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E. D. Gilles

Researcher at Max Planck Society

Publications -  46
Citations -  4103

E. D. Gilles is an academic researcher from Max Planck Society. The author has contributed to research in topics: Systems biology & Nonlinear system. The author has an hindex of 17, co-authored 46 publications receiving 3932 citations. Previous affiliations of E. D. Gilles include University of Stuttgart.

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Journal ArticleDOI

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

TL;DR: This work summarizes the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks, a software-independent language for describing models common to research in many areas of computational biology.
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The organization of metabolic reaction networks. III. Application for diauxic growth on glucose and lactose.

TL;DR: A mathematical model to describe carbon catabolite repression in Escherichia coli is developed and in part validated and all experiments could be sufficiently described with a single set of parameters.
Journal ArticleDOI

Modular analysis of signal transduction networks

TL;DR: The mechanisms that cells have developed to process information are described, and the decomposition of signalling networks into subsystems is discussed, and some simple criteria for the analysis of the resulting units are introduced.
Book ChapterDOI

Real-Time Optimization for Large Scale Processes: Nonlinear Model Predictive Control of a High Purity Distillation Column

TL;DR: An experimental proof-of-concept of the application of NMPC for large scale systems using specialized dynamic optimization strategies to a nontrivial process control example, namely the control of a high purity binary distillation column.
Journal ArticleDOI

The organization of metabolic reaction networks. II. Signal processing in hierarchical structured functional units.

TL;DR: Based on the analysis of molecular interactions of proteins with DNA binding sites, a new approach to developing mathematical models describing gene expression is introduced that will be used to decompose complex reaction schemes by assigning each regulator protein to one level in the hierarchy.