Introduction
Metabolic Engineering Workbench in python
MEWpy is a Computational Strain Optimization (CSO) tool able to aggregate different types of constraint-based models and simulation approaches. It relies on Evolutionary Algorithms (EAs) to identify the set of genetic modifications that favor and optimize a desired metabolic engineering goal. One of the main advantages of using EAs is that they enable the simultaneous optimizations of more than one objectives (product rate, growth rate, number of modifications, etc.), sometimes conflicting, and deliver in a single run a set of trade-off solutions between the objectives.
MEWPy currently supports REFRAMED and COBRApy phenotype simulators integrating both in a common API which enables different methods:
Flux Balance Analysis (FBA)
Parsimonious Flux Balance Analysis (pFBA)
Regulatory On/Off Minimization of metabolic flux (ROOM)
Minimization of Metabolic Adjustment (MOMA)
linear version of the Minimization of Metabolic Adjustment (lMOMA)
Flux Variability Analysis (FVA).
MEWpy also includes implementations of Regulatory FBA (RFBA), Steady-state Regulatory FBA (SRFBA), Probabilistic Regulation of Metabolism (PROM), and CoRegFlux phenotype simulation methods. The optimization engine relies on either Inspyred or jMetalPy packages. MEWPy requires a compatible solver for linear programming problems, with installed Python dependencies installed, from the following list: