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1.  Logical network of genotoxic stress-induced NF-κB signal transduction predicts putative target structures for therapeutic intervention strategies 
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.
PMCID: PMC3169943  PMID: 21918620
apoptosis; Boolean network; cancer therapy; DNA-damage response; NF-κB
2.  ProMoT: modular modeling for systems biology 
Bioinformatics  2009;25(5):687-689.
Summary: The modeling tool ProMoT facilitates the efficient and comprehensible setup and editing of modular models coupled with customizable visual representations. Since its last major publication in 2003, ProMoT has gained new functionality in particular support of logical models, efficient editing, visual exploration, model validation and support for SBML.
Availability: ProMoT is an open source project and freely available at http://www.mpi-magdeburg.mpg.de/projects/promot/.
Contact: mirschel@mpi-magdeburg.mpg.de; mirschel@mpi-magdeburg.mpg.de
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btp029
PMCID: PMC2647835  PMID: 19147665
3.  ALC: automated reduction of rule-based models 
BMC Systems Biology  2008;2:91.
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.
Conclusion
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.
doi:10.1186/1752-0509-2-91
PMCID: PMC2636783  PMID: 18973705
4.  Mathematical modeling and analysis of insulin clearance in vivo 
BMC Systems Biology  2008;2:43.
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.
Conclusion
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.
doi:10.1186/1752-0509-2-43
PMCID: PMC2430945  PMID: 18477391
5.  Correlation between Growth Rates, EIIACrr Phosphorylation, and Intracellular Cyclic AMP Levels in Escherichia coli K-12▿  
Journal of Bacteriology  2007;189(19):6891-6900.
In Escherichia coli K-12, components of the phosphoenolpyruvate-dependent phosphotransferase systems (PTSs) represent a signal transduction system involved in the global control of carbon catabolism through inducer exclusion mediated by phosphoenolpyruvate-dependent protein kinase enzyme IIACrr (EIIACrr) (= EIIAGlc) and catabolite repression mediated by the global regulator cyclic AMP (cAMP)-cAMP receptor protein (CRP). We measured in a systematic way the relation between cellular growth rates and the key parameters of catabolite repression, i.e., the phosphorylated EIIACrr (EIIACrr∼P) level and the cAMP level, using in vitro and in vivo assays. Different growth rates were obtained by using either various carbon sources or by growing the cells with limited concentrations of glucose, sucrose, and mannitol in continuous bioreactor experiments. The ratio of EIIACrr to EIIACrr∼P and the intracellular cAMP concentrations, deduced from the activity of a cAMP-CRP-dependent promoter, correlated well with specific growth rates between 0.3 h−1 and 0.7 h−1, corresponding to generation times of about 138 and 60 min, respectively. Below and above this range, these parameters were increasingly uncoupled from the growth rate, which perhaps indicates an increasing role executed by other global control systems, in particular the stringent-relaxed response system.
doi:10.1128/JB.00819-07
PMCID: PMC2045212  PMID: 17675376
6.  Host-pathogen systems biology: logical modelling of hepatocyte growth factor and Helicobacter pylori induced c-Met signal transduction 
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 Cγ1 (PLCγ1) 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 against PLCγ1. The in silico predictions for the knockout of two other network components were also verified experimentally.
Conclusion
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.
doi:10.1186/1752-0509-2-4
PMCID: PMC2254585  PMID: 18194572
7.  Modeling the electron transport chain of purple non-sulfur bacteria 
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.
doi:10.1038/msb4100191
PMCID: PMC2238716  PMID: 18197174
photosynthesis; redox regulation; Rhodobacter sphaeroides; Rhodospirillum rubrum; ubiquinone pool
8.  Analysis of global control of Escherichia coli carbohydrate uptake 
BMC Systems Biology  2007;1:42.
Background
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.
Results
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 EIIACrr. 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.
Conclusion
The steady-state characteristic presented shows a relationship between the growth rate and the output of the sensor system PTS. The glycolytic flux that is measured by this sensor is a good indicator to represent the nutritional status of the cell.
doi:10.1186/1752-0509-1-42
PMCID: PMC2148058  PMID: 17854493
9.  Reduced modeling of signal transduction – a modular approach 
BMC Bioinformatics  2007;8:336.
Background
Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations.
Results
We introduce a new reduction technique, which allows building modularized and highly reduced models. Compared to existing approaches further reduction of signal transduction networks is possible. The method also provides a new modularization criterion, which allows to dissect the model into smaller modules that are called layers and can be modeled independently. Hallmarks of the approach are conservation relations within each layer and connection of layers by signal flows instead of mass flows. The reduced model can be formulated directly without previous generation of detailed model equations. It can be understood and interpreted intuitively, as model variables are macroscopic quantities that are converted by rates following simple kinetics. The proposed technique is applicable without using complex mathematical tools and even without detailed knowledge of the mathematical background. However, we provide a detailed mathematical analysis to show performance and limitations of the method. For physiologically relevant parameter domains the transient as well as the stationary errors caused by the reduction are negligible.
Conclusion
The new layer based reduced modeling method allows building modularized and strongly reduced models of signal transduction networks. Reduced model equations can be directly formulated and are intuitively interpretable. Additionally, the method provides very good approximations especially for macroscopic variables. It can be combined with existing reduction methods without any difficulties.
doi:10.1186/1471-2105-8-336
PMCID: PMC2216040  PMID: 17854494
10.  Visual setup of logical models of signaling and regulatory networks with ProMoT 
BMC Bioinformatics  2006;7:506.
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 .
doi:10.1186/1471-2105-7-506
PMCID: PMC1665465  PMID: 17109765
11.  Microaerophilic Cooperation of Reductive and Oxidative Pathways Allows Maximal Photosynthetic Membrane Biosynthesis in Rhodospirillum rubrum 
Applied and Environmental Microbiology  2003;69(11):6577-6586.
The purple nonsulfur bacterium Rhodospirillum rubrum has been employed to study physiological adaptation to limiting oxygen tensions (microaerophilic conditions). R. rubrum produces maximal levels of photosynthetic membranes when grown with both succinate and fructose as carbon sources under microaerophilic conditions in comparison to the level (only about 20% of the maximum) seen in the absence of fructose. Employing a unique partial O2 pressure (pO2) control strategy to reliably adjust the oxygen tension to values below 0.5%, we have used bioreactor cultures to investigate the metabolic rationale for this effect. A metabolic profile of the central carbon metabolism of these cultures was obtained by determination of key enzyme activities under microaerophilic as well as aerobic and anaerobic phototrophic conditions. Under aerobic conditions succinate and fructose were consumed simultaneously, whereas oxygen-limiting conditions provoked the preferential breakdown of fructose. Fructose was utilized via the Embden-Meyerhof-Parnas pathway. High levels of pyrophosphate-dependent phosphofructokinase activity were found to be specific for oxygen-limited cultures. No glucose-6-phosphate dehydrogenase activity was detected under any conditions. We demonstrate that NADPH is supplied mainly by the pyridine-nucleotide transhydrogenase under oxygen-limiting conditions. The tricarboxylic acid cycle enzymes are present at significant levels during microaerophilic growth, albeit at lower levels than those seen under fully aerobic growth conditions. Levels of the reductive tricarboxylic acid cycle marker enzyme fumarate reductase were also high under microaerophilic conditions. We propose a model by which the primary “switching” of oxidative and reductive metabolism is performed at the level of the tricarboxylic acid cycle and suggest how this might affect redox signaling and gene expression in R. rubrum.
doi:10.1128/AEM.69.11.6577-6586.2003
PMCID: PMC262267  PMID: 14602616

Results 1-11 (11)