A5001044691- Gene Regulatory Network Analysis
- Microbial Metabolic Engineering and Bioproduction
- Protein Structure and Dynamics
- Metabolomics and Mass Spectrometry Studies
- Bioinformatics and Genomic Networks
- Elasticity and Wave Propagation
- Dynamics and Control of Mechanical Systems
- Mechanical Systems and Engineering
- Probabilistic and Robust Engineering Design
- Advanced Control Systems Optimization
- Numerical methods for differential equations
- Model Reduction and Neural Networks
- Control and Stability of Dynamical Systems
- Gaussian Processes and Bayesian Inference
- Matrix Theory and Algorithms
Technical University of Munich
2021-2024
Helmholtz Zentrum München
2021-2024
St Petersburg University
2013-2014
Reproducibility and reusability of the results data-based modeling studies are essential. Yet, there has been—so far—no broadly supported format for specification parameter estimation problems in systems biology. Here, we introduce PEtab, a which facilitates using Systems Biology Markup Language (SBML) models set tab-separated value files describing observation model experimental data as well parameters to be estimated. We already implemented PEtab support into eight well-established...
Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large complex systems. pyPESTO is a modular framework systematic parameter estimation, with scalable algorithms optimization uncertainty quantification. While tailored ordinary differential equation problems, broadly applicable black-box problems. Besides own implementations, it provides unified interface...
Dynamical models in the form of systems ordinary differential equations have become a standard tool biology. Many parameters such are usually unknown and to be inferred from experimental data. Gradient-based optimization has proven effective for parameter estimation. However, computing gradients becomes increasingly costly larger models, which required capturing complex interactions multiple biochemical pathways. Adjoint sensitivity analysis been pivotal working with large but methods...
Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires large number model simulations objective function gradient computations, if gradient-based optimization used. The depends on derivatives the state variables with respect to parameters, also called sensitivities, which are expensive compute. In many cases, steady-state computation part simulation, either due or an assumption that system at steady initial time...
As metabolomics datasets are becoming larger and more complex, there is an increasing need for model-based data integration analysis to optimally leverage these data. Dynamical models of metabolism allow the heterogeneous dynamical phenotypes. Here, we review recent efforts in using metabolic integration, focusing on approaches that not restricted steady-state measurements or require flux distributions as inputs. Furthermore, discuss advances current challenges. We conclude much progress has...
The stability of the trivial equilibrium position a nonlinear mechanical system with switched dissipative and potential forces is studied. It assumed that are linear, while homogeneous. By use multiple Lyapunov functions approach dwell time approach, conditions on law guaranteeing asymptotic practical obtained. An example presented to demonstrate effectiveness proposed approaches.
Estimating parameters of dynamic models from experimental data is a challenging, and often computationally-demanding task. It requires large number model simulations objective function gradient computations, if gradient-based optimization used. In many cases, steady-state computation part simulation, either due to or an assumption that the system at steady state initial time point. Various methods are available for computation. Yet, most efficient pair (one states, one gradients) particular...
Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large complex systems. We present pyPESTO, a modular framework systematic parameter estimation, with scalable algorithms optimization uncertainty quantification. While tailored ordinary differential equation problems, pyPESTO is broadly applicable black-box problems. Besides own implementations, it provides...
Certain classes of nonlinear mechanical systems with switched force fields are studied. By the use Lyapunov direct method, sufficient conditions asymptotic stability equilibrium positions for considered obtained. The results a computer simulation in MATLAB presented to illustrate proposed approaches.
Abstract Dynamical models in the form of systems ordinary differential equations have become a standard tool biology. Many parameters such are usually unknown and to be inferred from experimental data. Gradient-based optimization has proven effective for parameter estimation. However, computing gradients becomes increasingly costly larger models, which required capturing complex interactions multiple biochemical pathways. Adjoint sensitivity analysis been pivotal working with large but...