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Computational analysis of Pseudomonas Putida metabolism Using Large-scale Kinetic Models

Design of novel enzyme-catalyzed reactions linked to protein sequences for finding enzyme engineering targets

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

Identifying patterns in kinetic parameters that determine the impact of rate-limiting enzymes

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

Metabolic fluxes and chemical signaling during coral disease at the single cell-level.

Metabolic fluxes and chemical signaling during coral disease at the single cell-level.

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

A Study of Dynamic Responses of E. coli Metabolism Upon Large Perturbations Using Large-Scale Kinetic Models

Design of novel enzyme-catalyzed reactions linked to protein sequences for finding enzyme engineering targets

Computational analysis of Pseudomonas Putida metabolism Using Large-scale Kinetic Models

Identifying patterns in kinetic parameters that determine the impact of rate-limiting enzymes

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

Metabolic fluxes and chemical signaling during coral disease at the single cell-level.

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

Metabolic fluxes and chemical signaling during coral disease at the single cell-level.

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

A Study of Dynamic Responses of E. coli Metabolism Upon Large Perturbations Using Large-Scale Kinetic Models

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