studies. RESULTS ANIMO is a powerful and user-friendly tool that can compete with existing continuous and discrete paradigms. We show this by presenting ANIMO models for two case studies: Drosophila melanogaster circadian clock, and signal transduction events downstream of TNF α and EGF in HT-29 human colon carcinoma cells. The models were originally developed with ODEs and fuzzy logic, respectively. CONCLUSIONS Two biological case studies that have been modeled with respectively ODE and fuzzy logic models can be conveniently modeled using ANIMO. The ANIMO models require less parameters than ODEs and are more precise than fuzzy logic. For this reason we position the modelling paradigm of ANIMO between ODEs and fuzzy logic.
Collapse
Affiliation(s)
- Stefano Schivo
- Formal Methods and Tools, Faculty of EEMCS, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Jetse Scholma
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Paul E van der Vet
- Human Media Interaction, Faculty of EEMCS, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Marcel Karperien
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Janine N Post
- Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Jaco van de Pol
- Formal Methods and Tools, Faculty of EEMCS, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands
| | - Rom Langerak
- Formal Methods and Tools, Faculty of EEMCS, University of Twente, P.O. Box 217, Enschede, 7500AE, The Netherlands.
| |
Collapse
|
9
|
Konur S, Gheorghe M. A Property-Driven Methodology for Formal Analysis of Synthetic Biology Systems. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:360-371. [PMID: 26357223 DOI: 10.1109/tcbb.2014.2362531] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
This paper proposes a formal methodology to analyse bio-systems, in particular synthetic biology systems. An integrative analysis perspective combining different model checking approaches based on different property categories is provided. The methodology is applied to the synthetic pulse generator system and several verification experiments are carried out to demonstrate the use of our approach to formally analyse various aspects of synthetic biology systems.
Collapse
|
10
|
Andrieux G, Le Borgne M, Théret N. An integrative modeling framework reveals plasticity of TGF-β signaling. BMC SYSTEMS BIOLOGY 2014; 8:30. [PMID: 24618419 PMCID: PMC4007780 DOI: 10.1186/1752-0509-8-30] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 03/03/2014] [Indexed: 11/10/2022]
Abstract
Background The TGF-β transforming growth factor is the most pleiotropic cytokine controlling a broad range of cellular responses that include proliferation, differentiation and apoptosis. The context-dependent multifunctional nature of TGF-β is associated with complex signaling pathways. Differential models describe the dynamics of the TGF-β canonical pathway, but modeling the non-canonical networks constitutes a major challenge. Here, we propose a qualitative approach to explore all TGF-β-dependent signaling pathways. Results Using a new formalism, CADBIOM, which is based on guarded transitions and includes temporal parameters, we have built the first discrete model of TGF-β signaling networks by automatically integrating the 137 human signaling maps from the Pathway Interaction Database into a single unified dynamic model. Temporal property-checking analyses of 15934 trajectories that regulate 145 TGF-β target genes reveal the association of specific pathways with distinct biological processes. We identify 31 different combinations of TGF-β with other extracellular stimuli involved in non-canonical TGF-β pathways that regulate specific gene networks. Extensive analysis of gene expression data further demonstrates that genes sharing CADBIOM trajectories tend to be co-regulated. Conclusions As applied here to TGF-β signaling, CADBIOM allows, for the first time, a full integration of highly complex signaling pathways into dynamic models that permit to explore cell responses to complex microenvironment stimuli.
Collapse
Affiliation(s)
| | | | - Nathalie Théret
- INSERM U1085, IRSET, Université de Rennes 1, 2 avenue Pr Léon Bernard, 35043 Rennes, France.
| |
Collapse
|
11
|
Chaouiya C, Bérenguier D, Keating SM, Naldi A, van Iersel MP, Rodriguez N, Dräger A, Büchel F, Cokelaer T, Kowal B, Wicks B, Gonçalves E, Dorier J, Page M, Monteiro PT, von Kamp A, Xenarios I, de Jong H, Hucka M, Klamt S, Thieffry D, Le Novère N, Saez-Rodriguez J, Helikar T. SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools. BMC SYSTEMS BIOLOGY 2013; 7:135. [PMID: 24321545 PMCID: PMC3892043 DOI: 10.1186/1752-0509-7-135] [Citation(s) in RCA: 101] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2013] [Accepted: 11/26/2013] [Indexed: 05/03/2023]
Abstract
BACKGROUND Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.
Collapse
Affiliation(s)
- Claudine Chaouiya
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
| | - Duncan Bérenguier
- Institut de Mathématiques de Luminy, Campus de Luminy, Case 907, 13288 Marseille Cedex 9, France
| | - Sarah M Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Aurélien Naldi
- Center for Integrative Genomics, University of Lausanne, CH-1015 Lausanne, Switzerland
| | - Martijn P van Iersel
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Nicolas Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Andreas Dräger
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0412, USA
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany
| | - Finja Büchel
- Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, 72076 Tübingen, Germany
| | - Thomas Cokelaer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Bryan Kowal
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Benjamin Wicks
- College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
| | - Emanuel Gonçalves
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Julien Dorier
- Swiss-Prot & Vital-IT group, SIB- Swiss Institute of Bioinformatics, Center for Integrative Genomics, University of Lausanne, Quartier Sorge - Batiment Genopode, CH-1015 Lausanne, Switzerland
| | - Michel Page
- INRIA Grenoble – Rhône-Alpes, 655 avenue de l’Europe, Montbonnot, 38334 Saint-Ismier Cedex, France
- IAE Grenoble, Université Pierre-Mendès-France, Domaine universitaire BP 47, 38040 Grenoble Cedex 9, France
| | - Pedro T Monteiro
- Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal
- Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento (INESC-ID), Rua Alves Redol 9, 1000-029 Lisbon, Portugal
| | - Axel von Kamp
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Ioannis Xenarios
- Swiss-Prot & Vital-IT group, SIB- Swiss Institute of Bioinformatics, Center for Integrative Genomics, University of Lausanne, Quartier Sorge - Batiment Genopode, CH-1015 Lausanne, Switzerland
| | - Hidde de Jong
- INRIA Grenoble – Rhône-Alpes, 655 avenue de l’Europe, Montbonnot, 38334 Saint-Ismier Cedex, France
| | - Michael Hucka
- Computing and Mathematical sciences, California Institute of Technology, Pasadena, CA 91125, USA
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Denis Thieffry
- Institut de Biologie de l’Ecole Normale Supérieure (IBENS) - UMR CNRS 8197 - INSERM 1024 46 rue d’Ulm, 75230 Paris Cedex 05, France
| | - Nicolas Le Novère
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
- The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| |
Collapse
|
12
|
Requeno JI, Casado GDM, Blanco R, Colom JM. Temporal logics for phylogenetic analysis via model checking. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:1058-1070. [PMID: 24334397 DOI: 10.1109/tcbb.2013.87] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The need for general-purpose algorithms for studying biological properties in phylogenetics motivates research into formal verification frameworks. Researchers can focus their efforts exclusively on evolution trees and property specifications. To this end, model checking, a mature automated verification technique originating in computer science, is applied to phylogenetic analysis. Our approach is based on three cornerstones: a logical modeling of the evolution with transition systems; the specification of both phylogenetic properties and trees using flexible temporal logic formulas; and the verification of the latter by means of automated computer tools. The most conspicuous result is the inception of a formal framework which allows for a symbolic manipulation of biological data (based on the codification of the taxa). Additionally, different logical models of evolution can be considered, complex properties can be specified in terms of the logical composition of others, and the refinement of unfulfilled properties as well as the discovery of new properties can be undertaken by exploiting the verification results. Some experimental results using a symbolic model verifier support the feasibility of the approach.
Collapse
|
13
|
Bérenguier D, Chaouiya C, Monteiro PT, Naldi A, Remy E, Thieffry D, Tichit L. Dynamical modeling and analysis of large cellular regulatory networks. CHAOS (WOODBURY, N.Y.) 2013; 23:025114. [PMID: 23822512 DOI: 10.1063/1.4809783] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.
Collapse
Affiliation(s)
- D Bérenguier
- Institut de Mathématiques de Luminy, Marseille, France
| | | | | | | | | | | | | |
Collapse
|
14
|
Klarner H, Siebert H, Bockmayr A. Time series dependent analysis of unparametrized Thomas networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012; 9:1338-1351. [PMID: 22529333 DOI: 10.1109/tcbb.2012.61] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
This paper is concerned with the analysis of labeled Thomas networks using discrete time series. It focuses on refining the given edge labels and on assessing the data quality. The results are aimed at being exploitable for experimental design and include the prediction of new activatory or inhibitory effects of given interactions and yet unobserved oscillations of specific components in between specific sampling intervals. On the formal side, we generalize the concept of edge labels and introduce a discrete time series interpretation. This interpretation features two original concepts: 1) Incomplete measurements are admissible, and 2) it allows qualitative assumptions about the changes in gene expression by means of monotonicity. On the computational side, we provide a Python script, erda.py, that automates the suggested workflow by model checking and constraint satisfaction. We illustrate the workflow by investigating the yeast network IRMA.
Collapse
Affiliation(s)
- Hannes Klarner
- DFG Research Center Matheon, Freie Universität Berlin, Berlin, Germany.
| | | | | |
Collapse
|
15
|
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.
Collapse
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:
| |
Collapse
|
16
|
Barnat J, Brim L, Krejcí A, Streck A, Safránek D, Vejnár M, Vejpustek T. On parameter synthesis by parallel model checking. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012; 9:693-705. [PMID: 21788679 DOI: 10.1109/tcbb.2011.110] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
An important problem in current computational systems biology is to analyze models of biological systems dynamics under parameter uncertainty. This paper presents a novel algorithm for parameter synthesis based on parallel model checking. The algorithm is conceptually universal with respect to the modeling approach employed. We introduce the algorithm, show its scalability, and examine its applicability on several biological models.
Collapse
Affiliation(s)
- Jirí Barnat
- Faculty of Informatics, Masaryk University, Botanická 68a, Brno 60200, Czech Republic.
| | | | | | | | | | | | | |
Collapse
|
17
|
Batt G, Besson B, Ciron PE, de Jong H, Dumas E, Geiselmann J, Monte R, Monteiro PT, Page M, Rechenmann F, Ropers D. Genetic network analyzer: a tool for the qualitative modeling and simulation of bacterial regulatory networks. Methods Mol Biol 2012; 804:439-462. [PMID: 22144166 DOI: 10.1007/978-1-61779-361-5_22] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Genetic Network Analyzer (GNA) is a tool for the qualitative modeling and simulation of gene regulatory networks, based on so-called piecewise-linear differential equation models. We describe the use of this tool in the context of the modeling of bacterial regulatory networks, notably the network of global regulators controlling the adaptation of Escherichia coli to carbon starvation conditions. We show how the modeler, by means of GNA, can define a regulatory network, build a model of the network, determine the steady states of the system, perform a qualitative simulation of the network dynamics, and analyze the simulation results using model-checking tools. The example illustrates the interest of qualitative approaches for the analysis of the dynamics of bacterial regulatory networks.
Collapse
Affiliation(s)
- Grégory Batt
- INRIA Paris - Rocquencourt, Domaine de Voluceau, Le Chesnay, France
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
18
|
|
19
|
Monteiro PT, Dias PJ, Ropers D, Oliveira AL, Sá-Correia I, Teixeira MC, Freitas AT. Qualitative modelling and formal verification of the FLR1 gene mancozeb response in Saccharomyces cerevisiae. IET Syst Biol 2011; 5:308-16. [PMID: 22010757 DOI: 10.1049/iet-syb.2011.0001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Qualitative models allow understanding the relation between the structure and the dynamics of gene regulatory networks. The dynamical properties of these models can be automatically analysed by means of formal verification methods, like model checking. This facilitates the model-validation process and the test of new hypotheses to reconcile model predictions with the experimental data. RESULTS The authors report in this study the qualitative modelling and simulation of the transcriptional regulatory network controlling the response of the model eukaryote Saccharomyces cerevisiae to the agricultural fungicide mancozeb. The model allowed the analysis of the regulation level and activity of the components of the gene mancozeb-induced network controlling the transcriptional activation of the FLR1 gene, which is proposed to confer multidrug resistance through its putative role as a drug eflux pump. Formal verification analysis of the network allowed us to confront model predictions with the experimental data and to assess the model robustness to parameter ordering and gene deletion. CONCLUSIONS This analysis enabled us to better understand the mechanisms regulating the FLR1 gene mancozeb response and confirmed the need of a new transcription factor for the full transcriptional activation of YAP1. The result is a computable model of the FLR1 gene response to mancozeb, permitting a quick and cost-effective test of hypotheses prior to experimental validation.
Collapse
Affiliation(s)
- P T Monteiro
- INESC-ID/IST, Rua Alves Redol 9, Lisboa 1000-029, Portugal.
| | | | | | | | | | | | | |
Collapse
|
20
|
Baldazzi V, Ropers D, Geiselmann J, Kahn D, de Jong H. Importance of metabolic coupling for the dynamics of gene expression following a diauxic shift in Escherichia coli. J Theor Biol 2011; 295:100-15. [PMID: 22138386 DOI: 10.1016/j.jtbi.2011.11.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2011] [Revised: 11/07/2011] [Accepted: 11/08/2011] [Indexed: 11/27/2022]
Abstract
Gene regulatory networks consist of direct interactions, but also include indirect interactions mediated by metabolism. We investigate to which extent these indirect interactions arising from metabolic coupling influence the dynamics of the system. To this end, we build a qualitative model of the gene regulatory network controlling carbon assimilation in Escherichia coli, and use this model to study the changes in gene expression following a diauxic shift from glucose to acetate. In particular, we compare the relative variation in the steady-state concentrations of enzymes and transcription regulators during growth on glucose and acetate, as well as the dynamic response of gene expression to the exhaustion of glucose and the subsequent assimilation of acetate. We find significant differences between the dynamics of the system in the absence and presence of metabolic coupling. This shows that interactions arising from metabolic coupling cannot be ignored when studying the dynamics of gene regulatory networks.
Collapse
Affiliation(s)
- Valentina Baldazzi
- INRA, Plantes et Systèmes de Culture Horticoles, Domaine St Paul, Site Agroparc, 84941 Avignon Cedex 9, France.
| | | | | | | | | |
Collapse
|
21
|
Li C, Nagasaki M, Koh CH, Miyano S. Online model checking approach based parameter estimation to a neuronal fate decision simulation model in Caenorhabditis elegans with hybrid functional Petri net with extension. MOLECULAR BIOSYSTEMS 2011; 7:1576-92. [DOI: 10.1039/c0mb00253d] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
22
|
Abstract
The cell cycle is controlled by complex regulatory network to ensure that the phases of the cell cycle happen in the right order and transitions between phases happen only if the earlier phase is properly finished. This regulatory network receives signals from the environment, monitors the state of the DNA, and decides when the cell can proceed in its cycle. The transcriptional and post-translational regulatory interactions in this network can lead to complex dynamical responses. The cell cycle dependent oscillations in protein activities are driven by these interactions as the regulatory system moves between steady states that correspond to different phases of the cell cycle. The analysis of such complex molecular network behavior can be investigated with the tools of computational systems biology. Here we review the basic physiological and molecular transitions in the cell cycle and present how the system-level emergent properties were found by the help of mathematical/computational modeling.
Collapse
|
23
|
|
24
|
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.
Collapse
|
25
|
Twycross J, Band LR, Bennett MJ, King JR, Krasnogor N. Stochastic and deterministic multiscale models for systems biology: an auxin-transport case study. BMC SYSTEMS BIOLOGY 2010; 4:34. [PMID: 20346112 PMCID: PMC2873313 DOI: 10.1186/1752-0509-4-34] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2009] [Accepted: 03/26/2010] [Indexed: 11/10/2022]
Abstract
BACKGROUND Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks. RESULTS In this case study, we solve an auxin-transport model using analytical methods, deterministic numerical simulations and stochastic numerical simulations. Although the three approaches in general predict the same behaviour, the approaches provide different information that we use to gain distinct insights into the modelled biological system. We show in particular that the analytical approach readily provides straightforward mathematical expressions for the concentrations and transport speeds, while the stochastic simulations naturally provide information on the variability of the system. CONCLUSIONS Our study provides a constructive comparison which highlights the advantages and disadvantages of each of the considered modelling approaches. This will prove helpful to researchers when weighing up which modelling approach to select. In addition, the paper goes some way to bridging the gap between these approaches, which in the future we hope will lead to integrative hybrid models.
Collapse
Affiliation(s)
- Jamie Twycross
- Centre for Plant Integrative Biology, School of Biosciences, Sutton Bonington Campus, University of Nottingham, Nottingham LE125RD, UK
| | | | | | | | | |
Collapse
|
26
|
Monteiro PT, Dumas E, Besson B, Mateescu R, Page M, Freitas AT, de Jong H. A service-oriented architecture for integrating the modeling and formal verification of genetic regulatory networks. BMC Bioinformatics 2009; 10:450. [PMID: 20042075 PMCID: PMC2813247 DOI: 10.1186/1471-2105-10-450] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2009] [Accepted: 12/30/2009] [Indexed: 01/24/2023] Open
Abstract
Background The study of biological networks has led to the development of increasingly large and detailed models. Computer tools are essential for the simulation of the dynamical behavior of the networks from the model. However, as the size of the models grows, it becomes infeasible to manually verify the predictions against experimental data or identify interesting features in a large number of simulation traces. Formal verification based on temporal logic and model checking provides promising methods to automate and scale the analysis of the models. However, a framework that tightly integrates modeling and simulation tools with model checkers is currently missing, on both the conceptual and the implementational level. Results We have developed a generic and modular web service, based on a service-oriented architecture, for integrating the modeling and formal verification of genetic regulatory networks. The architecture has been implemented in the context of the qualitative modeling and simulation tool GNA and the model checkers NUSMV and CADP. GNA has been extended with a verification module for the specification and checking of biological properties. The verification module also allows the display and visual inspection of the verification results. Conclusions The practical use of the proposed web service is illustrated by means of a scenario involving the analysis of a qualitative model of the carbon starvation response in E. coli. The service-oriented architecture allows modelers to define the model and proceed with the specification and formal verification of the biological properties by means of a unified graphical user interface. This guarantees a transparent access to formal verification technology for modelers of genetic regulatory networks.
Collapse
Affiliation(s)
- Pedro T Monteiro
- INRIA Grenoble-Rhône-Alpes, 655 Avenue de l'Europe, Montbonnot, 38334 St Ismier Cedex, France.
| | | | | | | | | | | | | |
Collapse
|
27
|
Andrei O, Kirchner H. A Port Graph Calculus for Autonomic Computing and Invariant Verification. ACTA ACUST UNITED AC 2009. [DOI: 10.1016/j.entcs.2009.10.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
28
|
Li C, Nagasaki M, Ueno K, Miyano S. Simulation-based model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension. BMC SYSTEMS BIOLOGY 2009; 3:42. [PMID: 19393101 PMCID: PMC2691733 DOI: 10.1186/1752-0509-3-42] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2008] [Accepted: 04/27/2009] [Indexed: 11/10/2022]
Abstract
Background Model checking approaches were applied to biological pathway validations around 2003. Recently, Fisher et al. have proved the importance of model checking approach by inferring new regulation of signaling crosstalk in C. elegans and confirming the regulation with biological experiments. They took a discrete and state-based approach to explore all possible states of the system underlying vulval precursor cell (VPC) fate specification for desired properties. However, since both discrete and continuous features appear to be an indispensable part of biological processes, it is more appropriate to use quantitative models to capture the dynamics of biological systems. Our key motivation of this paper is to establish a quantitative methodology to model and analyze in silico models incorporating the use of model checking approach. Results A novel method of modeling and simulating biological systems with the use of model checking approach is proposed based on hybrid functional Petri net with extension (HFPNe) as the framework dealing with both discrete and continuous events. Firstly, we construct a quantitative VPC fate model with 1761 components by using HFPNe. Secondly, we employ two major biological fate determination rules – Rule I and Rule II – to VPC fate model. We then conduct 10,000 simulations for each of 48 sets of different genotypes, investigate variations of cell fate patterns under each genotype, and validate the two rules by comparing three simulation targets consisting of fate patterns obtained from in silico and in vivo experiments. In particular, an evaluation was successfully done by using our VPC fate model to investigate one target derived from biological experiments involving hybrid lineage observations. However, the understandings of hybrid lineages are hard to make on a discrete model because the hybrid lineage occurs when the system comes close to certain thresholds as discussed by Sternberg and Horvitz in 1986. Our simulation results suggest that: Rule I that cannot be applied with qualitative based model checking, is more reasonable than Rule II owing to the high coverage of predicted fate patterns (except for the genotype of lin-15ko; lin-12ko double mutants). More insights are also suggested. Conclusion The quantitative simulation-based model checking approach is a useful means to provide us valuable biological insights and better understandings of biological systems and observation data that may be hard to capture with the qualitative one.
Collapse
Affiliation(s)
- Chen Li
- Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo, Japan.
| | | | | | | |
Collapse
|
29
|
Abstract
One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.
Collapse
Affiliation(s)
- Attila Csikász-Nagy
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manci 17, Povo-Trento I-38100, Italy.
| |
Collapse
|
30
|
A Reduction of Logical Regulatory Graphs Preserving Essential Dynamical Properties. COMPUTATIONAL METHODS IN SYSTEMS BIOLOGY 2009. [DOI: 10.1007/978-3-642-03845-7_18] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
|
31
|
Mateescu R, Monteiro PT, Dumas E, de Jong H. Computation Tree Regular Logic for Genetic Regulatory Networks. ACTA ACUST UNITED AC 2008. [DOI: 10.1007/978-3-540-88387-6_6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
|