Browsing by Author "Koschorreck, Markus"

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Open Access
How to find soluble proteins : a comprehensive analysis of alpha/beta hydrolases for recombinant expression in E. coli
(2005) Koschorreck, Markus; Fischer, Markus; Barth, Sandra; Pleiss, Jürgen
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.
Open Access
Reduced order modeling and analysis of cellular signal transduction
(2009) Koschorreck, Markus; Gilles, Ernst Dieter (Prof. Dr.-Ing. Dr. h.c. mult.)
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.