Prof. Dr. rer. nat. Dr. h.c. Edda Klipp
Profil
Zusammenfassung
Edda Klipp entwickelt mathematische Modelle biologischer Systeme, um komplexe zelluläre Prozesse wie Stoffwechsel, Signalwege und Genregulation zu verstehen und vorherzusagen. Sie verbindet experimentelle Daten mit computergestützten Methoden, um biologische Netzwerke quantitativ zu beschreiben — von Hefezellen über Bakterien bis zu Krebserkrankungen. Diese Expertise ermöglicht es, biologische Systeme systematisch zu analysieren und gezielt zu manipulieren, etwa zur Identifikation von Angriffspunkten für Wirkstoffe oder zur Optimierung biotechnologischer Prozesse.
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Stammdaten
Identität, Organisation und Kontakt aus HU-FIS.
- Name
- Prof. Dr. rer. nat. Dr. h.c. Edda Klipp
- Titel
- Prof. Dr. rer. nat. Dr. h.c.
- Fakultät
- Lebenswissenschaftliche Fakultät
- Institut
- Institut für Biologie
- Arbeitsgruppe
- Theoretische Biophysik
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- Zuletzt gescrapt
- 27.6.2026, 01:08:58
Forschungsthemen40
Advanced Lecture Course in Systems Biology
Quelle ↗Zeitraum: 02/2018 - 07/2018 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp
Advanced Lecture Course in Systems Biology 2020
Quelle ↗Förderer: Andere inländische Stiftungen Zeitraum: 03/2020 - 03/2020 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp
Advanced Lecture Course on Systems Biology
Quelle ↗Zeitraum: 02/2016 - 03/2016 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp
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Publikationen25
Top 25 nach Zitationen — Quelle: OpenAlex (BAAI/bge-m3 embedded für Matching).
Science · 939 Zitationen · DOI
Outside In Acquisition and analysis of large data sets promises to move us toward a greater understanding of the mechanisms by which biological systems are dynamically regulated to respond to external cues. Now, two papers explore the responses of a bacterium to changing nutritional conditions (see the Perspective by Chalancon et al. ). Nicolas et al. (p. 1103 ) measured transcriptional regulation for more than 100 different conditions. Greater amounts of antisense RNA were generated than expected and appeared to be produced by alternative RNA polymerase targeting subunits called sigma factors. One transition, from malate to glucose as the primary nutrient, was studied in more detail by Buescher et al. (p. 1099 ) who monitored RNA abundance, promoter activity in live cells, protein abundance, and absolute concentrations of intracellular and extracellular metabolites. In this case, the bacteria responded rapidly and largely without transcriptional changes to life on malate, but only slowly adapted to use glucose, a shift that required changes in nearly half the transcription network. These data offer an initial understanding of why certain regulatory strategies may be favored during evolution of dynamic control systems.
Nature Biotechnology · 633 Zitationen · DOI
Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.
Nature Biotechnology · 606 Zitationen · DOI
Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.
Kooperationen56
Bestätigte Forscher↔Partner-Paare aus HU-FIS — Gold-Standard-Positive für das Matching.
Eukaryotic Unicellular Organism Biology – Systems Biology of the Control of Cell Growth and Proliferation
university
EU: Eine neue Generation von mikrobiellen Expressionswirten und -werkzeugen zur Herstellung von Biotherapeutika und hochwertigen Enzymen (SECRETERS)
university
Systematic Models for Biological Systems Engineering Training Network
university