Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

Profil

• 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

• An Experimental Network for Functional Integration

  Quelle ↗

  Zeitraum: 01/2009 - 05/2011 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Biological computation built on cell communication systems

  Quelle ↗

  Zeitraum: 04/2009 - 02/2011 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• ColoNET: A Systems Biology Approach for Integrating Molecular Diagnostics and Targeted Therapy in Colorectal Cancer

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 03/2009 - 06/2012 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Early Metabolic Injury – LiSym/EMI

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2016 - 06/2021 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Engineering of New-Generation Protein Secretion Systems

  Quelle ↗

  Zeitraum: 01/2015 - 12/2018 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• EU: Eine neue Generation von mikrobiellen Expressionswirten und -werkzeugen zur Herstellung von Biotherapeutika und hochwertigen Enzymen (SECRETERS)

  Quelle ↗

  Förderer: Horizon 2020: Innovative Training Network ITN Zeitraum: 01/2019 - 06/2023 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Eukaryotic Unicellular Organism Biology – Systems Biology of the Control of Cell Growth and Proliferation

  Quelle ↗

  Zeitraum: 01/2009 - 03/2013 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• EXC 2046 1 AG Klipp

  Quelle ↗

  Förderer: DFG Exzellenzstrategie Cluster Zeitraum: 01/2019 - 12/2020 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• EXC 2046: Berlin Mathematics Research Center (MATH+)

  Quelle ↗

  Förderer: DFG Exzellenzstrategie Cluster Zeitraum: 01/2019 - 12/2024 Projektleitung: Prof. Dr. Caren Tischendorf, Prof. Dr. Michael Hintermüller, Prof. Dr. Max Klimm, Prof. Dr. Dörte Kreher, Prof. Chris Wendl, Prof. Dr. Bettina Rösken-Winter, Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Gene interaction networks and models of cation homeostasis in Saccharomyces cerevisiae

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 04/2009 - 08/2010 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• GerontoSys: Mitochondriale Netzwerke von Signalwegen bei der Alterung und der Lebensspannenkontrolle – ein systembiologischer Ansatz

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2010 - 12/2012 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• GRK 1360/3: Genomics and Systems Biology of Molecular Networks

  Quelle ↗

  Förderer: DFG Graduiertenkolleg Zeitraum: 10/2010 - 05/2012 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• GRK 1772/1: Computergestützte Systembiologie

  Quelle ↗
  409-01 · Theoretische Informatik

  Förderer: DFG Graduiertenkolleg Zeitraum: 03/2011 - 07/2015 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• GRK 1772/2: Computergestützte Systembiologie

  Quelle ↗

  Förderer: DFG Graduiertenkolleg Zeitraum: 08/2015 - 01/2020 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• IGRK 1360/1: Genomics and Systems Biology of Molecular Networks

  Quelle ↗

  Förderer: DFG Graduiertenkolleg Zeitraum: 04/2006 - 09/2010 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• IGRK 2290/1: Grenzen überwinden: Molekulare Interaktionen bei Malaria

  Quelle ↗

  Förderer: DFG Graduiertenkolleg Zeitraum: 09/2017 - 02/2022 Projektleitung: Prof. Dr. Kai Matuschewski

• IGRK 2290: Grenzen überwinden: Molekulare Interaktionen bei Malaria

  Quelle ↗

  Förderer: DFG Graduiertenkolleg Zeitraum: 09/2017 - 02/2024 Projektleitung: Prof. Dr. Kai Matuschewski

• Individualisierter mikrofluidischer Multiorgan-Chip für die Analyse von substanzinduzierter Toxizität – für eine verbesserte Arzrneimittelsicherheit

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 10/2014 - 09/2017 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Influenza Systems Virology – Molecular Signatures of Permissive Virus Infection (K)

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2013 - 05/2016 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp, Prof. Dr. rer. nat. Andreas Herrmann

• Integrated analysis of aquaporin structure and function

  Quelle ↗

  Zeitraum: 12/2008 - 10/2010 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Interdisziplinäres Training Systembiologie

  Quelle ↗

  Zeitraum: 01/2005 - 12/2008 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Iztli Peptides: Towards Selective Celle Penetrating Peptides

  Quelle ↗

  Förderer: Alexander von Humboldt-Stiftung Zeitraum: 01/2014 - 12/2016 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Mathematische Modellierung des Wnt/beta-catenin Signalweges

  Quelle ↗

  Zeitraum: 09/2004 - 02/2009 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Modellierung der Ionenhomöostase in der Hefe saccharomyces cerevisiae (Transculent-2)

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 05/2010 - 10/2013 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• OncoPath: Dissecting and Modelling Vulnerabilities of Oncogeneic Pathways and Metabolism in Solid Cancers (K)

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2013 - 06/2016 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Open Access Publikationsbeihilfe für Artikel: "A comprehensive, mechanistically detailed, and executable model of the cell division cycle in Saccharomyces cerevisiae."

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 03/2019 - 09/2019 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Pathogenomics and Systems Biology of Fungal Infections – An Integrative Approach

  Quelle ↗

  Zeitraum: 10/2008 - 09/2012 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• SFB 618: Computerbasierte Vorhersage von optimalen Wirkstofforten in biochemischen Netzwerken (TP C8)

  Quelle ↗

  Förderer: DFG Sonderforschungsbereich Zeitraum: 07/2009 - 06/2013 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• SFB 740/1: Spatial Modeling of Gradient Sensing Exemplified for the Yeast Pheromone Response (TP D 06/08)

  Quelle ↗

  Förderer: DFG Sonderforschungsbereich Zeitraum: 01/2010 - 12/2018 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• SFB/TRR 175/1: Subzelluläre Modellierung metabolischer Akklimatisation (TP D03)

  Quelle ↗

  Förderer: DFG Sonderforschungsbereich Zeitraum: 07/2016 - 12/2020 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Single Molecule RNA Biology – Dynamics and Function of RNA From Transcription to Degradation

  Quelle ↗

  Förderer: Einstein Stiftung Berlin Zeitraum: 12/2013 - 11/2016 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Systematic Models for Biological Systems Engineering Training Network

  Quelle ↗

  Förderer: Horizon 2020: Innovative Training Network ITN Zeitraum: 09/2015 - 08/2019 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Systembiologische Analyse von „Omics“-Daten zur Identifizierung von Targets von Pflanzenschutzwirkstoffen

  Quelle ↗

  Zeitraum: 01/2012 - 12/2014 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Systems Biology of Mycobacterium tuberculosis

  Quelle ↗

  Zeitraum: 04/2010 - 09/2014 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Towards an understanding of dynamic transcriptional regulation at global scale in bacteria: a systems biology approach

  Quelle ↗

  Zeitraum: 02/2009 - 04/2011 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Untersuchung der Anastasis in Bäckerhefe

  Quelle ↗

  Förderer: Alexander von Humboldt-Stiftung: Forschungskostenzuschuss Zeitraum: 06/2019 - 12/2019 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Wirkstoffvermittelte Induktion von pluripotenten humanen Stammzellen aus humanen somatischen Zellen

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 01/2009 - 06/2012 Projektleitung: Prof. Dr. rer. nat. Dr. h.c. Edda Klipp

• Fundació Centre de Regulació Genòmica

  KPT

  27 Treffer85.0%
  • Systems Biology of Mycobacterium tuberculosis
    K

    85.0%

• DSM Food Specialities BV

  KPT

  87 Treffer85.0%
  • Engineering of New-Generation Protein Secretion Systems
    K

    85.0%

• Europäisches Laboratorium für Molekularbiologie (Heidelberg)

  KPT

  26 Treffer85.0%
  • Systems Biology of Mycobacterium tuberculosis
    K

    85.0%

• novozymess

  KPT

  89 Treffer85.0%
  • Engineering of New-Generation Protein Secretion Systems
    K

    85.0%

• Insilico Biotechnology AG

  KPT

  143 Treffer85.0%
  • Systematic Models for Biological Systems Engineering Training Network
    K

    85.0%

• Silicolife LDA

  KPT

  142 Treffer85.0%
  • Systematic Models for Biological Systems Engineering Training Network
    K

    85.0%

• UCB Celltech

  KPT

  86 Treffer85.0%
  • Engineering of New-Generation Protein Secretion Systems
    K

    85.0%

• Instytut Biochemii i biofizyki

  KPT

  27 Treffer85.0%
  • Systems Biology of Mycobacterium tuberculosis
    K

    85.0%

• Protatuans-Etaireia Ereynas Viotechologias Monoprosopi Etaireia Periorisments Eythinis

  KPT

  141 Treffer85.0%
  • Systematic Models for Biological Systems Engineering Training Network
    K

    85.0%

• Omnia Molecular, S.L.

  KPT

  23 Treffer85.0%
  • Systems Biology of Mycobacterium tuberculosis
    K

    85.0%

• Condition-Dependent Transcriptome Reveals High-Level Regulatory Architecture in <i>Bacillus subtilis</i>

  2012

  Science · 932 Zitationen · DOI

  Bacteria adapt to environmental stimuli by adjusting their transcriptomes in a complex manner, the full potential of which has yet to be established for any individual bacterial species. Here, we report the transcriptomes of Bacillus subtilis exposed to a wide range of environmental and nutritional conditions that the organism might encounter in nature. We comprehensively mapped transcription units (TUs) and grouped 2935 promoters into regulons controlled by various RNA polymerase sigma factors, accounting for ~66% of the observed variance in transcriptional activity. This global classification of promoters and detailed description of TUs revealed that a large proportion of the detected antisense RNAs arose from potentially spurious transcription initiation by alternative sigma factors and from imperfect control of transcription termination.

• Minimum information requested in the annotation of biochemical models (MIRIAM)

  2005

  Nature Biotechnology · 630 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.

• A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

  2008

  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.

• Integrative model of the response of yeast to osmotic shock

  2005

  Nature Biotechnology · 451 Zitationen · DOI

• Systems Biology in Practice

  2005

  390 Zitationen · DOI

  Basic principles -- Biology in a nutshell -- Mathematics in a nutshell -- Experimental techniques in a nutshell -- Metabolism -- Signal transduction -- Selected biological processes -- Modeling of gene expression -- Analysis of gene expression data -- Evolution and self-organization -- Data integration -- What's next? -- Databases and tools on the Internet -- Modeling tools

• Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

  2017

  Nature Communications · 341 Zitationen · DOI

  As interactions between the immune system and tumour cells are governed by a complex network of cell-cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patient's response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication-specific and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types, as well as three T cell subtypes. Using the tumour-derived RGEPs, we can estimate the content of many tumours associated immune and stromal cell types, their therapeutically relevant ratios, as well as an improved gene expression profile of the malignant cells.

• Triple‐negative breast cancer: Present challenges and new perspectives

  2010

  Molecular Oncology · 300 Zitationen · DOI

  Triple-negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined 'omics' approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi-dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.

• Global Network Reorganization During Dynamic Adaptations of <i>Bacillus subtilis</i> Metabolism

  2012

  Science · 278 Zitationen · DOI

  Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control programs.

• Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems

  2015

  Bioinformatics · 263 Zitationen · DOI

  Supplementary data are available at Bioinformatics online.

• Bringing metabolic networks to life: convenience rate law and thermodynamic constraints

  2006

  Theoretical Biology and Medical Modelling · 255 Zitationen · DOI

• Mathematical modeling of intracellular signaling pathways

  2006

  BMC Neuroscience · 167 Zitationen · DOI

  Dynamic modeling and simulation of signal transduction pathways is an important topic in systems biology and is obtaining growing attention from researchers with experimental or theoretical background. Here we review attempts to analyze and model specific signaling systems. We review the structure of recurrent building blocks of signaling pathways and their integration into more comprehensive models, which enables the understanding of complex cellular processes. The variety of mechanisms found and modeling techniques used are illustrated with models of different signaling pathways. Focusing on the close interplay between experimental investigation of pathways and the mathematical representations of cellular dynamics, we discuss challenges and perspectives that emerge in studies of signaling systems.

• Biophysical properties of Saccharomyces cerevisiae and their relationship with HOG pathway activation

  2010

  European Biophysics Journal · 160 Zitationen · DOI

  Parameterized models of biophysical and mechanical cell properties are important for predictive mathematical modeling of cellular processes. The concepts of turgor, cell wall elasticity, osmotically active volume, and intracellular osmolarity have been investigated for decades, but a consistent rigorous parameterization of these concepts is lacking. Here, we subjected several data sets of minimum volume measurements in yeast obtained after hyper-osmotic shock to a thermodynamic modeling framework. We estimated parameters for several relevant biophysical cell properties and tested alternative hypotheses about these concepts using a model discrimination approach. In accordance with previous reports, we estimated an average initial turgor of 0.6 ± 0.2 MPa and found that turgor becomes negligible at a relative volume of 93.3 ± 6.3% corresponding to an osmotic shock of 0.4 ± 0.2 Osm/l. At high stress levels (4 Osm/l), plasmolysis may occur. We found that the volumetric elastic modulus, a measure of cell wall elasticity, is 14.3 ± 10.4 MPa. Our model discrimination analysis suggests that other thermodynamic quantities affecting the intracellular water potential, for example the matrix potential, can be neglected under physiological conditions. The parameterized turgor models showed that activation of the osmosensing high osmolarity glycerol (HOG) signaling pathway correlates with turgor loss in a 1:1 relationship. This finding suggests that mechanical properties of the membrane trigger HOG pathway activation, which can be represented and quantitatively modeled by turgor.

• Selective benefits of damage partitioning in unicellular systems and its effects on aging

  2008

  Proceedings of the National Academy of Sciences · 158 Zitationen · DOI

  Cytokinesis in unicellular organisms sometimes entails a division of labor between cells leading to lineage-specific aging. To investigate the potential benefits of asymmetrical cytokinesis, we created a mathematical model to simulate the robustness and fitness of dividing systems displaying different degrees of damage segregation and size asymmetries. The model suggests that systems dividing asymmetrically (size-wise) or displaying damage segregation can withstand higher degrees of damage before entering clonal senescence. When considering population fitness, a system producing different-sized progeny like budding yeast is predicted to benefit from damage retention only at high damage propagation rates. In contrast, the fitness of a system of equal-sized progeny is enhanced by damage segregation regardless of damage propagation rates, suggesting that damage partitioning may also provide an evolutionary advantage in systems dividing by binary fission. Indeed, by using Schizosaccharomyces pombe as a model, we experimentally demonstrate that damaged proteins are unevenly partitioned during cytokinesis and the damage-enriched sibling suffers from a prolonged generation time and accelerated aging. This damage retention in S. pombe is, like in Saccharomyces cerevisiae, Sir2p- and cytoskeleton-dependent, suggesting this to be an evolutionarily conserved mechanism. We suggest that sibling-specific aging may be a result of the strong selective advantage of damage segregation, which may be more common in nature than previously anticipated.

• Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation

  2010

  Bioinformatics · 145 Zitationen · DOI

  Supplementary data are available at Bioinformatics online.

• Quantitative Analysis of Glycerol Accumulation, Glycolysis and Growth under Hyper Osmotic Stress

  2013

  PLoS Computational Biology · 125 Zitationen · DOI

  We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.

• Modelling the dynamics of the yeast pheromone pathway

  2004

  Yeast · 122 Zitationen · DOI

  We present a mathematical model of the dynamics of the pheromone pathways in haploid yeast cells of mating type MATa after stimulation with pheromone alpha-factor. The model consists of a set of differential equations and describes the dynamics of signal transduction from the receptor via several steps, including a G protein and a scaffold MAP kinase cascade, up to changes in the gene expression after pheromone stimulation in terms of biochemical changes (complex formations, phosphorylations, etc.). The parameters entering the models have been taken from the literature or adapted to observed time courses or behaviour. Using this model we can follow the time course of the various complex formation processes and of the phosphorylation states of the proteins involved. Furthermore, we can explain the phenotype of more than a dozen well-characterized mutants and also the graded response of yeast cells to varying concentrations of the stimulating pheromone.

• Prediction of temporal gene expression

  2002

  European Journal of Biochemistry · 119 Zitationen · DOI

  A computational approach is used to analyse temporal gene expression in the context of metabolic regulation. It is based on the assumption that cells developed optimal adaptation strategies to changing environmental conditions. Time-dependent enzyme profiles are calculated which optimize the function of a metabolic pathway under the constraint of limited total enzyme amount. For linear model pathways it is shown that wave-like enzyme profiles are optimal for a rapid substrate turnover. For the central metabolism of yeast cells enzyme profiles are calculated which ensure long-term homeostasis of key metabolites under conditions of a diauxic shift. These enzyme profiles are in close correlation with observed gene expression data. Our results demonstrate that optimality principles help to rationalize observed gene expression profiles.

• Cell Size at S Phase Initiation: An Emergent Property of the G1/S Network

  2007

  PLoS Computational Biology · 118 Zitationen · DOI

  The eukaryotic cell cycle is the repeated sequence of events that enable the division of a cell into two daughter cells. It is divided into four phases: G1, S, G2, and M. Passage through the cell cycle is strictly regulated by a molecular interaction network, which involves the periodic synthesis and destruction of cyclins that bind and activate cyclin-dependent kinases that are present in nonlimiting amounts. Cyclin-dependent kinase inhibitors contribute to cell cycle control. Budding yeast is an established model organism for cell cycle studies, and several mathematical models have been proposed for its cell cycle. An area of major relevance in cell cycle control is the G1 to S transition. In any given growth condition, it is characterized by the requirement of a specific, critical cell size, PS, to enter S phase. The molecular basis of this control is still under discussion. The authors report a mathematical model of the G1 to S network that newly takes into account nucleo/cytoplasmic localization, the role of the cyclin-dependent kinase Sic1 in facilitating nuclear import of its cognate Cdk1-Clb5, Whi5 control, and carbon source regulation of Sic1 and Sic1-containing complexes. The model was implemented by a set of ordinary differential equations that describe the temporal change of the concentration of the involved proteins and protein complexes. The model was tested by simulation in several genetic and nutritional setups and was found to be neatly consistent with experimental data. To estimate PS, the authors developed a hybrid model including a probabilistic component for firing of DNA replication origins. Sensitivity analysis of PS provides a novel relevant conclusion: PS is an emergent property of the G1 to S network that strongly depends on growth rate.

• Loss of hepatic Mboat7 leads to liver fibrosis

  2020

  Gut · 117 Zitationen · DOI

  Mboat7 deficiency in mice and human points to an inflammation-independent pathway of liver fibrosis that may be mediated by lipid signalling and a potentially targetable treatment option in NAFLD.

• Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

  2013

  PLoS ONE · 115 Zitationen · DOI

  The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

• Information theory based approaches to cellular signaling

  2011

  Biochimica et Biophysica Acta (BBA) - General Subjects · 114 Zitationen · DOI

• Global dynamic optimization approach to predict activation in metabolic pathways

  2014

  BMC Systems Biology · 107 Zitationen · DOI

  The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints.

• Fungal Hsp90: a biological transistor that tunes cellular outputs to thermal inputs

  2012

  Nature Reviews Microbiology · 105 Zitationen · DOI

• Quantitative analysis of transient and sustained transforming growth factor‐β signaling dynamics

  2011

  Molecular Systems Biology · 104 Zitationen · DOI

  Mammalian cells can decode the concentration of extracellular transforming growth factor-β (TGF-β) and transduce this cue into appropriate cell fate decisions. How variable TGF-β ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-β stimulation. The TGF-β pathway elicits a transient signaling response to a single pulse of TGF-β stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-β pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-β levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-β pathway might be critical for cell fate determination.

• Annotation and merging of SBML models with semanticSBML

  2009

  Bioinformatics · 101 Zitationen · DOI

  Abstract Summary: Systems Biology Markup Language (SBML) is the leading exchange format for mathematical models in Systems Biology. Semantic annotations link model elements with external knowledge via unique database identifiers and ontology terms, enabling software to check and process models by their biochemical meaning. Such information is essential for model merging, one of the key steps towards the construction of large kinetic models. SemanticSBML is a tool that helps users to check and edit MIRIAM annotations and SBO terms in SBML models. Using a large collection of biochemical names and database identifiers, it supports modellers in finding the right annotations and in merging existing models. Initially, an element matching is derived from the MIRIAM annotations and conflicting element attributes are categorized and highlighted. Conflicts can then be resolved automatically or manually, allowing the user to control the merging process in detail. Availability: SemanticSBML comes as a free software written in Python and released under the GPL 3. A Debian package, a source package for other Linux distributions, a Windows installer and an online version of semanticSBML with limited functionality are available at http://www.semanticsbml.org. A preinstalled version can be found on the Linux live DVD SB.OS, available at http://www.sbos.eu. Contact: wolfram.liebermeister@biologie.hu-berlin.de Supplementary information: Supplementary data are available at Bioinformatics online.

• Aberystwyth Universität

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• Academisch Ziekenhuis Groningen

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• Aristotelio Panepistimo Thessalonikis

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• Australian National University

  IGRK 2290: Grenzen überwinden: Molekulare Interaktionen bei Malaria

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• Centre national de la recherche scientifique

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• Charité – Universitätsmedizin Berlin

  IGRK 2290/1: Grenzen überwinden: Molekulare Interaktionen bei Malaria

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• Consejo Superior de Investigaciones Científicas

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• Danmarks Tekniske Universitet

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• DSM Food Specialities BV

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• Ecole Polytechnique federale de Lausanne

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• Eidgenössische Technische Hochschule Zürich

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• Europäisches Laboratorium für Molekularbiologie (Heidelberg)

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• Freie Universität Berlin

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• Fundació Centre de Regulació Genòmica

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• Imperial College of Science, Technology and Medicine

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• Insilico Biotechnology AG

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• Institute of Organic Chemistry and Biochemistry of the CAS

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• Institut National de la Recherche Agronomique

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• Instytut Biochemii i biofizyki

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• Katholieke Universiteit Leuven

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• LIONEX GmbH

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• Ludwig-Maximilians-Universität München

  SFB/TRR 175/1: Subzelluläre Modellierung metabolischer Akklimatisation (TP D03)

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• Max-Delbrück-Centrum für Molekulare Medizin

  GRK 1772/2: Computergestützte Systembiologie

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• Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V.

  Systems Biology of Mycobacterium tuberculosis

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• Max-Planck-Institut für molekulare Genetik

  GRK 1772/2: Computergestützte Systembiologie

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• Medical Research Council

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• Medizinische Universität Wien

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• novozymess

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• Omnia Molecular, S.L.

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• Pompeu Fabra University

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• Process Systems Enterprise Limited

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• Protatuans-Etaireia Ereynas Viotechologias Monoprosopi Etaireia Periorisments Eythinis

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• Rijksuniversiteit Groningen

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• Robert Koch-Institut

  IGRK 2290/1: Grenzen überwinden: Molekulare Interaktionen bei Malaria

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• Ruprecht-Karls-Universität Heidelberg

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• Silicolife LDA

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• Stiftelsen Fraunhofer Chalmers Centrum för Industrimatematik

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• Technische Hochschule Chalmers

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• Technische Universität Berlin

  EXC 2046: Berlin Mathematics Research Center (MATH+)

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• The Chancellor, Masters and Scholars of the University of Oxford

  Eukaryotic Unicellular Organism Biology – Systems Biology of the Control of Cell Growth and Proliferation

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• The University of Manchester

  Eukaryotic Unicellular Organism Biology – Systems Biology of the Control of Cell Growth and Proliferation

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• UCB Celltech

  Engineering of New-Generation Protein Secretion Systems

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• Uniklinik RWTH Aachen - Universitätsklinikum Aachen

  Systematic Models for Biological Systems Engineering Training Network

  university

• Università degli Studi di Milano-Bicocca

  Eukaryotic Unicellular Organism Biology – Systems Biology of the Control of Cell Growth and Proliferation

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• Universitá´del Piemonte Orientale

  Systems Biology of Mycobacterium tuberculosis

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• Universität Cambridge

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• Universität Edinburgh

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• Universität Göteborg

  Eukaryotic Unicellular Organism Biology – Systems Biology of the Control of Cell Growth and Proliferation

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• Universität Greifswald

  EU: Eine neue Generation von mikrobiellen Expressionswirten und -werkzeugen zur Herstellung von Biotherapeutika und hochwertigen Enzymen (SECRETERS)

  university

• Universität Kent

  EU: Eine neue Generation von mikrobiellen Expressionswirten und -werkzeugen zur Herstellung von Biotherapeutika und hochwertigen Enzymen (SECRETERS)

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• Universität Stockholm

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• Vereniging voor Christelijk Hoger Onderwijs Wetenschappelijk Onderzoek en Patientenzorg

  Eukaryotic Unicellular Organism Biology – Systems Biology of the Control of Cell Growth and Proliferation

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• Vrije Universität Amsterdam

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• Wageningen Universität

  Systems Biology of Mycobacterium tuberculosis

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• Weierstraß-Institut für Angewandte Analysis und Stochastik, Berlin

  EXC 2046: Berlin Mathematics Research Center (MATH+)

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• Zuse-Institut Berlin

  EXC 2046: Berlin Mathematics Research Center (MATH+)

  research_institute

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

Telefon

+49 30 2093-98242

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