A5057231464- EEG and Brain-Computer Interfaces
- Gene Regulatory Network Analysis
- Cytokine Signaling Pathways and Interactions
- Blind Source Separation Techniques
- interferon and immune responses
- Computational Drug Discovery Methods
- Immune Cell Function and Interaction
- Microbial Metabolic Engineering and Bioproduction
- Epilepsy research and treatment
- Bioinformatics and Genomic Networks
- Neural dynamics and brain function
- Gene expression and cancer classification
- Liver physiology and pathology
- Cognitive Computing and Networks
- Markov Chains and Monte Carlo Methods
- Liver Disease Diagnosis and Treatment
- Lysosomal Storage Disorders Research
- IgG4-Related and Inflammatory Diseases
- Statistical and Computational Modeling
- Chaos control and synchronization
- Machine Learning in Bioinformatics
- Control Systems and Identification
- Organometallic Complex Synthesis and Catalysis
- Gaussian Processes and Bayesian Inference
- Protein Kinase Regulation and GTPase Signaling
University of Freiburg
2003-2020
Merrimack Pharmaceuticals (United States)
2019
Center for Systems Biology
2010-2011
Harvard University
2010-2011
Heidelberg University
2009-2010
DKFZ-ZMBH Alliance
2009
German Cancer Research Center
2009
Wellcome Trust
2009
Abstract Motivation: Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order optimally explain experimental data. Often only parts the model can be observed directly. Given a that sufficiently describes measured data, it is important infer how well determined amount and quality This knowledge essential for further investigation predictions. For this reason major topic modeling identifiability analysis....
Abstract Summary: Modeling of dynamical systems using ordinary differential equations is a popular approach in the field biology. Two most critical steps this are to construct models biochemical reaction networks for large datasets and complex experimental conditions perform efficient reliable parameter estimation model fitting. We present modeling environment MATLAB that pioneers these challenges. The numerically expensive parts calculations such as solving associated sensitivity system...
Abstract Motivation: Mathematical modelling of biological systems is becoming a standard approach to investigate complex dynamic, non-linear interaction mechanisms in cellular processes. However, models may comprise non-identifiable parameters which cannot be unambiguously determined. Non-identifiability manifests itself functionally related parameters, are difficult detect. Results: We present the method mean optimal transformations, non-parametric bootstrap-based algorithm for...
Transmembrane channel proteins play pivotal roles in maintaining the homeostasis and responsiveness of cells cross-membrane electrochemical gradient by mediating transport ions molecules through biological membranes. Therefore, computational methods which, given a set 3D coordinates, can automatically identify describe channels transmembrane are key tools to provide insights into how they function. Herein we present PoreWalker, fully automated method, which detects characterises from their...
Mathematical description of biological processes such as gene regulatory networks or signalling pathways by dynamic models utilising ordinary differential equations faces challenges if the model parameters like rate constants are estimated from incomplete and noisy experimental data. Typically, only partially observed. Only a fraction modelled molecular species is measurable directly. This can result in structurally non-identifiable parameters. Furthermore, practical non-identifiability...
In systems biology, one of the major tasks is to tailor model complexity information content data. A useful should describe data and produce well-determined parameter estimates predictions. Too small a will not be able whereas which too large tends overfit measurement errors does provide precise Typically, modified tuned fit data, often results in an oversized model. To restore balance between available measurements, either new has gathered or reduced. this manuscript, we present data-based...
The unpredictability of the occurrence epileptic seizures contributes to burden disease a major degree. Thus, various methods have been proposed predict onset based on EEG recordings. A nonlinear feature motivated by correlation dimension is seemingly promising approach. In previous study this method was reported identify 'preictal drops' up 19 min before seizure onset, exceeding variability interictal data sets 30–50 duration. Here we investigated sensitivity and specificity invasive...
Complex cellular networks regulate regeneration, detoxification and differentiation of hepatocytes. By combining experimental data with mathematical modelling, systems biology holds great promises to elucidate the key regulatory mechanisms involved predict targets for efficient intervention. For generation high-quality quantitative suitable modelling a standardised in vitro system is essential. Therefore authors developed standard operating procedures preparation cultivation primary mouse To...
Quantitative experimental data is the critical bottleneck in modeling of dynamic cellular processes systems biology. Here, we present statistical approaches improving reproducibility protein quantification by immunoprecipitation and immunoblotting.Based on a large set with more than 3600 points, unravel that main sources biological variability noise are multiplicative log-normally distributed. Therefore, suggest log-transformation to obtain additive normally distributed noise. After this...
Article21 July 2020Open Access Transparent process Disentangling molecular mechanisms regulating sensitization of interferon alpha signal transduction Frédérique Kok orcid.org/0000-0002-7855-1422 Division Systems Biology Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany Faculty Biosciences, Heidelberg University, Search for more papers by this author Marcus Rosenblatt Institute Physics, University Freiburg, FDM - Freiburg Data Analysis and Modeling, Melissa...
Objectives: GEN1042 (BNT312; DuoBody®-CD40x4-1BB) is an investigational bispecific antibody (bsAb) that combines targeting and conditional activation of CD40 4-1BB on immune cells. Acasunlimab (BNT311; DuoBody®-PD-L1×4-1BB) bsAb providing with simultaneous complementary PD-L1 blockade. To properly describe changes in clinically measured outcomes resulting from acasunlimab therapy implemented NSCLC QSP model [1], parameters responsible for effect trimeric states the bsAbs (CD40-GEN1042-4-1BB,...
Objectives: Acasunlimab (GEN1046/BNT 311; DuoBody®-PD-L1×4-1BB) is an investigational bispecific antibody immunotherapy that combines conditional 4-1BB activation with simultaneous and complementary PD-L1 blockade. The aims of this study were to develop a Quantitative Systems Pharmacology (QSP) model non-small cell lung cancer (NSCLC) implements mechanisms action acasunlimab for dose optimization. Methods: A QSP NSCLC was developed on the basis HNSCC published in [1]. implemented as...
Type I interferons (IFN) are important components of the innate antiviral response. A key signalling pathway activated by IFNα is Janus kinase/signal transducer and activator transcription (JAK/STAT) pathway. Major have been identified. However, critical kinetic properties that facilitate accelerated initiation intracellular thereby promote virus elimination remain to be determined. By combining mathematical modelling with experimental analysis, we show control dynamic behaviour not...
Activation of the Met receptor tyrosine kinase, either by its ligand, hepatocyte growth factor (HGF), or via ligand-independent mechanisms, such as MET amplification overexpression, has been implicated in driving tumor proliferation, metastasis, and resistance to therapy. Clinical development Met-targeted antibodies challenging, however, bivalent exhibit agonistic properties, whereas monovalent lack potency capacity down-regulate Met. Through computational modeling, we found that a antibody...
Systems biology is an approach to the analysis and prediction of dynamic behaviour biological networks through mathematical modelling based on experimental data. The current lack reliable quantitative data, especially in field signal transduction, means that new methodologies data acquisition processing are needed. Here, we present methods advance established techniques immunoprecipitation immunoblotting more accurate procedures. We propose randomisation sample loading disrupt lane...
Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex cell-context specific signaling networks. Dissecting the underlying relations is crucial predict impact of targeted perturbations. However, a major challenge in identifying networks enormous number potentially possible interactions. Here, we report novel hybrid mathematical modeling strategy systematically unravel hepatocyte growth factor (HGF) stimulated...
The induction of an interferon-mediated response is the first line defense against pathogens such as viruses. Yet, dynamics and extent interferon alpha (IFNα)-induced antiviral genes vary remarkably comprise three expression clusters: early, intermediate late. By mathematical modeling based on time-resolved quantitative data, we identified mRNA stability well a negative regulatory loop key mechanisms endogenously controlling IFNα-induced in hepatocytes. Guided by model, uncovered that this...
Abstract The induction of an interferon-mediated response is the first line defense against pathogens such as viruses. Yet, dynamics and extent interferon alpha (IFNα)-induced antiviral genes vary remarkably comprise three expression clusters: early, intermediate late. By mathematical modeling based on time-resolved quantitative data, we identified mRNA stability well a negative regulatory loop key mechanisms endogenously controlling IFNα-induced in hepatocytes. Guided by model, uncovered...
In this thesis, signalling dynamics of the interferon alpha stimulated JAK/STAT pathway have been studied using a computational modelling approach. A model simulating kinetic response an Huh7.5 cell was developed literature data and experimental measurements. The used for predictions regarding behaviour signal transduction. IRF-9, transcription factor necessary transcriptionally active ISGF-3 complex, predicted to be major contributor time dependent An overexpression IRF-9 enhance accelerate...
Multivariate time series analysis techniques applied to electroencephalography (EEG) recordings enable a detection of interactions between different brain areas. Since several neurons synchronize when seizure is generated, multivariate might yield an increased prediction performance compared univariate ones. Based on intracranial EEG data recorded for 21 patients with 24 hours interictal and 2–5 pre-seizure periods (mean 4.2) each, two synchronization indexes originating from the theory...