Prediction, Analysis and Simulation of Metabolic Reaction Networks
PASMet is a web-based platform for predicting, modelling and analysing metabolic systems. It is a non-commercial and user-friendly tool for assisting non-experts in mathematical modelling, computing or programming to work on computational biology.
See available toolsThis function processes time-series data of metabolite concentrations and utilises them to predict a probable metabolic pathway using Granger causality test.
This function uses time-series of metabolic concentrations to predict a probable metabolic pathway with regulations using BST-loglem method which combines statistical techniques and mathematical modelling.
This function utilises time-series data of metabolite concentrations to construct a simple mathematical model. The model parameters are estimated using Levenberg-Marquardt algorithm coupled with ODE solver.
This function offeres the selection of methods to solve ordinary differential equations to observe dynamic behaviours of metabolite conccentrations using a mathematical model.
This function analyses metabolic systems using a mathematical model. It provides sensitivity analysis, logarithmic gains, and bottleneck ranking indicator to compare perturbation effects and check model stability.
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PASMet Development Team
Metabolic Systems Research Team
RIKEN Center for Sustainable Resource Science
1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045 Japan