Fabian Fröhlich

GEMOT: Towards Mechanistic World Models for Biology

This paper introduces GEMOT, a framework for mechanistic world modeling in biology, which autonomously constructs models that match or exceed reference models in fit and parsimony across diverse biological problems.

Symbolic regression enables coarse-grained model discovery of intracellular signalling dynamics

This paper uses symbolic regression to discover compact models of intracellular signaling dynamics, showing it can recover known kinetics and identify interpretable rate laws in cancer-relevant contexts.

Deep Mechanistic Models reveal pathway-extrinsic drivers of mammary MAPK signalling heterogeneity

This paper introduces deep mechanistic models to analyze mammary MAPK signaling heterogeneity, showing that most variation comes from pathway-extrinsic factors rather than core pathway components.

Nonlinear mixed-effect models and tailored parametrization schemes enables integration of single cell and bulk data

This paper presents a framework for integrating single-cell and bulk data using nonlinear mixed-effect models and tailored parameter estimation, improving parameter identifiability and prediction accuracy.

Dynamic Modeling of Cell Cycle Arrest Through Integrated Single-Cell and Mathematical Modelling Approaches

The paper develops a framework to integrate multiplexed immunofluorescence data with mathematical models to study cell cycle dynamics and arrest, validated on MCF-10A cells exposed to arresting agents.

PEtab.jl: Advancing the Efficiency and Utility of Dynamic Modelling

PEtab.jl improves dynamic modelling efficiency by comparing ODE solvers and parameter estimation methods, showing faster performance than pyPESTO for medium-sized models.

Multimodal profiling unveils a reversible breast basal-like cell state in AKT-inhibitor resistant tumours

This study identifies a reversible basal-like cell state in AKT-inhibitor resistant triple negative breast cancer using multimodal profiling and lineage tracing.

Imaging of MAP kinase dynamics reveals endocytic regulation of pulsatile signalling and network re-wiring in response to targeted therapy in EGFR-mutant non-small cell lung cancer

The paper uses single-cell imaging to show that drug-tolerant cells in EGFR-mutant lung cancer rewired ERK signaling through endocytic pathways and adhesion complexes, driven by YAP and PTPRS.

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

The paper models MAPK signaling in BRAFV600E melanoma cells to show how allostery and rewiring lead to drug resistance.

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

The paper introduces a new adjoint method for computing gradients in ODE models of biochemical reaction networks using steady-state data, achieving up to 4.4x speedup in simulation time for large-scale models.

Multi range ERK responses shape the proliferative trajectory of single cells following oncogene induced senescence

The paper investigates how varying ERK activity levels influence proliferation in cells undergoing oncogene-induced senescence, finding intermediate ERK activity promotes maximal proliferation.

Fides: Reliable Trust-Region Optimization for Parameter Estimation of Ordinary Differential Equation Models

The paper introduces fides, a Python framework for ODE model calibration using trust-region optimization, showing improved reliability and efficiency over existing methods.

Combination Treatment Optimization Using aPan-Cancer Pathway Model

This paper uses a pan-cancer pathway model to identify novel combination therapies and optimize treatment sequences for cancer cell lines.

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

The paper introduces a hierarchical method for efficiently parameterizing large-scale dynamic models using relative measurements, improving optimizer performance and enabling unbiased weighting of heterogeneous data.

Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection

This paper integrates single-cell perturbation experiments using nonlinear mixed effect modeling to robustly reconstruct cell-to-cell variability and improve parameter estimates.

Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

The paper presents a computational framework to parameterize large-scale mechanistic models for predicting drug response in cancer cell lines using exome and transcriptome data, achieving higher accuracy than statistical approaches.

Benchmarking optimization methods for parameter estimation in large kinetic models

The paper benchmarks optimization methods for parameter estimation in large kinetic models and finds that a hybrid metaheuristic outperforms multi-start local searches.

Mechanistic hierarchical population model identifies latent causes of cell-to-cell variability

This study introduces a hierarchical population model to identify latent causes of cell-to-cell variability in heterogeneous populations using single-cell data.

Optimization and uncertainty analysis of ODE models using second order adjoint sensitivity analysis

The paper introduces second order adjoint sensitivity analysis and a hybrid optimization-integration method to improve computational efficiency and robustness of parameter estimation for ODE models.

Scalable parameter estimation for genome-scale biochemical reaction networks

This paper introduces a scalable method for parameter estimation in genome-scale biochemical reaction networks using adjoint sensitivity analysis, showing improved efficiency and scalability compared to existing methods.