Efficient parameterization of large-scale dynamic models based on relative measurements
Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative
understanding of biological processes and the integration of heterogeneous datasets …
understanding of biological processes and the integration of heterogeneous datasets …
Scalable parameter estimation for genome-scale biochemical reaction networks
Mechanistic mathematical modeling of biochemical reaction networks using ordinary
differential equation (ODE) models has improved our understanding of small-and medium …
differential equation (ODE) models has improved our understanding of small-and medium …
Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems
Background Kinetic models of biochemical systems usually consist of ordinary differential
equations that have many unknown parameters. Some of these parameters are often …
equations that have many unknown parameters. Some of these parameters are often …
Efficient gradient-based parameter estimation for dynamic models using qualitative data
Motivation Unknown parameters of dynamical models are commonly estimated from
experimental data. However, while various efficient optimization and uncertainty analysis …
experimental data. However, while various efficient optimization and uncertainty analysis …
Inference for stochastic chemical kinetics using moment equations and system size expansion
Quantitative mechanistic models are valuable tools for disentangling biochemical pathways
and for achieving a comprehensive understanding of biological systems. However, to be …
and for achieving a comprehensive understanding of biological systems. However, to be …
Scalable inference of ordinary differential equation models of biochemical processes
Ordinary differential equation models have become a standard tool for the mechanistic
description of biochemical processes. If parameters are inferred from experimental data …
description of biochemical processes. If parameters are inferred from experimental data …
Symbolic Kinetic Models in Python (SKiMpy): Intuitive modeling of large-scale biological kinetic models
Motivation Large-scale kinetic models are an invaluable tool to understand the dynamic and
adaptive responses of biological systems. The development and application of these models …
adaptive responses of biological systems. The development and application of these models …
Calibration methods to fit parameters within complex biological models
P Nanda, DE Kirschner - Frontiers in applied mathematics and …, 2023 - frontiersin.org
Mathematical and computational models of biological systems are increasingly complex,
typically comprised of hybrid multi-scale methods such as ordinary differential equations …
typically comprised of hybrid multi-scale methods such as ordinary differential equations …
Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks
Background Mathematical modeling and analysis have become, for the study of biological
and cellular processes, an important complement to experimental research. However, the …
and cellular processes, an important complement to experimental research. However, the …
Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks
P Lakrisenko, P Stapor, S Grein… - PLOS Computational …, 2023 - journals.plos.org
Dynamical models in the form of systems of ordinary differential equations have become a
standard tool in systems biology. Many parameters of such models are usually unknown and …
standard tool in systems biology. Many parameters of such models are usually unknown and …