E. D. Gilles

The systems biology markup language (SBML) : a medium for representation and exchange of biochemical network models

MOTIVATION Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY The specification of SBML Level 1 is freely available from http://www.sbml.org/

PROMOT: A Modeling Tool for Chemical Processes

The novel process modeling tool PROMOT supports the object-oriented modeling of chemical processes for the simulation environment DIVA. In PROMOT, differential-algebraic process models can be built by aggregating structural and behavioral modeling entities that represent the topological structure or the dynamic and steady-state behavior, respectively, of the investigated chemical processes. Process models and their modeling entities may be defined either in an object-oriented modeling language or with a graphical user interface. This paper discusses the modeling concept, the modeling language, the knowledge representation aspects, and the implementation of PROMOT.

Modeling of inducer exclusion and catabolite repression based on a PTS-dependent sucrose and non-PTS-dependent glycerol transport systems in Escherichia coli K-12 and its experimental verification

We used genetically engineered sucrose positive Escherichia coli K-12 derivatives as a model system for the modeling and experimental verification of regulatory processes in bacteria. These cells take up and metabolize sucrose by the phosphoenolpyruvate (PEP)-dependent sucrose phosphotransferase system (Scr-PTS). Expression of the scr genes, which cluster in two different operons (scrYAB and scrK), is negatively controlled by the ScrR repressor. Additionally, expression of the scrYAB operon, but not of the scrK operon is positively controlled by the cAMP-CRP complex. Modeling of sucrose transport and metabolism through the Scr-system and of the scr gene expression has been performed using a modular and object-orientated new approach. To verify the model and identify important model parameters we measured in a first set of experiments induction kinetics of the scr genes after growth on glycerol using strains with single copy lacZ operon fusions in the scrK or scrY genes, respectively. In a second set of experiments an additional copy of the complete scr-regulon was integrated into the chromosome to construct diplogenotic strains. Differences were observed in the induction kinetics of the cAMP-CRP-dependent scrY operon compared to the cAMP-CRP independent scrK operon as well as between the single copy and the corresponding diplogenotic strains.

The galactose switch in Kluyveromyces lactis depends on nuclear competition between Gal4 and Gal1 for Gal80 binding

The Gal4 protein represents a universally functional transcription activator, which in yeast is regulated by protein-protein interaction of its transcription activation domain with the inhibitor Gal80. Gal80 inhibition is relieved via galactose-mediated Gal80-Gal1-Gal3 interaction. The Gal4-Gal80-Gal1/3 regulatory module is conserved between Saccharomyces cerevisiae and Kluyveromyces lactis. Here we demonstrate that K. lactis Gal80 (KlGal80) is a nuclear protein independent of the Gal4 activity status, whereas KlGal1 is detected throughout the entire cell, which implies that KlGal80 and KlGal1 interact in the nucleus. Consistently KlGal1 accumulates in the nucleus upon KlGAL80 overexpression. Furthermore, we show that the KlGal80-KlGal1 interaction blocks the galactokinase activity of KlGal1 and is incompatible with KlGal80-KlGal4-AD interaction. Thus, we propose that dissociation of KlGal80 from the AD forms the basis of KlGal4 activation in K. lactis. Quantitation of the dissociation constants for the KlGal80 complexes gives a much lower affinity for KlGal1 as compared with Gal4. Mathematical modeling shows that with these affinities a switch based on competition between Gal1 and Gal4 for Gal80 binding is nevertheless efficient provided two monomeric Gal1 molecules interact with dimeric Gal80. Consistent with such a mechanism, analysis of the sedimentation behavior by analytical ultracentrifugation demonstrates the formation of a heterotetrameric KlGal80-KlGal1 complex of 2:2 stoichiometry.

An integrative model links multiple inputs and signaling pathways to the onset of DNA synthesis in hepatocytes

During liver regeneration, quiescent hepatocytes re‐enter the cell cycle to proliferate and compensate for lost tissue. Multiple signals including hepatocyte growth factor, epidermal growth factor, tumor necrosis factor α, interleukin‐6, insulin and transforming growth factor β orchestrate these responses and are integrated during the G1 phase of the cell cycle. To investigate how these inputs influence DNA synthesis as a measure for proliferation, we established a large‐scale integrated logical model connecting multiple signaling pathways and the cell cycle. We constructed our model based upon established literature knowledge, and successively improved and validated its structure using hepatocyte‐specific literature as well as experimental DNA synthesis data. Model analyses showed that activation of the mitogen‐activated protein kinase and phosphatidylinositol 3‐kinase pathways was sufficient and necessary for triggering DNA synthesis. In addition, we identified key species in these pathways that mediate DNA replication. Our model predicted oncogenic mutations that were compared with the COSMIC database, and proposed intervention targets to block hepatocyte growth factor‐induced DNA synthesis, which we validated experimentally. Our integrative approach demonstrates that, despite the complexity and size of the underlying interlaced network, logical modeling enables an integrative understanding of signaling‐controlled proliferation at the cellular level, and thus can provide intervention strategies for distinct perturbation scenarios at various regulatory levels.

Dynamics of Proximal Signaling Events after TCR/CD8-Mediated Induction of Proliferation or Apoptosis in Mature CD8+ T Cells

Engagement of the TCR can induce different functional outcomes such as activation, proliferation, survival, or apoptosis. How the TCR-mediated signaling cascades generating these distinct cellular responses are organized on the molecular level is so far not completely understood. To obtain insight into this question, we analyzed TCR/CD8-mediated signaling events in mature OT-I TCR transgenic T cells under conditions of stimulation that lead to either proliferation or apoptosis. These experiments revealed major differences in the phosphorylation dynamics of LAT, ZAP70, protein kinase B, phospholipase C-γ1, protein kinase D1, and ERK1/2. Moreover, input signals leading to apoptosis induced a strong, but transient activation of ERK1/2 mainly at sites of TCR-engagement. In contrast, stimuli promoting survival/proliferation generated a low and sustained activation of ERK1/2, which colocalizes with Ras in recycling endosomal vesicles. The transient activation of ERK1/2 under pro-apoptotic conditions of stimulation is at least partially due to the rapid polyubiquitination and subsequent degradation of ZAP70, whereas the sustained activation of ERK1/2 under survival promoting conditions is paralleled by the induction/phosphorylation of anti-apoptotic molecules such as protein kinase B and Bcl-xL. Collectively, our data provide signaling signatures that are associated with proliferation or apoptosis of T cells.

Multistability of signal transduction motifs

Protein domains are the basic units of signalling processes. The mechanisms they are involved in usually follow recurring patterns, such as phosphorylation/dephosphorylation cycles. A set of common motifs was defined and their dynamic models were analysed with respect to number and stability of steady states. In a first step, Feinbergs chemical reaction network theory was used to determine whether a motif can show multistationarity or not. The analysis revealed that, apart from double-step activation motifs including a distributive mechanism, only those motifs involving an autocatalytic reaction can show multistationarity. To further characterise these motifs, a large number of randomly chosen parameter sets leading to bistability was generated, followed by a bifurcation analysis of each parameter set and a statistical evaluation of the results. The statistical results can be used to explore robustness against noise, pointing to the observation that multistationarity at the single-motif level may not be a robust property; the range of protein concentrations compatible with multistationarity is fairly narrow. Furthermore, experimental evidence suggests that protein concentrations vary substantially between cells. Considering a motif designed to be a bistable switch, this implies that fluctuation of protein concentrations between cells would prevent a significant proportion of motifs from acting as a switch. The authors consider this to be a first step towards a catalogue of fully characterised signalling modules.

Object-oriented modeling of distillation processes

Abstract The process modeling tool ProMoT supports the object-oriented and equation-based modeling of chemical processes for the process simulation environment Diva . This contribution demonstrates the modeling methodology of ProMoT and the advantages of object-oriented modeling by looking at the design of a knowledge base containing reusable modeling entities for modeling (reactive) distillation processes.

Analysis of an apoptotic core model focused on experimental design using artificial data

The activation of caspases is a central mechanism in apoptosis. To gain further insights into complex processes like this, mathematical modelling using ordinary differential equations (ODEs) can be a very powerful research tool. Unfortunately, the lack of measurement data is a common problem in building such kinetic models, because it practically constrains the identifiability of the model parameters. An existing mathematical model of caspase activation during apoptosis was used in order to design future experimental setups that will help to maximise the obtained information. For this purpose, artificial measurement data are generated in silico to simulate potential experiments, and the model is fitted to this data. The model is also analysed using observability gramian and sensitivity analyses. The used analysis methods are compared. The artificial data approach allows one to make conclusions about system properties, identifiability of parameters and the potential information content of additional measurements for the used caspase activation model. The latter facilitates to improve the experimental design of further measurements significantly. The performed analyses reveal that several kinetic parameters are not at all, or only scarcely, identifiable, and that measurements of activated caspase 8 will maximally improve the parameter estimates. Furthermore, we can show that many assays with inhibitor of apoptosis protein (IAP) knockout cells only provide redundant information for our needs and as such do not have to be carried out.

Symbolic discretization of population models for process simulation

Publisher Summary This chapter discusses the symbolic discretization of population models for process simulation. In this connection, it describes the architecture of the symbolic preprocessing for the simulation environment. In addition, population balance equations are also discussed in detail in the chapter. The population balance approach characterizes particles of the dispersed phase by internal coordinates such as the particle length in crystallization processes. This approach allows integration of submodels for the microscopic phenomena on a macroscopic scale into a model for the overall process unit. Because of the contained integrals and the hyperbolic nature of the population balance equations, standardized discretization schemes can only be applied on fine grids for satisfactory simulation results. Therefore, advanced discretization methods have been investigated with the objective to reduce the computational effort—essentially nonoscillatory schemes and the robust upwind scheme. The main advantage of both methods is the maintenance of sharp profiles during dynamic simulations.