CellBox: interpretable machine learning for perturbation biology with application to the design of cancer combination therapy
Systematic perturbation of cells followed by comprehensive measurements of molecular and
phenotypic responses provides informative data resources for constructing computational …
phenotypic responses provides informative data resources for constructing computational …
A protocol for dynamic model calibration
Ordinary differential equation models are nowadays widely used for the mechanistic
description of biological processes and their temporal evolution. These models typically …
description of biological processes and their temporal evolution. These models typically …
PEtab—Interoperable specification of parameter estimation problems in systems biology
L Schmiester, Y Schälte, FT Bergmann… - PLoS computational …, 2021 - journals.plos.org
Reproducibility and reusability of the results of data-based modeling studies are essential.
Yet, there has been—so far—no broadly supported format for the specification of parameter …
Yet, there has been—so far—no broadly supported format for the specification of parameter …
AMICI: high-performance sensitivity analysis for large ordinary differential equation models
Ordinary differential equation models facilitate the understanding of cellular signal
transduction and other biological processes. However, for large and comprehensive models …
transduction and other biological processes. However, for large and comprehensive models …
pyPESTO: a modular and scalable tool for parameter estimation for dynamic models
Mechanistic models are important tools to describe and understand biological processes.
However, they typically rely on unknown parameters, the estimation of which can be …
However, they typically rely on unknown parameters, the estimation of which can be …
Complete populations of virtual patients for in silico clinical trials
Motivation Model-based approaches to safety and efficacy assessment of pharmacological
drugs, treatment strategies or medical devices (In Silico Clinical Trial, ISCT) aim to decrease …
drugs, treatment strategies or medical devices (In Silico Clinical Trial, ISCT) aim to decrease …
Artificial neural networks enable genome-scale simulations of intracellular signaling
Mammalian cells adapt their functional state in response to external signals in form of
ligands that bind receptors on the cell-surface. Mechanistically, this involves signal …
ligands that bind receptors on the cell-surface. Mechanistically, this involves signal …
[HTML][HTML] Model reduction of genome-scale metabolic models as a basis for targeted kinetic models
RP van Rosmalen, RW Smith, VAPM Dos Santos… - Metabolic …, 2021 - Elsevier
Constraint-based, genome-scale metabolic models are an essential tool to guide metabolic
engineering. However, they lack the detail and time dimension that kinetic models with …
engineering. However, they lack the detail and time dimension that kinetic models with …
A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization
Macrophages are highly plastic immune cells that dynamically integrate microenvironmental
signals to shape their own functional phenotypes, a process known as polarization. Here we …
signals to shape their own functional phenotypes, a process known as polarization. Here we …
Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models
F Fröhlich, PK Sorger - PLoS computational biology, 2022 - journals.plos.org
Ordinary differential equation (ODE) models are widely used to study biochemical reactions
in cellular networks since they effectively describe the temporal evolution of these networks …
in cellular networks since they effectively describe the temporal evolution of these networks …