Fabian Fröhlich

Principal Investigator
Google Scholar

Fabian is a group leader at the Francis Crick Institute.

Before joining the Crick, Fabian was a Human Frontier Science Program Fellow in the Laboratory of Systems Pharmacology at Harvard Medical School, working under the guidance of Peter Sorger. He received his PhD from Technische Universität München, working with Jan Hasenauer and Fabian Theis at the Institute of Computational Biology at Helmholtz Munich.


pyPESTO: A modular and scalable tool for parameter estimation for dynamic models

Multi-range ERK responses shape the proliferative trajectory of single cells following oncogene induction

Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance

Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks

Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models

BioSimulators: a central registry of simulation engines and services for recommending specific tools

Combination treatment optimization using a pan-cancer pathway model

A protocol for dynamic model calibration

AMICI: high-performance sensitivity analysis for large ordinary differential equation models

PEtab—Interoperable specification of parameter estimation problems in systems biology

Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells

Efficient parameterization of large-scale dynamic models based on relative measurements

Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model

Multi-experiment nonlinear mixed effect modeling of single-cell translation kinetics after transfection

Benchmarking optimization methods for parameter estimation in large kinetic models

Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis

A Hierarchical, Data-Driven Approach to Modeling Single-Cell Populations Predicts Latent Causes of Cell-To-Cell Variability

GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models

PESTO: Parameter EStimation TOolbox

Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

Parameter estimation for dynamical systems with discrete events and logical operations

Inference for Stochastic Chemical Kinetics Using Moment Equations and System Size Expansion

CERENA: ChEmical REaction Network Analyzer - A Toolbox for the Simulation and Analysis of Stochastic Chemical Kinetics