Jörg Stelling

The paper introduces minimal metabolic pathways to enable scalable analysis of metabolic subnetworks in organisms and communities.

ENKIE predicts KM and kcat enzyme kinetic parameters and their uncertainties using Bayesian Multilevel Models, achieving performance comparable to deep learning methods.

The paper constructs synthetic genotype networks of gene regulatory networks in E. coli to study robustness and evolvability in genotype-phenotype maps.

The paper introduces a method for precise single-cell tracking and lineage reconstruction using cell region fingerprints, improving tracking performance across species and data types.

The paper identifies glutamine and secondary oxidative propionyl-CoA oxidation pathways as important in MMUT-type methylmalonic aciduria through genetic and environmental interventions in cellular and computational models.

The paper introduces a probabilistic framework for analyzing metabolic networks by integrating thermodynamics and flux constraints, revealing new insights into E. coli metabolism.

This paper introduces minimal pathways for scalable metabolic pathway analysis in genome-scale models, improving gene importance predictions and metabolite exchange analysis in microbial and mammalian systems.

This paper designs and demonstrates CRISPRi-based synthetic gene circuits in E. coli, including an oscillator, bistable switch, and stripe pattern-forming IFFL, showing the potential of CRISPRi for dynamic and multistable circuit construction.

The paper develops methods to efficiently manipulate and generate Kirchhoff polynomials for analyzing non-equilibrium biochemical reaction networks, reducing symbolic complexity through graph-based factorization and simplification.

The paper integrates metabolomics and genomics data to identify functional SNPs in Mycobacterium tuberculosis strains, revealing genetic basis for antibiotic sensitivity.

The paper introduces a two-stage method to estimate dynamic nonlinear mixed effects models from single-cell time-lapse data, demonstrating accuracy and scalability with yeast endocytosis data.

The paper predicts ligand-dependent tumor growth in cell lines and patient samples using a mechanistic signaling model and Bagged Decision Trees, finding a correlation between growth factor expression and predicted response.