Multiobjective strain design: A framework for modular cell engineering

Abstract

Diversity of cellular metabolism can be harnessed to produce a large space of molecules. However, development of optimal strains with high product titers, rates, and yields required for industrial production is laborious and expensive. To accelerate the strain engineering process, we have recently introduced a modular cell design concept that enables rapid generation of optimal production strains by systematically assembling a modular cell with an exchangeable production module(s) to produce target molecules efficiently. In this study, we formulated the modular cell design concept as a general multiobjective optimization problem with flexible design objectives derived from mass balance. We developed algorithms and an associated software package, named ModCell2, to implement the design. We demonstrated that ModCell2 can systematically identify genetic modifications to design modular cells that can couple with a variety of production modules and exhibit a minimal tradeoff among modularity, performance, and robustness. Analysis of the modular cell designs revealed both intuitive and complex metabolic architectures enabling modular production of these molecules. We envision ModCell2 provides a powerful tool to guide modular cell engineering and sheds light on modular design principles of biological systems.

Comparison of Multi-objective Evolutionary Algorithms to Solve the Modular Cell Design Problem for Novel Biocatalysis

“…Modular cell design enables rapid assembly of strains with desirable phenotypes from a modular (chassis) cell, [1] resembling the efficiency advantages of modular design in conventional engineering disciplines. [7,10] A modular cell is constructed by eliminating genes from a parent strain to maintain only core metabolic pathways shared across all pathway modules.…”

“…Each module enables an optimized target product synthesis phenotype that leads to high yields, titers, and production rates. The different biochemical nature of each target metabolite can make the objectives compete with each other, turning the modular cell design problem into a multi-objective optimization problem known as ModCell2: [1] max y j ,z jk…”

“…Optimal solutions for a multi-objective optimization problem (1)(2)(3)(4)(5)(6)(7)(8) are defined based on the concept of domination: A vector a = (a 1 , . .…”

“…[4,5] Due to the current strain design process that is prohibitively laborious and expensive [6], the application of modular design principles commonly used in engineering [7] to microbial biocatalysis has been recently proposed to overcome this challenge. [1,[8][9][10] This modular cell design approach, known as ModCell, uses multi-objective optimization to account for the competing cellular objectives when cellular metabolism is (re)designed in a modular fashion to produce a diverse class of target chemicals. ModCell has been experimentally demonstrated for biosynthesis of alcohols [8,11,12] and esters [13][14][15][16][17] in Escherichia coli.…”

“…Recently, much attention has been placed in the development of MOEAs to solve many-objective problems (e.g., problems with 4 or more objectives) that often correspond to real-world applications, but can be very challenging to solve with conventional MOEAs. [19] For the case of ModCell problem, the popular MOEA NSGA-II [20,21] was used to design a modular cell under 20 different production modules, [1]. Due to the large chemical space of molecules that can potentially be synthesized by modular cells, scalability issues are expected to occur when constructing modular cells that are designed to be compatible with tenths or hundreds of products.…”