SMET: Systematic multiple enzyme targeting – a method to rationally design optimal strains for target chemical overproduction

Abstract

Identifying multiple enzyme targets for metabolic engineering is very critical for redirecting cellular metabolism to achieve desirable phenotypes, e.g., overproduction of a target chemical. The challenge is to determine which enzymes and how much of these enzymes should be manipulated by adding, deleting, under-, and/or over-expressing associated genes. In this study, we report the development of a systematic multiple enzyme targeting method (SMET), to rationally design optimal strains for target chemical overproduction. The SMET method combines both elementary mode analysis and ensemble metabolic modeling to derive SMET metrics including l-values and c-values that can identify rate-limiting reaction steps and suggest which enzymes and how much of these enzymes to manipulate to enhance product yields, titers, and productivities. We illustrated, tested, and validated the SMET method by analyzing two networks, a simple network for concept demonstration and an Escherichia coli metabolic network for aromatic amino acid overproduction. The SMET method could systematically predict simultaneous multiple enzyme targets and their optimized expression levels, consistent with experimental data from the literature, without performing an iterative sequence of single-enzyme perturbation. The SMET method was much more efficient and effective than single-enzyme perturbation in terms of computation time and finding improved solutions.

Development of a core Clostridium thermocellum kinetic metabolic model consistent with multiple genetic perturbations

“…Kinetic models such as k-ctherm118 can already be used to assess the computationally designed mutants in terms of predicted metabolite concentrations, needed enzyme levels, and unforeseen regulatory effects such as the nitrogen limitation case study showing increased amino acid yields due to changes in cofactor pools. In addition, computational strain design protocols such as k-OptForce [59] and SMET [60] that make use of kinetic information to overproduce a target metabolite can be applied to k-ctherm118 to increase biofuel production. Furthermore, k-ctherm118 lays the foundation for building genome-scale or consortia-based kinetic models of potential CBP organisms inclusive of substrate uptake and product toxicity kinetics to engineer high-performing industrial strains.…”

In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories

“…More recently, Trinh and coworkers presented the systematic multiple-enzyme targeting approach (SMET) (137). This method uses cMCS to find the set of modes maximizing a desired product yield and ensemble metabolic modeling (EMM) to generate ensemble models representing the steady-state phenotype of the wild-type strain.…”

Bridging the gap between systems biology and synthetic biology

“…The consistency between the computational results and the experimental measurements suggested that cell metabolism could be directed through programing design (Ranganathan et al, 2012). Another method developed by Flowers et al (2013) can systematically identify, instead of one enzyme at each time, multiple target enzymes, whose expression levels could be simultaneously manipulated to obtain the desired phenotype. This method improved the computation efficiency dramatically.…”