The Process-Interaction-Model: a common representation of rule-based and logical models allows studying signal transduction on different levels of detail

Kolczyk, Katrin; Samaga, Regina; Conzelmann, H.; Mirschel, S.; Conradi, Carsten

doi:10.1186/1471-2105-13-251

Cited by 11 publications

(12 citation statements)

References 40 publications

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“…A possible tool extension might exploit a new, recently developed ProMoT feature: the Process Interaction Model [50] (PIM) concept that gives a compact specification of a rule-based model and has been applied to the modeling of signaling pathways. This could lead to a fully rule-based, modular design of synthetic signaling networks, from the receptor membrane proteins down to the genes regulated in the nucleus.…”

Section: Discussionmentioning

confidence: 99%

Modular, rule-based modeling for the design of eukaryotic synthetic gene circuits

Marchisio

Colaiacovo

Whitehead

et al. 2013

BMC Systems Biology

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BackgroundThe modular design of synthetic gene circuits via composable parts (DNA segments) and pools of signal carriers (molecules such as RNA polymerases and ribosomes) has been successfully applied to bacterial systems. However, eukaryotic cells are becoming a preferential host for new synthetic biology applications. Therefore, an accurate description of the intricate network of reactions that take place inside eukaryotic parts and pools is necessary. Rule-based modeling approaches are increasingly used to obtain compact representations of reaction networks in biological systems. However, this approach is intrinsically non-modular and not suitable per se for the description of composable genetic modules. In contrast, the Model Description Language (MDL) adopted by the modeling tool ProMoT is highly modular and it enables a faithful representation of biological parts and pools.ResultsWe developed a computational framework for the design of complex (eukaryotic) gene circuits by generating dynamic models of parts and pools via the joint usage of the BioNetGen rule-based modeling approach and MDL. The framework converts the specification of a part (or pool) structure into rules that serve as inputs for BioNetGen to calculate the part’s species and reactions. The BioNetGen output is translated into an MDL file that gives a complete description of all the reactions that take place inside the part (or pool) together with a proper interface to connect it to other modules in the circuit. In proof-of-principle applications to eukaryotic Boolean circuits with more than ten genes and more than one thousand reactions, our framework yielded proper representations of the circuits’ truth tables.ConclusionsFor the model-based design of increasingly complex gene circuits, it is critical to achieve exact and systematic representations of the biological processes with minimal effort. Our computational framework provides such a detailed and intuitive way to design new and complex synthetic gene circuits.

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Section: Discussionmentioning

confidence: 99%

Modular, rule-based modeling for the design of eukaryotic synthetic gene circuits

Marchisio

Colaiacovo

Whitehead

et al. 2013

BMC Systems Biology

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“…It is implemented and simulated in the classical synchronous Boolean fashion, but retaining the exact network structure of the rxncon input. In this regard, our method goes into a similar direction as the recently published site-specific logical models proposed by [9]. However, it does not require parameterisation whereas the site-specific logical models require threshold parameters on top of a fully parameterised rule based model.…”

Section: Introductionmentioning

confidence: 97%

Reaction-contingency based bipartite Boolean modelling

et al. 2013

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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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“…they describe functionality at the component level, not at the level of states, which is insufficient to describe the often context dependent activity of signal transduction components. To our knowledge, only three methods have been developed for mechanistic Boolean simulation of signal transduction networks: One derived from SBGN-PD diagrams, one from rule-based models, and one bipartite Boolean modelling (bBM) method based on the rxncon language [12][13][14] . These can all be used for detailed description of signalling events.…”

Section: Introductionmentioning

confidence: 99%

“…These can all be used for detailed description of signalling events. However, the first is based on microstate description, inheriting the scalability issues of these approaches 12 ; the second requires a full parametrised rulebased model, inheriting the problem of parametrisation 13 ; and the third inherited shortcomings in expressiveness and precision from the first generation of the rxncon language 14 . However, the bBM method was successfully used for validation and gap-filling of large signal transduction models 14,15 .…”

Section: Introductionmentioning

confidence: 99%

A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models

Romers¹,

Thieme²,

Muenzner³

et al. 2017

Preprint

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The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. In contrast, current established methods do not support genome-scale mechanistic models of signal transduction networks. These networks encode information through internal states, and dealing with these states leads to scalability issues both in model formulation and execution. While rule based modelling can be used for efficient model definition (through rules) and simulation (through agent based execution), these quantitative models require parametrisation. This introduces yet another layer of uncertainty, due to the sparsity of reliably measured parameters. Hence, parameter-free simulation and validation will be important to support large-scale reconstruction and analysis of signal transduction network models. Here, we present a scalable method for parameterfree simulation of mechanistic signal transduction network models. It is based on rxncon, the reaction-contingency language, which describes the signalling network in terms of elemental reactions and states. We develop two generic update rules for states and reactions, based on detail analysis of two minimal reaction motifs, that can be used to map an arbitrary rxncon network on fully defined bipartite Boolean model. Locally defined update rules are assembled into a functional model without system level optimisation, making the methods suitable for network validation. Furthermore, an underlying model defined solely in terms of molecular reactions and causalities can be used to explain and predict system level behaviour. Taken together, we present a method for parameter-free simulation of mechanistic signal transduction models. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.

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The Process-Interaction-Model: a common representation of rule-based and logical models allows studying signal transduction on different levels of detail

Cited by 11 publications

References 40 publications

Modular, rule-based modeling for the design of eukaryotic synthetic gene circuits

Modular, rule-based modeling for the design of eukaryotic synthetic gene circuits

Reaction-contingency based bipartite Boolean modelling

A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models

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