StrainDesign

A COBRApy^[1]-based package for computational design of metabolic networks

The comprehensive StrainDesign package for MILP-based strain design computation with the COBRApy toolbox supports MCS, MCS with nested optimization, OptKnock ^[2], RobustKnock ^[3] and OptCouple ^[4], GPR-rule integration, gene and reaction knockouts and additions as well as regulatory interventions. The automatic lossless network and GPR compression allows strain design computations from genome-scale metabolic networks. Supported solvers are GLPK (available from COBRApy), CPLEX, Gurobi and SCIP ^[5].

Installation instructions ...

Download Jupyter notebook examples ...

The default compression uses a pure Python sparse RREF implementation. A legacy Java-based compression via EFMTool ^[6] is optionally available (see Legacy Methods).

Installation:

The StrainDesign package is available on pip and Anaconda. To install the latest release, run:

pip install straindesign

or

conda install -c cnapy straindesign

Developer Installation:

Download the repository and run

pip install -e .

in the main folder. Through the installation with -e, updates from a ‘git pull’ are at once available in your Python envrionment without the need for a reinstallation.

Examples:

Computation examples are provided in the different chapters of this documentation. The original Jupyer notebook files are located in the StrainDesign package at docs/source/examples.

How to cite:

Schneider P., Bekiaris P. S., von Kamp A., Klamt S. - StrainDesign: a comprehensive Python package for computational design of metabolic networks. Bioinformatics, btac632 (2022)

Contents:

• Solvers
  • 3rd party solver installation
    • CPLEX
    • Gurobi
    • SCIP
  • Solver selection
• Network Analysis
  • Flux optimization (FBA/pFBA)
    • Parsimonious FBA (pFBA)
  • Flux variability analysis (FVA)
  • Yield optimization
    • Mathematical background
• Plotting the flux space
  • Production Envelopes
  • Yield Spaces
  • Mixed Plots
  • 3D plots
    • Interactive and animated 3D plots
• Computational strain design: Growth-coupled production (GCP)
  • pGCP: potentially growth-coupled production
  • wGCP: weakly growth-coupled production
  • dGCP: directionally growth-coupled production
  • SUCP: substrate-uptake-coupled production
• Minimal Cut Sets (MCS)
  • Prerequisites
    • 1) Add and verify production pathway
  • Example 1: Strain designs with a minimum product (1,4-butanediol) yield (SUCP strain design)
  • Example 2: Enforce product (1,4-BDO) synthesis at all growth states (dGCP strain design)
  • Example 3: Suppress flux states that are optimal with respect to a pre-defined objective function (wGCP strain design)
  • Example 4: Protect flux states that are optimal with respect to a pre-defined objective function (pGCP strain design)
  • Example 5: All single gene knockouts that prohibit growth (synthetic lethals).
  • Example 6: Genome-scale strain designs with a minimum product (1,4-butanediol) yield (SUCP strain design)
  • Example 7: Suppress flux states in a toy network
  • Example 8: Suppress and protect flux states in a toy network
  • Theoretical background
• Multi-level strain optimization approaches
  • OptKnock
    • Example 9: OptKnock strain design
    • Example 10: OptKnock strain design with a tilted objective function
    • Example 11: Genome-scale OptKnock strain design
  • RobustKnock
    • Example 12: RobustKnock strain design
  • OptCouple
    • Example 13: OptCouple strain design
  • Combining nested optimization strain design with MCS
    • Example 14: Combining OptKnock with a tilted objective function and the MCS approach
• Standalone network compression
  • Standalone GPR-integraton
    • Gene perturbation studies
• CNApy interface
• StrainDesign API
  • straindesign
    • Submodules
      • straindesign.compression
      • straindesign.compute_strain_designs
      • straindesign.cplex_interface
      • straindesign.efmtool_cmp_interface
      • straindesign.glpk_interface
      • straindesign.gurobi_interface
      • straindesign.indicatorConstraints
      • straindesign.lptools
      • straindesign.names
      • straindesign.networktools
      • straindesign.parse_constr
      • straindesign.pool
      • straindesign.scip_interface
      • straindesign.solver_interface
      • straindesign.strainDesignMILP
      • straindesign.strainDesignModule
      • straindesign.strainDesignProblem
      • straindesign.strainDesignSolutions
    • Package Contents
      • DisableLogger
• Legacy Methods
  • Java-based EFMTool compression (backend='efmtool_rref')
  • JAVA_HOME path
• StrainDesign Developer’s Guide
  • Table of Contents
  • 1. Architecture Overview
  • 2. Repository Structure
  • 3. Public API & Entry Points
    • Primary function
    • SDModule specification
    • Result access
    • Lower-level access
  • 4. Preprocessing Pipeline
    • 4.1 Model Cleaning
    • 4.2 GPR Integration
      • Step 1 — Remove irrelevant genes (remove_irrelevant_genes)
      • Step 2 — Extend model with artificial gene reactions (extend_model_gpr)
      • Step 3 — GPR solution translation (SDSolutions.__init__)
    • 4.3 Network Compression
      • RationalMatrix
      • Compression Algorithm (StoichMatrixCompressor)
      • Compression Result
      • Backend Selection
    • 4.4 Regulatory Interventions
  • 5. MILP Construction
    • 5.1 Overview & z-map Matrices
    • 5.2 Primal LP from COBRApy Model
    • 5.3 SUPPRESS Module — Farkas’ Lemma Dual
    • 5.4 PROTECT Module
    • 5.5 OPTKNOCK — LP Duality for Bilevel Optimization
    • 5.6 ROBUSTKNOCK — Three-Level Duality
    • 5.7 OPTCOUPLE — Growth Coupling Potential
    • 5.8 Linking Binary Variables: link_z
    • 5.9 Big-M Bounding
    • 5.10 Indicator Constraints vs. Big-M
  • 6. Solver Backends
    • 6.1 MILP_LP Unified Interface
    • 6.2 CPLEX
    • 6.3 Gurobi
    • 6.4 SCIP
    • 6.5 GLPK
  • 7. Computation Approaches — SDMILP
    • 7.1 ANY
    • 7.2 BEST
    • 7.3 POPULATE
    • 7.4 Exclusion Constraints
    • 7.5 Solution Verification
  • 8. Post-processing & Result Container
    • 8.1 Solution Decompression
    • 8.2 GPR Translation
    • 8.3 SDSolutions
  • 9. Constraint & Expression Parsing
  • 10. Supporting Utilities
    • lptools.py
    • pool.py — SDPool
    • names.py
    • __init__.py
  • 11. Known Issues, Urgent Actions & Future Work
    • 11.1 Urgent Action Items
    • 11.2 Low-Hanging Fruits
    • 11.3 Future Development Directions
  • 12. Testing
  • Quick Reference: Data Flow Through Key Functions

References:

[1] Ebrahim, A., Lerman, J.A., Palsson, B.O. et al. - COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol 7, 74 (2013)

[2] Burgard, A. P., Pharkya, P., & Maranas, C. D. - Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnology and bioengineering, 84(6), 647–657 (2003)

[3] Tepper N., Shlomi T. - Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways, Bioinformatics. Volume 26, Issue 4, Pages 536–543 (2010)

[4] Jensen K., Broeken V., Lærke Hansen A.S., et al. - OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs. Metabolic Engineering Communications, Volume 8 (2019)

[5] Bestuzheva K., Besançon M., Chen W.K. et al. - The SCIP Optimization Suite 8.0. Available at Optimization Online and as ZIB-Report 21-41, (2021)

[6] Marco Terzer, Jörg Stelling, Large-scale computation of elementary flux modes with bit pattern trees, Bioinformatics, Volume 24, Issue 19, (2008), Pages 2229–2235,

Indices and tables

• Index

• Module Index

• Search Page