Jens Timmer

About Jens Timmer:
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SEEK ID: https://seek.lisym.org/people/118

Location:  Germany

Expertise: Not specified

Tools: Not specified

ORCID: https://orcid.org/0000-0003-4517-1383

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LiSyM-Krebs

LiSyM-Krebs ist ein nationales Forschungsnetz zur Früherkennung und Prävention von Leberkrebs, das unter Verwendung des systemmedizinischen Forschungsansatzes die komplexen, dynamischen Prozesse der Krankheitsprogression analysiert, um ausgehend von den Erkenntnissen aus dem Forschungsnetz LiSyM die Entstehung von Leberkrebs besser zu verstehen, vorherzusagen und im besten Fall sogar zu verhindern.

Projects: LiSyM-Krebs Partnering, LiSyM-Krebs

Web page: Not specified

LiSyM: Liver Systems Medicine

Liver Systems Medicine : striving to develop non-invasive methods for diagnosing and treating NAFLD by combining mathematical modeling and biological research. LiSyM, is a multidisciplinary research network, in which molecular and cell biologists, clinical researchers, pharmacologists and experts in mathematical modeling examine the liver in its entirety. LiSyM research focuses on the metabolic liver disease non-alcoholic fatty liver disease (NAFLD), which includes non-alcoholic steatohepatitis ...

Projects: LiSyM Core Infrastructure and Management (LiSyM-PD), LiSyM Pillar I: Early Metabolic Injury (LiSyM-EMI), LiSyM Pillar II: Chronic Liver Disease Progression (LiSyM-DP), LiSyM Pillar III: Regeneration and Repair in Acute-on-Chronic Liver Failure (LiSyM-ACLF), LiSyM Pillar IV: Liver Function Diagnostics (LiSyM-LiFuDi), Model Guided Pharmacotherapy In Chronic Liver Disease (LiSyM-MGP), Molecular Steatosis - Imaging & Modeling (LiSyM-MSIM), The Hedgehog Signalling Pathway (LiSyM-JGMMS), Multi-Scale Models for Personalized Liver Function Tests (LiSyM-MM-PLF), LiSyM PALs, Project Management PTJ, LiSyM network, LiSyM Scientific Leadership Team (LiSyM-LT)

Web page: https://www.lisym.org/

LiSyM Pillar II: Chronic Liver Disease Progression (LiSyM-DP)

In one in five people with NAFLD, the functioning liver cells, the hepatocytes, are replaced by connective tissue. Eventually this fibrosis becomes irreversible. In this state the liver is like a ‘scar that never heals’. Through it, the liver loses many of its vital functions. LiSyM-Pillar II wants to know more about which factors promote fibrosis and the conditions under which fibrosis becomes irreversible How can fibrosis be diagnosed as early as possible? Researchers in the pillar are also ...

Programme: LiSyM: Liver Systems Medicine

Public web page: http://www.lisym.org/our-work/pillar-research

Start date: 1st Jan 2016

LiSyM-Krebs

LiSyM-Krebs

Programme: LiSyM-Krebs

Public web page: Not specified

LiSyM network

This comprises the whole LiSyM network

Programme: LiSyM: Liver Systems Medicine

Public web page: http://www.lisym.org

Start date: 1st Jan 2016

LiSyM-Krebs Partnering

This generic project is intended to be a forum for all LiSyM partner and external stakeholders interested in participating in the BMBF initiative LiSyM-Krebs.

Programme: LiSyM-Krebs

Public web page: https://www.ptj.de/projektfoerderung/gesundheitsforschung/lisym-krebs

LiSyM Pillar III: Regeneration and Repair in Acute-on-Chronic Liver Failure (LiSyM-ACLF)

In chronic diseases, at some point the liver can suddenly stop functioning. This is called acute-on-chronic liver failure, or ACLF. This is the leading cause of death in liver patients and is often provoked by the use of transcription or freely available drugs or alcohol abuse. For this condition we need an effective treatment quickly. LiSyM-Pillar III researches the factors that contribute significantly to ACLF. What exactly happens then? Are there any early signs that would enable ACLF to be ...

Programme: LiSyM: Liver Systems Medicine

Public web page: http://www.lisym.org/our-work/pillar-research

Start date: 1st Jan 2016

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PEtab—Interoperable specification of parameter estimation problems in systems biology
LiSyM network

Abstract

Not specified

Authors: Leonard Schmiester, Yannik Schälte, Frank T. Bergmann, Tacio Camba, Erika Dudkin, Janine Egert, Fabian Fröhlich, Lara Fuhrmann, Adrian L. Hauber, Svenja Kemmer, Polina Lakrisenko, Carolin Loos, Simon Merkt, Wolfgang Müller, Dilan Pathirana, Elba Raimúndez, Lukas Refisch, Marcus Rosenblatt, Paul L. Stapor, Philipp Städter, Dantong Wang, Franz-Georg Wieland, Julio R. Banga, Jens Timmer, Alejandro F. Villaverde, Sven Sahle, Clemens Kreutz, Jan Hasenauer, Daniel Weindl

Date Published: 26th Jan 2021

Publication Type: Journal

Disentangling molecular mechanisms regulating sensitization of interferon alpha signal transduction.
LiSyM Pillar II: Chronic Liver Disease Progression (LiSyM-DP), LiSyM Pillar III: Regeneration and Repair in Acute-on-Chronic Liver Failure (LiSyM-ACLF)

Abstract (Expand)

Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFNalpha) orchestrates antiviral … responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFNalpha doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation model for IFNalpha signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model-based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFNalpha dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFNalpha leads to a dose-dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient-specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient-specific pathway desensitization, while the abundance of STAT2 predicts the patient-specific IFNalpha signal response.

Authors: F. Kok, M. Rosenblatt, M. Teusel, T. Nizharadze, V. Goncalves Magalhaes, C. Dachert, T. Maiwald, A. Vlasov, M. Wasch, S. Tyufekchieva, K. Hoffmann, G. Damm, D. Seehofer, T. Boettler, M. Binder, J. Timmer, M. Schilling, U. Klingmuller

Date Published: 23rd Jul 2020

Publication Type: Journal

Local Riemannian geometry of model manifolds and its implications for practical parameter identifiability
LiSyM Pillar II: Chronic Liver Disease Progression (LiSyM-DP)

Abstract (Expand)

When non-linear models are fitted to experimental data, parameter estimates can be poorly constrained albeit being identifiable in principle. This means that along certain paths in parameter space, the … log-likelihood does not exceed a given statistical threshold but remains bounded. This situation, denoted as practical non-identifiability, can be detected by Monte Carlo sampling or by systematic scanning using the profile likelihood method. In contrast, any method based on a Taylor expansion of the log-likelihood around the optimum, e.g., parameter uncertainty estimation by the Fisher Information Matrix, reveals no information about the boundedness at all. In this work, we present a geometric approach, approximating the original log-likelihood by geodesic coordinates of the model manifold. The Christoffel Symbols in the geodesic equation are fixed to those obtained from second order model sensitivities at the optimum. Based on three exemplary non-linear models we show that the information about the log-likelihood bounds and flat parameter directions can already be contained in this local information. Whereas the unbounded case represented by the Fisher Information Matrix is embedded in the geometric framework as vanishing Christoffel Symbols, non-vanishing constant Christoffel Symbols prove to define prototype non-linear models featuring boundedness and flat parameter directions of the log-likelihood. Finally, we investigate if those models could allow to approximate and replace computationally expensive objective functions originating from non-linear models by a surrogate objective function in parameter estimation problems.

Authors: Daniel Lill, Jens Timmer, Daniel Kaschek

Date Published: 3rd Jun 2019

Publication Type: Not specified

Mapping connections in signaling networks with ambiguous modularity.
LiSyM Pillar II: Chronic Liver Disease Progression (LiSyM-DP)

Abstract (Expand)

Modular Response Analysis (MRA) is a suite of methods that under certain assumptions permits the precise reconstruction of both the directions and strengths of connections between network modules from … network responses to perturbations. Standard MRA assumes that modules are insulated, thereby neglecting the existence of inter-modular protein complexes. Such complexes sequester proteins from different modules and propagate perturbations to the protein abundance of a downstream module retroactively to an upstream module. MRA-based network reconstruction detects retroactive, sequestration-induced connections when an enzyme from one module is substantially sequestered by its substrate that belongs to a different module. Moreover, inferred networks may surprisingly depend on the choice of protein abundances that are experimentally perturbed, and also some inferred connections might be false. Here, we extend MRA by introducing a combined computational and experimental approach, which allows for a computational restoration of modular insulation, unmistakable network reconstruction and discrimination between solely regulatory and sequestration-induced connections for a range of signaling pathways. Although not universal, our approach extends MRA methods to signaling networks with retroactive interactions between modules arising from enzyme sequestration effects.

Authors: D. Lill, O. S. Rukhlenko, A. J. Mc Elwee, E. Kashdan, J. Timmer, B. N. Kholodenko

Date Published: 1st Jun 2019

Publication Type: Not specified

Model-based identification of TNFalpha-induced IKKbeta-mediated and IkappaBalpha-mediated regulation of NFkappaB signal transduction as a tool to quantify the impact of drug-induced liver injury compounds.
LiSyM Pillar III: Regeneration and Repair in Acute-on-Chronic Liver Failure (LiSyM-ACLF)

Abstract (Expand)

Drug-induced liver injury (DILI) has become a major problem for patients and for clinicians, academics and the pharmaceutical industry. To date, existing hepatotoxicity test systems are only poorly … predictive and the underlying mechanisms are still unclear. One of the factors known to amplify hepatotoxicity is the tumor necrosis factor alpha (TNFalpha), especially due to its synergy with commonly used drugs such as diclofenac. However, the exact mechanism of how diclofenac in combination with TNFalpha induces liver injury remains elusive. Here, we combined time-resolved immunoblotting and live-cell imaging data of HepG2 cells and primary human hepatocytes (PHH) with dynamic pathway modeling using ordinary differential equations (ODEs) to describe the complex structure of TNFalpha-induced NFkappaB signal transduction and integrated the perturbations of the pathway caused by diclofenac. The resulting mathematical model was used to systematically identify parameters affected by diclofenac. These analyses showed that more than one regulatory module of TNFalpha-induced NFkappaB signal transduction is affected by diclofenac, suggesting that hepatotoxicity is the integrated consequence of multiple changes in hepatocytes and that multiple factors define toxicity thresholds. Applying our mathematical modeling approach to other DILI-causing compounds representing different putative DILI mechanism classes enabled us to quantify their impact on pathway activation, highlighting the potential of the dynamic pathway model as a quantitative tool for the analysis of DILI compounds.

Authors: A. Oppelt, D. Kaschek, S. Huppelschoten, R. Sison-Young, F. Zhang, M. Buck-Wiese, F. Herrmann, S. Malkusch, C. L. Kruger, M. Meub, B. Merkt, L. Zimmermann, A. Schofield, R. P. Jones, H. Malik, M. Schilling, M. Heilemann, B. van de Water, C. E. Goldring, B. K. Park, J. Timmer, U. Klingmuller

Date Published: 15th Jun 2018

Publication Type: Not specified

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