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Krantz M, Eklund D, Särndahl E, Hedbrant A. A detailed molecular network map and model of the NLRP3 inflammasome. Front Immunol 2023; 14:1233680. [PMID: 38077364 PMCID: PMC10699087 DOI: 10.3389/fimmu.2023.1233680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/16/2023] [Indexed: 12/18/2023] Open
Abstract
The NLRP3 inflammasome is a key regulator of inflammation that responds to a broad range of stimuli. The exact mechanism of activation has not been determined, but there is a consensus on cellular potassium efflux as a major common denominator. Once NLRP3 is activated, it forms high-order complexes together with NEK7 that trigger aggregation of ASC into specks. Typically, there is only one speck per cell, consistent with the proposal that specks form - or end up at - the centrosome. ASC polymerisation in turn triggers caspase-1 activation, leading to maturation and release of IL-1β and pyroptosis, i.e., highly inflammatory cell death. Several gain-of-function mutations in the NLRP3 inflammasome have been suggested to induce spontaneous activation of NLRP3 and hence contribute to development and disease severity in numerous autoinflammatory and autoimmune diseases. Consequently, the NLRP3 inflammasome is of significant clinical interest, and recent attention has drastically improved our insight in the range of involved triggers and mechanisms of signal transduction. However, despite recent progress in knowledge, a clear and comprehensive overview of how these mechanisms interplay to shape the system level function is missing from the literature. Here, we provide such an overview as a resource to researchers working in or entering the field, as well as a computational model that allows for evaluating and explaining the function of the NLRP3 inflammasome system from the current molecular knowledge. We present a detailed reconstruction of the molecular network surrounding the NLRP3 inflammasome, which account for each specific reaction and the known regulatory constraints on each event as well as the mechanisms of drug action and impact of genetics when known. Furthermore, an executable model from this network reconstruction is generated with the aim to be used to explain NLRP3 activation from priming and activation to the maturation and release of IL-1β and IL-18. Finally, we test this detailed mechanistic model against data on the effect of different modes of inhibition of NLRP3 assembly. While the exact mechanisms of NLRP3 activation remains elusive, the literature indicates that the different stimuli converge on a single activation mechanism that is additionally controlled by distinct (positive or negative) priming and licensing events through covalent modifications of the NLRP3 molecule. Taken together, we present a compilation of the literature knowledge on the molecular mechanisms on NLRP3 activation, a detailed mechanistic model of NLRP3 activation, and explore the convergence of diverse NLRP3 activation stimuli into a single input mechanism.
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Affiliation(s)
- Marcus Krantz
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Inflammatory Response and Infection Susceptibility Centre (iRiSC), Örebro University, Örebro, Sweden
| | - Daniel Eklund
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Inflammatory Response and Infection Susceptibility Centre (iRiSC), Örebro University, Örebro, Sweden
| | - Eva Särndahl
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Inflammatory Response and Infection Susceptibility Centre (iRiSC), Örebro University, Örebro, Sweden
| | - Alexander Hedbrant
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Inflammatory Response and Infection Susceptibility Centre (iRiSC), Örebro University, Örebro, Sweden
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Nazem-Bokaee H, Hom EFY, Warden AC, Mathews S, Gueidan C. Towards a Systems Biology Approach to Understanding the Lichen Symbiosis: Opportunities and Challenges of Implementing Network Modelling. Front Microbiol 2021; 12:667864. [PMID: 34012428 PMCID: PMC8126723 DOI: 10.3389/fmicb.2021.667864] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 04/09/2021] [Indexed: 11/16/2022] Open
Abstract
Lichen associations, a classic model for successful and sustainable interactions between micro-organisms, have been studied for many years. However, there are significant gaps in our understanding about how the lichen symbiosis operates at the molecular level. This review addresses opportunities for expanding current knowledge on signalling and metabolic interplays in the lichen symbiosis using the tools and approaches of systems biology, particularly network modelling. The largely unexplored nature of symbiont recognition and metabolic interdependency in lichens could benefit from applying a holistic approach to understand underlying molecular mechanisms and processes. Together with ‘omics’ approaches, the application of signalling and metabolic network modelling could provide predictive means to gain insights into lichen signalling and metabolic pathways. First, we review the major signalling and recognition modalities in the lichen symbioses studied to date, and then describe how modelling signalling networks could enhance our understanding of symbiont recognition, particularly leveraging omics techniques. Next, we highlight the current state of knowledge on lichen metabolism. We also discuss metabolic network modelling as a tool to simulate flux distribution in lichen metabolic pathways and to analyse the co-dependence between symbionts. This is especially important given the growing number of lichen genomes now available and improved computational tools for reconstructing such models. We highlight the benefits and possible bottlenecks for implementing different types of network models as applied to the study of lichens.
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Affiliation(s)
- Hadi Nazem-Bokaee
- CSIRO Australian National Herbarium, Centre for Australian National Biodiversity Research, National Research Collections Australia, NCMI, Canberra, ACT, Australia.,CSIRO Land and Water, Canberra, ACT, Australia.,CSIRO Synthetic Biology Future Science Platform, Canberra, ACT, Australia
| | - Erik F Y Hom
- Department of Biology and Center for Biodiversity and Conservation Research, The University of Mississippi, University City, MS, United States
| | | | - Sarah Mathews
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Cécile Gueidan
- CSIRO Australian National Herbarium, Centre for Australian National Biodiversity Research, National Research Collections Australia, NCMI, Canberra, ACT, Australia
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3
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Münzner U, Klipp E, Krantz M. A comprehensive, mechanistically detailed, and executable model of the cell division cycle in Saccharomyces cerevisiae. Nat Commun 2019; 10:1308. [PMID: 30899000 PMCID: PMC6428898 DOI: 10.1038/s41467-019-08903-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Accepted: 01/24/2019] [Indexed: 01/31/2023] Open
Abstract
Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology. This will require computational models, whose predictive power is expected to increase with coverage and precision of formulation. Genome-scale models revolutionised the metabolic field and made the first whole-cell model possible. However, the lack of genome-scale models of signalling networks blocks the development of eukaryotic whole-cell models. Here, we present a comprehensive mechanistic model of the molecular network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the reaction-contingency language, to neutralise the scalability issues preventing formulation, visualisation and simulation of signalling networks at the genome-scale. We use parameter-free modelling to validate the network and to predict genotype-to-phenotype relationships down to residue resolution. This mechanistic genome-scale model offers a new perspective on eukaryotic cell cycle control, and opens up for similar models-and eventually whole-cell models-of human cells.
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Affiliation(s)
- Ulrike Münzner
- Humboldt-Universität zu Berlin, Institute of Biology, Theoretical Biophysics, Berlin, 10099, Germany
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, 611-0011, Japan
| | - Edda Klipp
- Humboldt-Universität zu Berlin, Institute of Biology, Theoretical Biophysics, Berlin, 10099, Germany
| | - Marcus Krantz
- Humboldt-Universität zu Berlin, Institute of Biology, Theoretical Biophysics, Berlin, 10099, Germany.
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Welkenhuysen N, Schnitzer B, Österberg L, Cvijovic M. Robustness of Nutrient Signaling Is Maintained by Interconnectivity Between Signal Transduction Pathways. Front Physiol 2019; 9:1964. [PMID: 30719010 PMCID: PMC6348271 DOI: 10.3389/fphys.2018.01964] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 12/31/2018] [Indexed: 12/16/2022] Open
Abstract
Systems biology approaches provide means to study the interplay between biological processes leading to the mechanistic understanding of the properties of complex biological systems. Here, we developed a vector format rule-based Boolean logic model of the yeast S. cerevisiae cAMP-PKA, Snf1, and the Snf3-Rgt2 pathway to better understand the role of crosstalk on network robustness and function. We identified that phosphatases are the common unknown components of the network and that crosstalk from the cAMP-PKA pathway to other pathways plays a critical role in nutrient sensing events. The model was simulated with known crosstalk combinations and subsequent analysis led to the identification of characteristics and impact of pathway interconnections. Our results revealed that the interconnections between the Snf1 and Snf3-Rgt2 pathway led to increased robustness in these signaling pathways. Overall, our approach contributes to the understanding of the function and importance of crosstalk in nutrient signaling.
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Affiliation(s)
- Niek Welkenhuysen
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Barbara Schnitzer
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Linnea Österberg
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Marija Cvijovic
- Department of Mathematical Sciences, University of Gothenburg, Gothenburg, Sweden.,Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
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Abstract
We present a protocol for building, validating, and simulating models of signal transduction networks. These networks are challenging modeling targets due to the combinatorial complexity and sparse data, which have made it a major challenge even to formalize the current knowledge. To address this, the community has developed methods to model biomolecular reaction networks based on site dynamics. The strength of this approach is that reactions and states can be defined at variable resolution, which makes it possible to adapt the model resolution to the empirical data. This improves both scalability and accuracy, making it possible to formalize large models of signal transduction networks. Here, we present a method to build and validate large models of signal transduction networks. The workflow is based on rxncon, the reaction-contingency language. In a five-step process, we create a mechanistic network model, convert it into an executable Boolean model, use the Boolean model to evaluate and improve the network, and finally export the rxncon model into a rule-based format. We provide an introduction to the rxncon language and an annotated, step-by-step protocol for the workflow. Finally, we create a small model of the insulin signaling pathway to illustrate the protocol, together with some of the challenges-and some of their solutions-in modeling signal transduction.
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Affiliation(s)
- Jesper Romers
- Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Thieme
- Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulrike Münzner
- Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.,Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
| | - Marcus Krantz
- Institute of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
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Abstract
The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types) of the molecular networks, for example, genome-scale metabolic network (GMN), transcriptional regulatory network (TRN), and signal transduction network (STN). It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling) of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.
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Affiliation(s)
- Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Dan Wu
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Lingxuan Zhao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Qian Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Edwin Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Tianjin Bohai Fisheries Research Institute, Tianjin, China
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Garland J. Unravelling the complexity of signalling networks in cancer: A review of the increasing role for computational modelling. Crit Rev Oncol Hematol 2017; 117:73-113. [PMID: 28807238 DOI: 10.1016/j.critrevonc.2017.06.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 06/01/2017] [Accepted: 06/08/2017] [Indexed: 02/06/2023] Open
Abstract
Cancer induction is a highly complex process involving hundreds of different inducers but whose eventual outcome is the same. Clearly, it is essential to understand how signalling pathways and networks generated by these inducers interact to regulate cell behaviour and create the cancer phenotype. While enormous strides have been made in identifying key networking profiles, the amount of data generated far exceeds our ability to understand how it all "fits together". The number of potential interactions is astronomically large and requires novel approaches and extreme computation methods to dissect them out. However, such methodologies have high intrinsic mathematical and conceptual content which is difficult to follow. This review explains how computation modelling is progressively finding solutions and also revealing unexpected and unpredictable nano-scale molecular behaviours extremely relevant to how signalling and networking are coherently integrated. It is divided into linked sections illustrated by numerous figures from the literature describing different approaches and offering visual portrayals of networking and major conceptual advances in the field. First, the problem of signalling complexity and data collection is illustrated for only a small selection of known oncogenes. Next, new concepts from biophysics, molecular behaviours, kinetics, organisation at the nano level and predictive models are presented. These areas include: visual representations of networking, Energy Landscapes and energy transfer/dissemination (entropy); diffusion, percolation; molecular crowding; protein allostery; quinary structure and fractal distributions; energy management, metabolism and re-examination of the Warburg effect. The importance of unravelling complex network interactions is then illustrated for some widely-used drugs in cancer therapy whose interactions are very extensive. Finally, use of computational modelling to develop micro- and nano- functional models ("bottom-up" research) is highlighted. The review concludes that computational modelling is an essential part of cancer research and is vital to understanding network formation and molecular behaviours that are associated with it. Its role is increasingly essential because it is unravelling the huge complexity of cancer induction otherwise unattainable by any other approach.
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Affiliation(s)
- John Garland
- Manchester Interdisciplinary Biocentre, Manchester University, Manchester, UK.
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Lubitz T, Welkenhuysen N, Shashkova S, Bendrioua L, Hohmann S, Klipp E, Krantz M. Network reconstruction and validation of the Snf1/AMPK pathway in baker's yeast based on a comprehensive literature review. NPJ Syst Biol Appl 2015; 1:15007. [PMID: 28725459 PMCID: PMC5516868 DOI: 10.1038/npjsba.2015.7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2015] [Revised: 06/19/2015] [Accepted: 07/14/2015] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND/OBJECTIVES The SNF1/AMPK protein kinase has a central role in energy homeostasis in eukaryotic cells. It is activated by energy depletion and stimulates processes leading to the production of ATP while it downregulates ATP-consuming processes. The yeast SNF1 complex is best known for its role in glucose derepression. METHODS We performed a network reconstruction of the Snf1 pathway based on a comprehensive literature review. The network was formalised in the rxncon language, and we used the rxncon toolbox for model validation and gap filling. RESULTS We present a machine-readable network definition that summarises the mechanistic knowledge of the Snf1 pathway. Furthermore, we used the known input/output relationships in the network to identify and fill gaps in the information transfer through the pathway, to produce a functional network model. Finally, we convert the functional network model into a rule-based model as a proof-of-principle. CONCLUSIONS The workflow presented here enables large scale reconstruction, validation and gap filling of signal transduction networks. It is analogous to but distinct from that established for metabolic networks. We demonstrate the workflow capabilities, and the direct link between the reconstruction and dynamic modelling, with the Snf1 network. This network is a distillation of the knowledge from all previous publications on the Snf1/AMPK pathway. The network is a knowledge resource for modellers and experimentalists alike, and a template for similar efforts in higher eukaryotes. Finally, we envisage the workflow as an instrumental tool for reconstruction of large signalling networks across Eukaryota.
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Affiliation(s)
- Timo Lubitz
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Niek Welkenhuysen
- Department of Chemistry and Molecular Biology, University of Gothenburg, Göteborg, Sweden
| | - Sviatlana Shashkova
- Department of Chemistry and Molecular Biology, University of Gothenburg, Göteborg, Sweden
| | - Loubna Bendrioua
- Department of Chemistry and Molecular Biology, University of Gothenburg, Göteborg, Sweden
| | - Stefan Hohmann
- Department of Chemistry and Molecular Biology, University of Gothenburg, Göteborg, Sweden
| | - Edda Klipp
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Marcus Krantz
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
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Mori T, Flöttmann M, Krantz M, Akutsu T, Klipp E. Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks. BMC SYSTEMS BIOLOGY 2015; 9:45. [PMID: 26259567 PMCID: PMC4531511 DOI: 10.1186/s12918-015-0193-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Accepted: 07/30/2015] [Indexed: 11/29/2022]
Abstract
Background Cellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical reaction detail in a quantitative or semi quantitative manner. Results Here, we present a probabilistic bipartite Boolean modeling method that addresses these issues. The method is based on the reaction-contingency formalism, and enables fast simulation of large networks. We demonstrate its scalability by applying it to the yeast mitogen-activated protein kinase (MAPK) network consisting of 140 proteins and 608 nodes. Conclusion The probabilistic Boolean model can be generated and parameterized automatically from a rxncon network description, using only two global parameters, and its qualitative behavior is robust against order of magnitude variation in these parameters. Our method can hence be used to simulate the outcome of large signal transduction network reconstruction, with little or no overhead in model creation or parameterization.
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Affiliation(s)
- Tomoya Mori
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan.
| | - Max Flöttmann
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115, Berlin, Germany.
| | - Marcus Krantz
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115, Berlin, Germany.
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan.
| | - Edda Klipp
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115, Berlin, Germany.
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Flöttmann M, Krause F, Klipp E, Krantz M. Reaction-contingency based bipartite Boolean modelling. BMC SYSTEMS BIOLOGY 2013; 7:58. [PMID: 23835289 PMCID: PMC3710479 DOI: 10.1186/1752-0509-7-58] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2012] [Accepted: 06/14/2013] [Indexed: 11/26/2022]
Abstract
Background Intracellular signalling systems are highly complex, rendering mathematical modelling of large signalling networks infeasible or impractical. Boolean modelling provides one feasible approach to whole-network modelling, but at the cost of dequantification and decontextualisation of activation. That is, these models cannot distinguish between different downstream roles played by the same component activated in different contexts. Results Here, we address this with a bipartite Boolean modelling approach. Briefly, we use a state oriented approach with separate update rules based on reactions and contingencies. This approach retains contextual activation information and distinguishes distinct signals passing through a single component. Furthermore, we integrate this approach in the rxncon framework to support automatic model generation and iterative model definition and validation. We benchmark this method with the previously mapped MAP kinase network in yeast, showing that minor adjustments suffice to produce a functional network description. Conclusions Taken together, we (i) present a bipartite Boolean modelling approach that retains contextual activation information, (ii) provide software support for automatic model generation, visualisation and simulation, and (iii) demonstrate its use for iterative model generation and validation.
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Affiliation(s)
- Max Flöttmann
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr, 42, Berlin 10115, Germany.
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