Sustainable Metabolic Engineering in Organisms: AI Stoichiometric Models
Explore the cutting-edge methods in sustainable metabolic engineering using AI stoichiometric models, including optKnock, robustKnock, and optCouple. Learn about growth-coupling relationships, strain design, and novel active reactions design. Evolutionary algorithms and game theory play a key role in the ModCell2 method. Understand the use of mixed-integer linear programming (MILP) in optimizing metabolic pathways for target metabolite production.
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Presentation Transcript
Artificial intelligence in stoichiometric model-based sustainable metabolic engineering of organism strains Kristaps B rzi PhD student
Exploration of methods - optKnock First method (created in 2003) Bilevel programming framework First level maximize yield of product Second level Optimizes growth Solved using mixed integer linear programming (MILP) Weak growth-coupling
Growth-coupling and Envelopes When organism grows, it produces target metabolite Growth-coupling relationship can be represented by projecting flux space onto the product and biomass yield axis
Exploration of methods - robustKnock Created in 2010 Improves on optKnock Guarantees that target metabolite is produced Uses max-min optimization Solved using mixed integer linear programming (MILP)
Exploration of methods - optCouple Created in 2019 Python Another improvement on optKnock Possible knock-outs, knock-ins and modification to growth medium Postprocessing to identify modifications that contribute (improves calculation times)
Exploration of methods ModCell2 Created in 2019 Evolutionary game theory and evolutionary algorithms Can calculate multiple target metabolites at the same time
Exploration of methods strainDesign Created in 2022 Python GUI Combines four previous methods User input is minimized MILP creation is automated Can include desired and undesired regions
Designing novel method active reactions Constant zero flux Variability including zero Variability excluding zero Constant non-zero flux
Understanding MILP matrix Stoichiometric matrix S Reactions n Metabolites m Constraints C Objective B
Artificial Inteligence Genetic Algorithms ModCell2 optGene kStrain Neural network
