StrainDesign: a comprehensive Python package for computational design of metabolic networks

Abstract

Summary Various constraint-based optimization approaches have been developed for the computational analysis and design of metabolic networks. Herein, we present StrainDesign, a comprehensive Python package that builds upon the COBRApy toolbox and integrates the most popular metabolic design algorithms, including nested strain optimization methods such as OptKnock, RobustKnock and OptCouple as well as the more general minimal cut sets approach. The optimization approaches are embedded in individual modules, which can also be combined for setting up more elaborate strain design problems. Advanced features, such as the efficient integration of GPR rules and the possibility to consider gene and reaction additions or regulatory interventions, have been generalized and are available for all modules. The package uses state-of-the-art preprocessing methods, supports multiple solvers and provides a number of enhanced tools for analyzing computed intervention strategies including 2D and 3D plots of user-selected metabolic fluxes or yields. Furthermore, a user-friendly interface for the StrainDesign package has been implemented in the GUI-based metabolic modeling software CNApy. StrainDesign provides thus a unique and rich framework for computational strain design in Python, uniting many algorithmic developments in the field and allowing modular extension in the future. Availability and implementation The StrainDesign package can be retrieved from PyPi, Anaconda and GitHub (https://github.com/klamt-lab/straindesign) and is also part of the latest CNApy package.

gMCSpy: Efficient and accurate computation of Genetic Minimal Cut Sets in Python

“…We have implemented an efficient recursive strategy in Python to reduce the computational expenditure in constructing GPR networks. This function allows us to efficiently deal with complex GPR rules, in contrast to other methods that limit the size of GPRs that they can handle (Schneider et al, 2022).…”

“…In particular, we developed a function in the COBRA toolbox (Heirendt et al, 2019) to carry out this task (Apaolaza et al, 2019), called here GMCS. More recently, StrainDesign was released (Schneider et al, 2022), a Python library that improves previous developments of the same group and extends their framework to an opensource platform.…”

Optimizing VLP production in gene therapy: Opportunities and challenges for in silico modeling

“…[68] To efficiently design strains and predict engineering targets in silico, numerous tools for computational strain design (computational strain design (CSD)) have been developed as reviewed recently, [69] and condensed in workbenches. [70,71] CSD tools have been successfully applied to improve titers in microbial cell factories, such as S.…”

“…To efficiently design strains and predict engineering targets in silico , numerous tools for computational strain design (computational strain design (CSD)) have been developed as reviewed recently, [ 69 ] and condensed in workbenches. [ 70,71 ] CSD tools have been successfully applied to improve titers in microbial cell factories, such as S. roseosporus ; three genes were predicted in the GSMM to have synergistic effects on daptomycin production and led to a titer improvement of 43.2%. [ 72 ] However, CSD tools have not been applied to mammalian cell lines so far.…”

OptEnvelope: a target point guided method for growth-coupled production using knockouts

“…This method is more complicated to use because users have to create manually specific files for each model. The fifth method – strainDesign [9] – is a Python package created to integrate four of the most popular MILP-based strain design approaches – optKnock, robustKnock, optCouple, and MCS [10] - to improve calculation times and reach better results. User input is minimized (e.g., definition of the objective function), and MILP construction is automated.…”