Welcome to marsi!

marsi is an open-source software to created to identify non-GMO strain design targets.

There are two main experimental scenarios:

1. Adaptive Laboratory Evolution (ALE)
2. Classic Strain Improvement (CSI)

CSI

In this scenario we assume that metabolizing the target compound is going to kill the cells. Using chemical mutagenesis, the surviving cells have found a way around the metabolism and are capable of resuming their activity without the reactions related with that metabolite.

Here the search can be performed using existing methods (such as OptGene[1] or OptKnock[2]) that can predict knockout targets. The targets will be then replaced and tested for the presence of an analog. We also implemented OptMet, a new method that uses Heuristic Optimization to search for metabolite targets directly.

ALE

The ALE scenario assumes a long term exposition and adaptation of the cells to an analog metabolite. Here we account for essential and non-essential metabolites. For essential metabolites the cells will produce more of the target metabolite so it can compete with the analog for the enzymes. For non-essential metabolites we assume reduced activity/specificity towards the target metabolite and the activity will be inhibited. To identify which pathways should be inhibited we use DifferentialFVA[3].

User’s guide

• Installation

• Examples
  • 1.1 Sensitivity Analysis
    • Sensitivity analysis for L-Serine
    • Sensitivity analysis for Pyruvate
  • 1.2 Metabolite Knockout Landscape
    • Metabolite Knockout Landscape
  • 1.3 Running OptMet
  • 1.4 Converting Gene or Reaction Knockouts into Metabolite Knockouts
    • Design a D-lactate producing strain
  • 2.1 Find metabolites in the database
    • Metabolites are indexed using InChI key.
    • The query can also be done using identifiers from databases
  • 2.2 Find analogs for target
  • 2.3 Targets for metabolites in the model
    • Load a model from the BiGG database
    • Annotate the model with metabolite information

Command Line Interface (CLI)

• CLI Docs

Application Programming Interface (API)

• API Docs
  • marsi package

References

[1] Patil,K.R. et al. (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics, 6, 308.

[2] Burgard,A.P. et al. (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng., 84, 647–657.

[3] Cardoso,J.G.R. et al. (2017) Cameo : A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories. bioRxiv.

Indices and tables

• Index
• Module Index
• Search Page