2020-10-30T11:09:59Zhttp://edoc.mpg.de/ac_ft_oai.ploai:edoc.mpg.de:350322012-10-2419:94
Strukturierung zellulärer Funktionseinheiten - ein signalorientierter Modellierungsansatz für zelluläre Systeme am Beispiel von Escherichia coli
Kremling, Andreas
expertsonly
The understanding of the growth and production behaviour of microorganisms requires a detailed knowledge in microbiology and genetics. To use the high potential of biological systems, e.g. in biotechnology, the knowledge has to be structured and collected in an apparent form. This will be a basis for setting up mathematical models. The thesis starts with a presentation of the research field, followed by an introduction in the modeling concept. In the thesis, a systematic approach to develop a detailed metabolic model is introduced. The approach is based on the definition of modeling objects (submodels) with defined in- and outputs. To guarantee a high biological transparency the modeling objects can be assigned directly to cellular units, e.g. a single enzymatic reaction step or e.g. complete pathways for transport and degradation of carbohydrates. Elementary modeling objects represent modeling objects with the highest resolution, i.e. substance storages, substance transformers and signal transformers. Substance storages represent metabolites, substance transformers stand for enzymatic reaction steps or polymerization steps while signal transformers describe processes of signal transduction and processing. A mathematical description is realized by assigning equations to the modeling objects. The following chapter describes the aggregation of elementary modeling objects to ’functional units’,i.e. units with higher complexity. Three criteria are given to demarcate functional units. The most important one describes the organization according signal transduction and processing. Special characteristics of functional units are (i) a hierarchical structure and (ii) signal processing in local as well as global signal transduction elements. An important point discussed in the chapter is signal processing in hierarchical structured units. For the initiation of transcription, in general, activators or inhibitors interact with the RNA polymerase to modify the initiation frequency. However, the influence of these effectors is limited to a small number of binding sites, e.g. the lactose repressor LacI has only one binding site on the whole E. coli genome. Hence, the repressor will clearly influence the initiation frequency of the lac-operon, but it will not influence the overall distribution of the RNA polymerase inside the cell. This fact is used in a new method to describe signal processing. Every protein involved in transcription of a gene is assigned to one level in hierarchy. Signals are transduced from top level to the lower level, but not vice versa. A computer tool is necessary to set up models with a large number of equations. The software package CellMod is based on C++ and supports the modeler by generating the required equations automatically. A number of elementary and aggregated modeling objects are implemented. Central in this tool is a model library for enzyme catalysed reactions, including about 42 entrys, starting from a simple Michaels-Menten equation up to complex models for 4 reaction partners. The modeling concept is applied to carbohydrate uptake in Escherichiacoli. The phenomenon of “glucose catabolite repression” means the repression of uptake of carbon sources if glucose is present in the medium. Only if glucose has run out, transport systems of other available carbohydrates are synthesised. This is due to a complex signal transduction pathway starting from the main glucose uptake system. The mathematical model developed in this thesis describes the main glucose uptake system and further elements in the signal transduction pathway. They represent the highest level of control in a functional unit called crp-modulon, named after the final target of the signal transduction pathway, the protein Crp. Besides the description of the signal transduction pathway, the metabolic pathways for lactose uptake and degradation and for the glycolysis are also included in the model. Finally the simulation results are compared to data published in the literature. The comparision of simulation results and experimental data of a wild type strain and of strains which are genetically modified are in good agreement.
Universität
2001
PhD-Thesis
http://edoc.mpg.de/35032
urn:ISBN:3-8265-9888-1
de
oai:edoc.mpg.de:1920302012-10-2419:94
Untersuchungen zur Regulation der Antibiotikaproduktion in Fermentationskulturen von Streptomycestendae und Amycolatopsis mediterranei
Grammel, Hartmut
expertsonly
Eberhard-Karls-Universität
1999
PhD-Thesis
http://edoc.mpg.de/192030
de
oai:edoc.mpg.de:2079462012-10-2419:94
Mathematical modeling of signal transduction pathways in mammalian cells at the example of the EGF induced MAP kinase cascade and TNF receptor crosstalk
Schoeberl, Birgit
expertsonly
Universität
2003
PhD-Thesis
http://edoc.mpg.de/207946
urn:ISBN:3-8322-2985-X
en
oai:edoc.mpg.de:2083532012-03-0519:94
Computation of elementary modes : a unifying framework and the new binary approach
Gagneur, J.
Klamt, S.
expertsonly
Background: Metabolic pathway analysis has been recognized as a central approach to the structural analysis of metabolic networks. The concept of elementary (flux) modes provides a rigorous formalism to describe and assess pathways and has proven to be valuable for many applications. However, computing elementary modes is a hard computational task. In recent years we assisted in a multiplication of algorithms dedicated to it. We require a summarizing point of view and a continued improvement of the current methods.
Results: We show that computing the set of elementary modes is equivalent to computing the set of extreme rays of a convex cone. This standard mathematical representation provides a unified framework that encompasses the most prominent algorithmic methods that compute elementary modes and allows a clear comparison between them. Taking lessons from this benchmark, we here introduce a new method, the binary approach, which computes the elementary modes as binary patterns of participating reactions from which the respective stoichiometric coefficients can be computed in a post-processing step. We implemented the binary approach in FluxAnalyzer 5.1, a software that is free for academics. The binary approach decreases the memory demand up to 96% without loss of speed giving the most efficient method available for computing elementary modes to date.
Conclusions: The equivalence between elementary modes and extreme ray computations offers opportunities for employing tools from polyhedral computation for metabolic pathway analysis. The new binary approach introduced herein was derived from this general theoretical framework and facilitates the computation of elementary modes in considerably larger networks.
© 2004 Gagneur and Klamt; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2004
Article
http://edoc.mpg.de/208353
BMC Bioinformatics, v.5 (2004)
en
oai:edoc.mpg.de:2219102012-10-2419:94
Systems analysis of robustness in cellular networks
Stelling, Jörg
expertsonly
Universität
2004
PhD-Thesis
http://edoc.mpg.de/221910
en
oai:edoc.mpg.de:2399662012-03-0519:94
A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks
Conzelmann, H.
Saez-Rodriguez, J.
Sauter, T.
Kholodenko, B. N.
Gilles, E. D.
expertsonly
2006
Article
http://edoc.mpg.de/239966
BMC Bioinformatics, v.7 (2006)
en
oai:edoc.mpg.de:2467642012-11-0119:94
Die bakterielle Signalverarbeitung am Beispiel des Sucrose Phosphotransferasesystems in Escherichia coli : Modellierung und experimentelle Überprüfung
Sauter, Thomas
expertsonly
Universität
2003
PhD-Thesis
http://edoc.mpg.de/246764
de
oai:edoc.mpg.de:2514152012-03-0519:94
A methodology for the structural and functional analysis of signaling and regulatory networks
Klamt, S.
Saez-Rodriguez, J.
Lindquist, J.
Simeoni, L.
Gilles, E. D.
expertsonly
2006
Article
http://edoc.mpg.de/251415
BMC Bioinformatics, v.7 (2006)
en
oai:edoc.mpg.de:2928702012-03-0619:94
Visual set-up of logical models of signaling and regulatory networks with ProMoT
Saez-Rodriguez, J.
Mirschel, S.
Hemenway, R.
Klamt, S.
Gilles, E. D.
Ginkel, M.
expertsonly
Background: The analysis of biochemical networks using a logical (Boolean) description is an important approach in Systems Biology. Recently, new methods have been proposed to analyze large signaling and regulatory networks using this formalism. Even though there is a large number of tools to set up models describing biological networks using a biochemical (kinetic) formalism, however, they do not support logical models.
Results: Herein we present a flexible framework for setting up large logical models in a visual manner with the software tool ProMoT. An easily extendible library, ProMoT's inherent modularity and object-oriented concept as well as adaptive visualization techniques provide a versatile environment. Both the graphical and the textual description of the logical model can be exported to different formats.
Conclusion: New features of ProMoT facilitate an efficient set-up of large Boolean models of biochemical interaction networks. The modeling environment is flexible; it can easily be adapted to specific requirements, and new extensions can be introduced. ProMoT is freely available from http://www.mpi-magdeburg.mpg.de/projects/promot/.
2006
Article
http://edoc.mpg.de/292870
BMC Bioinformatics, v.7 (2006)
en
oai:edoc.mpg.de:2938712012-03-0619:94
Structural and functional analysis of cellular networks with CellNetAnalyzer
Klamt, S.
Saez-Rodriguez, J.
Gilles, E. D.
expertsonly
Background
Mathematical modelling of cellular networks is an integral part of Systems Biology and requires appropriate software tools. An important class of methods in Systems Biology deals with structural or topological (parameter-free) analysis of cellular networks. So far, software tools providing such methods for both mass-flow (metabolic) as well as signal-flow (signalling and regulatory) networks are lacking.
Results
Herein we introduce CellNetAnalyzer, a toolbox for MATLAB facilitating, in an interactive and visual manner, a comprehensive structural analysis of metabolic, signalling and regulatory networks. The particular strengths of CellNetAnalyzer are methods for functional network analysis, i.e. for characterising functional states, for detecting functional dependencies, for identifying intervention strategies, or for giving qualitative predictions on the effects of perturbations. CellNetAnalyzer extends its predecessor FluxAnalyzer (originally developed for metabolic network and pathway analysis) by a new modelling framework for examining signal-flow networks. Two of the novel methods implemented in CellNetAnalyzer are discussed in more detail regarding algorithmic issues and applications: the computation and analysis (i) of shortest positive and shortest negative paths and circuits in interaction graphs and (ii) of minimal intervention sets in logical networks.
Conclusions
CellNetAnalyzer provides a single suite to perform structural and qualitative analysis of both mass-flow- and signal-flow-based cellular networks in a user-friendly environment. It provides a large toolbox with various, partially unique, functions and algorithms for functional network analysis. CellNetAnalyzer is freely available for academic use.
2007
Article
http://edoc.mpg.de/293871
BMC Systems Biology, v.1 (2007)
en
oai:edoc.mpg.de:3209322012-03-0519:94
GSMN-TB : a web-based genome-scale network model of Mycobacterium tuberculosis metabolism
Beste, D. J.
Hooper, T.
Stewart, G.
Bonde, B.
Avignone-Rossa, C.
Bushell, M. E.
Wheeler, P.
Klamt, S.
Kierzek, A. M.
McFadden, J.
expertsonly
Background
An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis.
Results
GSMN-TB, a genome-scale metabolic model of M. tuberculosis, was constructed, consisting of 849 unique reactions and 739 metabolites, and involving 726 genes. The model was calibrated by growing Mycobacterium bovis bacille Calmette Guérin in continuous culture and steady-state growth parameters were measured. Flux balance analysis was used to calculate substrate consumption rates, which were shown to correspond closely to experimentally determined values. Predictions of gene essentiality were also made by flux balance analysis simulation and were compared with global mutagenesis data for M. tuberculosis grown in vitro. A prediction accuracy of 78% was achieved. Known drug targets were predicted to be essential by the model. The model demonstrated a potential role for the enzyme isocitrate lyase during the slow growth of mycobacteria, and this hypothesis was experimentally verified. An interactive web-based version of the model is available.
Conclusion
The GSMN-TB model successfully simulated many of the growth properties of M. tuberculosis. The model provides a means to examine the metabolic flexibility of bacteria and predict the phenotype of mutants, and it highlights previously unexplored features of M. tuberculosis metabolism.
© 2007 Beste et al.; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2007
Article
http://edoc.mpg.de/320932
Genome Biology, v.8 (2007)
en
oai:edoc.mpg.de:3221672012-03-0519:94
A logical model provides insights into T cell receptor signaling
Saez-Rodriguez, J.
Simeoni, L.
Lindquist, J.
Hemenway, R.
Bommhardt, U.
Arndt, B.
Haus, U. U.
Weismantel, R.
Gilles, E. D.
Klamt, S.
Schraven, B.
expertsonly
Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our largescale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.
© 2007 Saez-Rodriguez et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
2007
Article
http://edoc.mpg.de/322167
PLoS Computational Biology, v.3 (2007)
en
oai:edoc.mpg.de:3274162012-11-0119:94
Modular Analysis of Signal Transduction Networks
Saez-Rodriguez, Julio
expertsonly
Otto-von-Guericke Universität
2007
PhD-Thesis
http://edoc.mpg.de/327416
en
oai:edoc.mpg.de:3297392012-03-0619:94
Analysis of global control of Escherichia coli carbohydrate uptake
Kremling, A.
Bettenbrock, K.
Gilles, E. D.
expertsonly
Global control influences the regulation of many individual subsystems by superimposed regulator proteins. A prominent example is the control of carbohydrate uptake systems by the transcription factor Crp in Escherichia coli. A detailed understanding of the coordination of the control of individual transporters offers possibilities to explore the potential of microorganisms e.g. in biotechnology.
An o.d.e. based mathematical model is presented that maps a physiological parameter - the specific growth rate - to the sensor of the signal transduction unit, here a component of the bacterial phosphotransferase system (PTS), namely EIIA^Crr. The model describes the relation between the growth rate and the degree of phosphorylation of EIIA^Crr for a number of carbohydrates by a distinctive response curve, that differentiates between PTS transported carbohydrates and non-PTS carbohydrates. With only a small number of kinetic parameters, the model is able to describe a broad range of experimental steady-state and dynamical conditions. With a minor number of kinetic parameters, the model is able to describe a broad range of experimental steady-state and dynamical conditions.
2007
Article
http://edoc.mpg.de/329739
BMC Systems Biology, v.1 (2007)
en
oai:edoc.mpg.de:3312862012-03-0619:94
Host-pathogen systems biology : Logical modelling of hepatocyte growth factor and Helicobacter pylori induced c-Met signal transduction
Franke, R.
Mueller, M.
Wundrack, N.
Gilles, E. D.
Klamt, S.
Kaehne, T.
Naumann, M.
expertsonly
Background
The hepatocyte growth factor (HGF) stimulates mitogenesis, motogenesis, and morphogenesis in a wide range of tissues, including epithelial cells, on binding to the receptor tyrosine kinase c-Met. Abnormal c-Met signalling contributes to tumour genesis, in particular to the development of invasive and metastatic phenotypes. The human microbial pathogen Helicobacter pylori can induce chronic gastritis, peptic ulceration and more rarely, gastric adenocarcinoma. The H. pylori effector protein cytotoxin associated gene A (CagA), which is translocated via a type IV secretion system (T4SS) into epithelial cells, intracellularly modulates the c-Met receptor and promotes cellular processes leading to cell scattering, which could contribute to the invasiveness of tumour cells. Using a logical modelling framework, the presented work aims at analysing the c-Met signal transduction network and how it is interfered by H. pylori infection, which might be of importance for tumour development
Results
A logical model of HGF and H. pylori induced c-Met signal transduction is presented in this work. The formalism of logical interaction hypergraphs (LIH) was used to construct the network model. The molecular interactions included in the model were all assembled manually based on a careful meta-analysis of published experimental results. Our model reveals the differences and commonalities of the response of the network upon HGF and H. pylori induced c-Met signalling. As another important result, using the formalism of minimal intervention sets, phospholipase Cg1 (PLCg1) was identified as knockout target for repressing the activation of the extracellular signal regulated kinase 1/2 (ERK1/2), a signalling molecule directly linked to cell scattering in H. pylori infected cells. The model predicted only an effect on ERK1/2 for the H. pylori stimulus, but not for HGF treatment. This result could be confirmed experimentally in MDCK cells using a specific pharmacological inhibitor ag
ainst PLCg1. The in silico predictions for the knockout of two other network components were also verified experimentally.
Conclusions
This work represents one of the first approaches in the direction of host-pathogen systems biology aiming at deciphering signalling changes brought about by pathogenic bacteria. The suitability of our network model is demonstrated by an in silico prediction of a relevant target against pathogen infection.
© 2008 Franke et al; licensee BioMed Central Ltd.
[accessed June 6, 2008]
2008
Article
http://edoc.mpg.de/331286
BMC Systems Biology, v.2 (2008)
en
oai:edoc.mpg.de:3312892012-03-0519:94
Modeling the electron transport chain of purple non-sulfur bacteria
Klamt, S.
Grammel, H.
Straube, R.
Ghosh, R.
Gilles, E. D.
expertsonly
Purple non-sulfur bacteria (Rhodospirillaceae) have been extensively employed for studying principles of photosynthetic and respiratory electron transport phosphorylation and for investigating the regulation of gene expression in response to redox signals. Here, we use mathematical modeling to evaluate the steady-state behavior of the electron transport chain (ETC) in these bacteria under different environmental conditions. Elementary-modes analysis of a stoichiometric ETC model reveals nine operational modes. Most of them represent well-known functional states, however, two modes constitute reverse electron flow under respiratory conditions, which has been barely considered so far. We further present and analyze a kinetic model of the ETC in which rate laws of electron transfer steps are based on redox potential differences. Our model reproduces well-known phenomena of respiratory and photosynthetic operation of the ETC and also provides non-intuitive predictions. As one key result, model simulations demonstrate a stronger reduction of ubiquinone when switching from high-light to low-light conditions. This result is parameter insensitive and supports the hypothesis that the redox state of ubiquinone is a suitable signal for controlling photosynthetic gene expression.
© 2008 EMBO and Nature Publishing Group
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits distribution and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation or the creation of derivative works without specific permission.
2008
Article
http://edoc.mpg.de/331289
Molecular Systems Biology, v.4 (2008)
en
oai:edoc.mpg.de:3452872012-03-0619:94
Reduced modeling of signal transduction - a modular approach
Koschorreck, M.
Conzelmann, H.
Ebert, S.
Ederer, M.
Gilles, E. D.
expertsonly
2007
Article
http://edoc.mpg.de/345287
BMC Bioinformatics, v.8 (2007)
en
oai:edoc.mpg.de:3664522012-03-0519:94
Mathematical modeling and analysis of insulin clearance in vivo
Koschorreck, M.
Gilles, E. D.
expertsonly
Background:
Analyzing the dynamics of insulin concentration in the blood is necessary for a comprehensive understanding of the effects of insulin in vivo. Insulin removal from the blood has been addressed in many studies. The results are highly variable with respect to insulin clearance and the relative contributions of hepatic and renal insulin degradation.
Results:
We present a dynamic mathematical model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. The model describes renal and hepatic insulin degradation, pancreatic insulin secretion and nonspecific insulin binding in the liver. Hepatic insulin receptor activation by insulin binding, receptor internalization and autophosphorylation is explicitly included in the model. We present a detailed mathematical analysis of insulin degradation and insulin clearance. Stationary model analysis shows that degradation rates, relative contributions of the different tissues to total insulin degradation and insulin clearance highly depend on the insulin concentration.
Conclusions:
This study provides a detailed dynamic model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. Experimental data sets from literature are used for the model validation. We show that essential dynamic and stationary characteristics of insulin degradation are nonlinear and depend on the actual insulin concentration.
© 2008 Koschorreck and Gilles; licensee BioMed Central Ltd.
[accessed July 4, 2008]
2008
Article
http://edoc.mpg.de/366452
BMC Systems Biology, v.2 (2008)
en
oai:edoc.mpg.de:3799902011-04-1419:94
ProMoT : Modular Modeling for Systems Biology
Mirschel, S.
Steinmetz, K.
Rempel, M.
Ginkel, M.
Gilles, E. D.
expertsonly
Summary: PROMOT is a software designed to support efﬁcient and comprehensible modeling, visualization and analysis of complex and large-scale models. In recent years it has been improved in many aspects. New functionality especially tailored for Systems Biology has been added. It is now a very convenient tool for modular modeling.
Availability: PROMOT is an open source project and freely available at http://www.mpi-magdeburg.mpg.de/projects/promot/download.html.
2009
Article
http://edoc.mpg.de/379990
Bioinformatics, v.25, 687-689 (2009)
en
oai:edoc.mpg.de:3799932012-06-2119:94
An asymptotic analysis of intracellular signaling gradients arising from multiple small compartments
Straube, R.
Ward, M. J.
expertsonly
2009
Article
http://edoc.mpg.de/379993
SIAM Journal on Applied Mathematics, v.70, 248-269 (2009)
en
oai:edoc.mpg.de:3799942012-06-2119:94
Diffusion on a Sphere with Localized Traps : Mean First Passage Time, Eigenvalue Asymptotics, and Fekete Points
Coombs, D.
Straube, R.
Ward, M. J.
expertsonly
2009
Article
http://edoc.mpg.de/379994
SIAM Journal on Applied Mathematics, v.70, 302-332 (2009)
en
oai:edoc.mpg.de:3800142012-03-0219:94
Exact model reduction of combinatorial reaction networks
Conzelmann, H.
Fey, D.
Gilles, E. D.
expertsonly
2008
Article
http://edoc.mpg.de/380014
BMC Systems Biology, v.2 (2008)
en
oai:edoc.mpg.de:3959572012-03-0519:94
ALC : automated reduction of rule-based models
Koschorreck, M.
Gilles, E. D.
expertsonly
Background
Combinatorial complexity is a challenging problem for the modeling of cellular signal transduction since the association of a few proteins can give rise to an enormous amount of feasible protein complexes. The layer-based approach is an approximative, but accurate method for the mathematical modeling of signaling systems with inherent combinatorial complexity. The number of variables in the simulation equations is highly reduced and the resulting dynamic models show a pronounced modularity. Layer-based modeling allows for the modeling of systems not accessible previously.
Results
ALC (Automated Layer Construction) is a computer program that highly simplifies the building of reduced modular models, according to the layer-based approach. The model is defined using a simple but powerful rule-based syntax that supports the concepts of modularity and macrostates. ALC performs consistency checks on the model definition and provides the model output in different formats (C MEX, MATLAB, Mathematica and SBML) as ready-to-run simulation files. ALC also provides additional documentation files that simplify the publication or presentation of the models. The tool can be used offline or via a form on the ALC website.
Conclusions
ALC allows for a simple rule-based generation of layer-based reduced models. The model files are given in different formats as ready-to-run simulation files.
© 2008 Koschorreck and Gilles, licensee BioMed Central Ltd.
[accessed December 17, 2008]
2008
Article
http://edoc.mpg.de/395957
BMC Systems Biology, v.2 (2008)
en
oai:edoc.mpg.de:3973542012-10-3019:94
Mathematical Modeling of Biochemical Signal Transduction Pathways in Mammalian Cells – A Domain-Oriented Approach to Reduce Combinatorial Complexity
Conzelmann, Holger
expertsonly
Universität Stuttgart
2008
PhD-Thesis
http://edoc.mpg.de/397354
en
oai:edoc.mpg.de:4396562012-03-0519:94
Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction
Saez-Rodriguez, J.
Alexopoulos, L. G.
Epperlein, J.
Samaga, R.
Lauffenburger, D. A.
Klamt, S.
Sorger, P. K.
expertsonly
Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks.
This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits distribution and reproduction in any medium, provided the original author and source are credited. Creation of derivative works is permitted but the resulting work may be distributed only under the same or similar licence to this one. This licence does not permit commercial exploitation without specific permission. [accessed February 5, 2010]
2009
Article
http://edoc.mpg.de/439656
Molecular Systems Biology, v.5 (2009)
en
oai:edoc.mpg.de:4438252012-11-1419:94
Logical network of genotoxic stress-induced NF-kB signal transduction predicts putative target structures for therapeutic intervention strategies
Poltz, R.
Franke, R.
Schweitzer, K.
Klamt, S.
Gilles, E. D.
Naumann, M.
expertsonly
Genotoxic stress is induced by a broad range of DNA-damaging agents and could lead to a variety of human diseases including cancer. DNA damage is also therapeutically induced for cancer treatment with the aim to eliminate tumor cells. However, the effectiveness of radio- and chemotherapy is strongly hampered by tumor cell resistance. A major reason for radio- and chemotherapeutic resistances is the simultaneous activation of cell survival pathways resulting in the activation of the transcription factor nuclear factor-kappa B (NF-κB). Here, we present a Boolean network model of the NF-κB signal transduction induced by genotoxic stress in epithelial cells. For the representation and analysis of the model, we used the formalism of logical interaction hypergraphs. Model reconstruction was based on a careful meta-analysis of published data. By calculating minimal intervention sets, we identified p53-induced protein with a death domain (PIDD), receptor-interacting protein 1 (RIP1), and protein inhibitor of activated STAT y (PIASy) as putative therapeutic targets to abrogate NF-κB activation resulting in apoptosis. Targeting these structures therapeutically may potentiate the effectiveness of radio- and chemotherapy. Thus, the presented model allows a better understanding of the signal transduction in tumor cells and provides candidates as new therapeutic target structures.
© 2009 Poltz et al, publisher and licensee Dove Medical Press Ltd. This is an Open Access article
which permits unrestricted noncommercial use, provided the original work is properly cited. [accessed February 5th, 2010]
2009
Article
http://edoc.mpg.de/443825
Advances and Applications in Bioinformatics and Chemistry, v.2, 125-138 (2009)
en
oai:edoc.mpg.de:4474462012-10-3019:94
Thermokinetic Modeling and Model Reduction of Reaction Networks
Ederer, Michael
expertsonly
Universität Stuttgart
2009
PhD-Thesis
http://edoc.mpg.de/447446
en
oai:edoc.mpg.de:4474862012-10-3019:94
Reduced Order Modeling and Analysis of Cellular Signal Transduction
Koschorreck, Markus
expertsonly
Universität Stuttgart
2009
PhD-Thesis
http://edoc.mpg.de/447486
en
oai:edoc.mpg.de:4716062012-11-1419:94
ON/OFF and Beyond - A Boolean Model of Apoptosis
Schlatter, R.
Schmich, K.
Avalos Vizcarra, I.
Scheurich, P.
Sauter, T.
Borner, C.
Ederer, M.
Merfort, I.
Sawodny, O.
expertsonly
2009
Article
http://edoc.mpg.de/471606
PLoS Computational Biology, v.5 (2009)
en
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expertsonly
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http://edoc.mpg.de/570624
urn:ISBN:978-3-8440-0152-5
en