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Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

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Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Graph Chatbot

Chattez avec Graph Search

Systematic reduction of genome-scale models for the study of metabolic phenotypes of human cells

Portable bioluminescence systems and methods for in vivo monitoring of molecular and metabolic events in animals

Generation and Analysis of Large-Scale Dynamic Non-Linear Models of Metabolism

Identification of the enzymes that determine the robustness of lipid profiles in yeast

Deciphering ambiguous control over fluxes through characterization and reduction of uncertainty

Integration of Metabolic, Regulatory and Signaling Networks Towards Analysis of Perturbation and Dynamic Responses

Unlocking the full potential of kinetic metabolic models through characterization and reduction of uncertainty

“K-90”, conciliation entre industrie chimique bâloise et paysage rhénan

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Unlocking the full potential of kinetic metabolic models through characterization and reduction of uncertainty

Systematic reduction of genome-scale models for the study of metabolic phenotypes of human cells

Generation and Analysis of Large-Scale Dynamic Non-Linear Models of Metabolism

Identification of the enzymes that determine the robustness of lipid profiles in yeast

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

“K-90”, conciliation entre industrie chimique bâloise et paysage rhénan

Portable bioluminescence systems and methods for in vivo monitoring of molecular and metabolic events in animals

Deciphering ambiguous control over fluxes through characterization and reduction of uncertainty

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Integration of Metabolic, Regulatory and Signaling Networks Towards Analysis of Perturbation and Dynamic Responses