ALC: automated reduction of rule-based models

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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, <it>Mathematica </it>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.</p> <p>Conclusion</p> <p>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.</p

Reduzierte Modellierung und Analyse zellulärer Signalübertragungssysteme

Cellular signal transduction is crucial for the regulation of many physiological processes. Understanding the signaling systems is of high medical interest because malfunctions can result in severe disorders such as cancer and diabetes. The behavior of these systems however, is often nonlinear and cannot be predicted intuitively. Therefore, mathematical modeling is necessary to understand and to analyze the system level properties of cellular signaling. Insulin is a hormone that has a major role in the regulation of glucose concentration in the blood and the cellular energy metabolism. This thesis provides a mathematical model describing hepatic insulin receptor activation as well as insulin degradation and synthesis in vivo. Model analysis shows that insulin clearance and the relative contributions of the liver and the kidney to insulin degradation are highly dependent on insulin concentration. At low concentrations, insulin is mainly degraded by the liver, whereas renal insulin degradation is predominant at high insulin concentrations. Insulin clearance is therefore only a valid measure for the state of the insulin metabolism when corresponding insulin concentrations are taken into account, which is not the case in many experimental studies. Building comprehensive models of complete signaling systems is in many cases impeded by combinatorial complexity. The association and modification of a few proteins can result in an enormous amount of feasible complexes and an equivalent amount of differential equations, when applying the conventional modeling approach. For example, 1.5*10^8 differential equations would be required to describe in detail the insulin signaling system, thereby establishing the need for a reduced order description. This thesis introduces layer-based modeling, a new approximative method for the modeling of cellular signaling systems. Layer-based modeling provides high reduction of the model size and simultaneously a high quality of approximation. The errors introduced by the approximation are dynamically and ultimately bounded. In special cases, the reduced model is exact for steady states or even represents an exact minimal realization of the system. Layer-based models show a pronounced modularity and the state variables have a direct biochemical interpretation. Reduced order model equations can be generated directly employing a procedure quite similar to conventional modeling. The preceding generation of a potentially very large conventional model is not necessary, which allows for the modeling of systems not accessible previously. Furthermore, the computer program Automated Layer Construction (ALC) is presented. Using ALC highly simplifies the generation of the model equations. The models are defined in terms of a rule-based model definition that utilizes a simple but powerful syntax. ALC allows the modeler to define layer-based models of very large systems with a relatively short and simple model definition. The output files of ALC are ready-to-run simulation files in the formats C MEX, MATLAB, Mathematica and SBML. ALC also provides the model equations in LaTeX and plain text format to simplify their publication or presentation. The application of ALC and layer-based modeling is demonstrated for a model definition for a layer-based model of insulin signaling with 51 ordinary differential equations (ODEs) approximating a conventional model with 1.5*10^8 ODEs.Die zelluläre Signalübertragung hat die Funktion, Signale aus der Umgebung in die Zelle zu übertragen, sie gegebenenfalls zu verstärken und die Aktivität von Zielmolekülen zu beeinflussen. Obwohl viele Komponenten der Signalübertragungssysteme bekannt sind und charakterisiert wurden, sind die Funktionsweise und das Verhalten der Netzwerke in vielen Fällen noch nicht vollständig verstanden. Die Ursache dafür ist, dass die Interaktion von Komponenten mit gut charakterisierten Eigenschaften zu neuen, oft unerwarteten Eigenschaften auf der Systemebene wie zum Beispiel Oszillationen oder Bistabilitäten führen kann. Die mathematische Modellierung ist eine systematische Herangehensweise, um diese versteckten Systemeigenschaften zu erkennen und zu verstehen. Ein Problem bei der Verwendung des konventionellen Modellierungsansatzes ist aber, dass die Assoziation von einigen wenigen Proteinen zu einer sehr hohen Anzahl von möglichen Komplexen und derselben Anzahl von für die Systembeschreibung notwendigen Differentialgleichungen führen kann. Aufgrund dieser kombinatorischen Komplexität kann das Insulinsignalsystem nur dann auf konventionelle Weise modelliert werden, wenn die Komplexbildung stark vereinfacht dargestellt wird und sich die Modellierung auf wenige Prozesse beschränkt. Ein solches konventionelles Modell, das die Dynamik des Insulinrezeptors in Leberzellen und die daran gekoppelte, durch den Abbau und die Synthese von Insulin verursachte Dynamik der Insulinkonzentration im Blut beschreibt, wird in dieser Arbeit vorgestellt und analysiert. Es wird gezeigt, dass die relativen Beiträge von Leber und Nieren zum Insulinabbau in hohem Maße von der Insulinkonzentration abhängen. Die Insulin Clearance ist ein häufig verwendeter Indikator für den Zustand des Insulinstoffwechsels. Es wird gezeigt, dass auch die Insulin Clearance aufgrund von Nichtlinearitäten des Insulinabbaus von der Insulinkonzentration im Blut abhängt. Daraus folgt, dass die Verwendung der Insulin Clearance nur zu sinnvollen Aussagen führen kann, wenn die zugehörige Insulinkonzentration angegeben wird. Die konventionelle Modellierung von Signalsystemen mit auftretender kombinatorischer Komplexität führt zu extrem umfangreichen Modellen, wenn umfassende mathematische Beschreibungen der Systeme erwünscht sind. Eine sinnvolle und handhabbare Beschreibung dieser komplexen Systeme ist nur mit Modellen reduzierter Ordnung möglich. In dieser Arbeit wird eine neue approximative Modellierungsmethode (layer-based modeling) beschrieben, die in hohem Maße für die Modellierung von Signalsystemen mit auftretender kombinatorischer Komplexität geeignet ist. Diese Methode zeichnet sich durch eine starke Verringerung der Systemordnung bei gleichzeitig hoher Approximationsgenauigkeit aus. Die resultierenden Modelle weisen eine ausgeprägte modulare Struktur auf, und die Zustandsgrößen haben eine direkte physiologische Bedeutung. Ein wesentlicher Unterschied zu anderen Modellreduktionsmethoden ist, dass das möglicherweise extrem große konventionelle Modell nicht erstellt werden muss, da das reduzierte Modell direkt generiert werden kann. Die für die Erstellung des reduzierten Modells notwendige Vorgehensweise weist große Ähnlichkeiten zur konventionellen Modellierung auf. Durch die direkte Erstellung der Modelle reduzierter Ordnung wird es möglich, Systeme zu modellieren, die zuvor nicht zugänglich waren. Die Erstellung von reduzierten Modellen für komplexe kombinatorische Systeme wird durch das in dieser Arbeit vorgestellte Computerprogramm ALC (Automated Layer Construction) in hohem Maße vereinfacht. Die Modelle werden durch eine regelbasierte Modelldefinition in einer leicht verständlichen, aber mächtigen Syntax definiert. Die modulare Struktur der reduzierten Modelle spiegelt sich in den Modelldefinitionen wider. Diese werden einer Vielzahl von Konsistenzkontrollen unterzogen, was dazu führt, dass die meisten Fehler in den Modelldefinitionen leicht zu finden und zu beseitigen sind. ALC ist frei verfügbar und kann lokal oder über die ALC-Webseite ausgeführt werden. Die durch die Modelldefinitionen definierten Modelle werden in verschiedenen Formaten (C MEX, MATLAB, Mathematica und SBML) als direkt verwendbare Simulationsdateien ausgegeben. Des weiteren werden die Modellgleichungen auch in LaTeX und Textformat ausgegeben, um die Veröffentlichung und Präsentation der Modelle zu vereinfachen. Das Leistungsvermögen von ALC wird anhand verschiedener Modelldefinitionen demonstriert. Unter anderem wird eine Modelldefinition für ein Modell des Insulinsignalsystems vorgestellt, die die kombinatorische Komplexität berücksichtigt. Diese Modelldefinition führt zu einem reduzierten Modell mit 51 Differentialgleichungen, welches ein konventionelles Modell mit 1,5*10^8 Differentialgleichungen approximiert

How to find soluble proteins: a comprehensive analysis of alpha/beta hydrolases for recombinant expression in <it>E. coli</it>

<p>Abstract</p> <p>Background</p> <p>In screening of libraries derived by expression cloning, expression of active proteins in <it>E. coli </it>can be limited by formation of inclusion bodies. In these cases it would be desirable to enrich gene libraries for coding sequences with soluble gene products in <it>E. coli </it>and thus to improve the efficiency of screening. Previously Wilkinson and Harrison showed that solubility can be predicted from amino acid composition (Biotechnology 1991, 9(5):443–448). We have applied this analysis to members of the alpha/beta hydrolase fold family to predict their solubility in <it>E. coli</it>. alpha/beta hydrolases are a highly diverse family with more than 1800 proteins which have been grouped into homologous families and superfamilies.</p> <p>Results</p> <p>The predicted solubility in <it>E. coli </it>depends on hydrolase size, phylogenetic origin of the host organism, the homologous family and the superfamily, to which the hydrolase belongs. In general small hydrolases are predicted to be more soluble than large hydrolases, and eukaryotic hydrolases are predicted to be less soluble in <it>E. coli </it>than prokaryotic ones. However, combining phylogenetic origin and size leads to more complex conclusions. Hydrolases from prokaryotic, fungal and metazoan origin are predicted to be most soluble if they are of small, medium and large size, respectively. We observed large variations of predicted solubility between hydrolases from different homologous families and from different taxa.</p> <p>Conclusion</p> <p>A comprehensive analysis of all alpha/beta hydrolase sequences allows more efficient screenings for new soluble alpha/beta hydrolases by the use of libraries which contain more soluble gene products. Screening of hydrolases from families whose members are hard to express as soluble proteins in <it>E. coli </it>should first be done in coding sequences of organisms from phylogenetic groups with the highest average of predicted solubility for proteins of this family. The tools developed here can be used to identify attractive target genes for expression using protein sequences published in databases. This analysis also directs the design of degenerate, family- specific primers to amplify new members from homologous families or superfamilies with a high probability of soluble alpha/beta hydrolases.</p

How to find soluble proteins : a comprehensive analysis of alpha/beta hydrolases for recombinant expression in E. coli

Background: In screening of libraries derived by expression cloning, expression of active proteinsin E. coli can be limited by formation of inclusion bodies. In these cases it would be desirable to enrich gene libraries for coding sequences with soluble gene products in E. coli and thus to improve the efficiency of screening. Previously Wilkinson and Harrison showed that solubility can be predicted from amino acid composition (Biotechnology 1991, 9(5):443-448). We have applied this analysis to members of the alpha/beta hydrolase fold family to predict their solubility in E. coli. alpha/beta hydrolases are a highly diverse family with more than 1800 proteins which have been grouped into homologous families and superfamilies. Results: The predicted solubility in E. coli depends on hydrolase size, phylogenetic origin of the host organism, the homologous family and the superfamily, to which the hydrolase belongs. In general small hydrolases are predicted to be more soluble than large hydrolases, and eukaryotic hydrolases are predicted to be less soluble in E. coli than prokaryotic ones. However, combining phylogenetic origin and size leads to more complex conclusions. Hydrolases from prokaryotic, fungal and metazoan origin are predicted to be most soluble if they are of small, medium and large size, respectively. We observed large variations of predicted solubility between hydrolases from different homologous families and from different taxa. Conclusion: A comprehensive analysis of all alpha/beta hydrolase sequences allows more efficient screenings for new soluble alpha/beta hydrolases by the use of libraries which contain more soluble gene products. Screening of hydrolases from families whose members are hard to express as soluble proteins in E. coli should first be done in coding sequences of organisms from phylogenetic groups with the highest average of predicted solubility for proteins of this family. The tools developed here can be used to identify attractive target genes for expression using protein sequences published in databases. This analysis also directs the design of degenerate, family- specific primers to amplify new members from homologous families or superfamilies with a high probability of soluble alpha/beta hydrolases

Decreasing δ13C and δ15N values in four coastal species at different trophic levels indicate a fundamental food-web shift in the southern North and Baltic Seas between 1988 and 2016

Marine ecosystems are exposed to increasing human pressures and climatic change worldwide. It has therefore become essential to describe ecosystem statuses with respect to multinational protection schemes, often necessitating long-term monitoring programmes. Changes in the food-web structure, which can be monitored via stable isotope measurements, represent an important descriptor of the status of marine ecosystems. We investigated long-term changes (29 years) in isotopic values (δ13C and δ15N) in four indicative organisms at different trophic levels in the southern North and Baltic Seas: bladderwrack (Fucus vesiculosus), blue mussel (Mytilus ssp.), eelpout (Zoarces viviparus), and herring gull (Larus argentatus). Time series analyses using generalised additive models revealed largely consistent declines in δ13C and δ15N throughout all trophic levels of the coastal food web at all study sites, indicating a clear change in these coastal regions from 1988 to 2016. There were no clear long-term patterns in egg biometrics for herring gulls, except for a consistent increase in eggshell thickness. The declines in stable isotope values were in line with the results of previous long-term studies of single higher-trophic-level species, which suggested that the noted changes were mainly caused by altered foraging patterns of the studied species. The current results demonstrate that declines in δ13Cand δ15N have occurred throughout the whole food web, not just in particular species. We discuss the possible reasons for the decrease in stable isotope values, including decreasing eutrophication and an increase in terrestrial carbon source

Occurrence of Pharmaceuticals and Personal Care Products in German Fish Tissue: A National Study

German Environment Specimen Bank (GESB) fish tissue samples, collected from 14 different GESB locations, were analyzed for 15 pharmaceuticals, 2 pharmaceutical metabolites, and 12 personal care products. Only 2 pharmaceuticals, diphenhydramine and desmethylsertraline, were measured above MDL. Diphenhydramine (0.04–0.07 ng g^–1 ww) and desmethylsertraline (1.65–3.28 ng g^–1 ww) were measured at 4 and 2 locations, respectively. The maximum concentrations of galaxolide (HHCB) (447 ng g^–1 ww) and tonalide (AHTN) (15 ng g^–1 ww) were measured at the Rehlingen sampling site in the Saar River. A significant decrease in HHCB and AHTN fish tissue concentrations was observed from 1995 to 2008 at select GESB sampling sites (<i>r</i>² = 0.69–0.89 for galaxolide and 0.89–0.97 for tonalide with <i>p</i> < 0.003). Galaxolide and tonalide fish tissue concentrations in Germany were ∼19× and ∼28× lower, respectively, as compared to fish tissue concentrations measured in a United States nationwide PPCP study conducted in 2006. Proximity of the sampling locations to the upstream wastewater treatment plant discharging point and mean annual flow at the sampling location were found to significantly predict galaxolide and tonalide fish tissue concentrations (HHCB: <i>r</i>² = 0.79, <i>p</i> = 0.021 and AHTN: <i>r</i>² = 0.81, <i>p</i> = 0.037) in Germany