Modular modeling of  cellular systems with ProMoT/Diva

Ginkel, Martin; Kremling, Andreas; Nutsch, Torsten; Rehner, Ruxandra; Gilles, Ernst Dieter

doi:10.1093/bioinformatics/btg128

Cited by 83 publications

(67 citation statements)

References 16 publications

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“…2 and 3, necessary conditions for bistability-the existence of two stable steady states-are given. The so-called Thomas conjecture states that positive feedback loops are necessary but not sufficient for bistability (3). In addition, Feinberg's chemical reaction network theory (CRNT) gives necessary and sufficient conditions for multistationarity, provided that all kinetics are of the mass action form and that the network satisfies certain structural conditions.…”

mentioning

confidence: 99%

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks

Conradi

Flockerzi

Raisch

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

102

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In analyzing and mathematical modeling of complex (bio)chemical reaction networks, formal methods that connect network structure and dynamic behavior are needed because often, quantitative knowledge of the networks is very limited. This applies to many important processes in cell biology. Chemical reaction network theory allows for the classification of the potential network behavior-for instance, with respect to the existence of multiple steady states-but is computationally limited to small systems. Here, we show that by analyzing subnetworks termed elementary flux modes, the applicability of the theory can be extended to more complex networks. For an example network inspired by cell cycle control in budding yeast, the approach allows for model discrimination, identification of key mechanisms for multistationarity, and robustness analysis. The presented methods will be helpful in modeling and analyzing other complex reaction networks.reaction network ͉ structure ͉ elementary flux mode ͉ bistability

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mentioning

confidence: 99%

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks

Conradi

Flockerzi

Raisch

et al. 2007

Proc. Natl. Acad. Sci. U.S.A.

102

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“…The models have been set up with the modeling tool PROMOT in a modularly structured way using an abstract and general modeling methodology (Ginkel et al 2003). Elementary modeling objects representing elementary molecular functional units (like storages and reactions) are aggregated to higher structured modules that can be further connected to more complex models.…”

Section: Methods Modelingmentioning

confidence: 99%

Signal Processing and Flagellar Motor Switching During Phototaxis of Halobacterium salinarum

et al. 2003

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Prokaryotic taxis, the active search of motile cells for the best environmental conditions, is one of the paradigms for signal transduction. The search algorithm implemented by the cellular biochemistry modulates the probability of switching the rotational direction of the flagellar motor, a nanomachine that propels prokaryotic cells. On the basis of the well-known biochemical mechanisms of chemotaxis in Escherichia coli, kinetic modeling of the events leading from chemoreceptor activation by ligand binding to the motility response has been performed with great success. In contrast to Escherichia coli, Halobacterium salinarum, in addition, responds to visible light, which is sensed through specific photoreceptors of different wavelength sensitivity (phototaxis). Light stimuli of defined intensity and time course can be controlled precisely, which facilitates input-output measurements used for system analysis of the molecular network connecting the sensory receptors to the flagellar motor switch. Here, we analyze the response of halobacterial cells to single and double-pulse light stimuli and present the first kinetic model for prokaryotic cells that couples the signal-transduction pathway with the flagellar motor switch. Modeling based on experimental data supports the current biochemical model of halobacterial phototaxis. Moreover, the simulations demonstrate that motor switching occurs through subsequent rate-limiting steps, which are both under sensory control, suggesting that two signals may be involved in halobacterial phototaxis

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“…An effort using quantitative modules in CellML format has been presented at the SysBioSys 2007 conference (Rouilly et al 2007). Some recent tools have allowed the export in SBML format, such as the command line tool ASMPARTS (Rodrigo et al 2007), synthetic biology software suite (Hill et al 2008), as well as an extension (Marchisio & Stelling 2008) written for the PROMOT (Ginkel et al 2003) suite. These tools and efforts are still in the early stages, but they have already seemed to be quite usable, even if they do not offer the full range of biological parts available in the registry.…”

Section: Model Creationmentioning

confidence: 99%

Designing and encoding models for synthetic biology

Endler

Rodríguez

Juty

et al. 2009

J. R. Soc. Interface.

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A key component of any synthetic biology effort is the use of quantitative models. These models and their corresponding simulations allow optimization of a system design, as well as guiding their subsequent analysis. Once a domain mostly reserved for experts, dynamical modelling of gene regulatory and reaction networks has been an area of growth over the last decade. There has been a concomitant increase in the number of software tools and standards, thereby facilitating model exchange and reuse. We give here an overview of the model creation and analysis processes as well as some software tools in common use. Using markup language to encode the model and associated annotation, we describe the mining of components, their integration in relational models, formularization and parametrization. Evaluation of simulation results and validation of the model close the systems biology 'loop'.

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Modular modeling of cellular systems with ProMoT/Diva

Abstract: http://www.mpi-magdeburg.mpg.de/projects/promot

Cited by 83 publications

References 16 publications

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks

Subnetwork analysis reveals dynamic features of complex (bio)chemical networks

Signal Processing and Flagellar Motor Switching During Phototaxis of Halobacterium salinarum

Designing and encoding models for synthetic biology

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