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

Abstract

SUMMARYShifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together within silicostrain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the mainin silicoconstraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

When Do Two‐Stage Processes Outperform One‐Stage Processes?

“…For the one‐stage process we assume that a production strain has been designed which couples growth with product synthesis in a fixed (yield) ratio. A number of such growth‐coupled strain designs have been constructed in the past, often with the help of computational strain design methods . Accordingly, describes the (true) biomass ( X ) yield per substrate ( S ) used, and the true product ( P ) yield per biomass produced (for parameters and their units see Table ).…”

“…A number of such growth-coupled strain designs have been constructed in the past, often with the help of computational strain design methods. [1][2][3] Accordingly, Y true X/S describes the (true) biomass (X) yield per substrate (S) used, and Y true P/X the true product (P) yield per biomass produced (for parameters and their units see Table 1). Importantly, both Y true X/S and Y true P/X are true (maximum theoretical) yields thus not accounting for other uses of substrate including the synthesis of ATP for nongrowth associated maintenance (NGAM).…”

“…This challenge is usually tackled by a combination of methods for strain design (metabolic engineering) and for optimizing bioprocess conditions and strategies (e.g., temperature, pH, substrate concentrations, volume, feeding strategies, etc.). Strain design strategies often focus on the construction of suitable microorganisms that achieve high product yields while maintaining some minimal growth . Due to the inherent trade‐off between the production of biomass (needed as catalyst to synthesize the product) and the actual target product, yield, and volumetric productivity cannot be maximized simultaneously .…”

“…Strain design strategies often focus on the construction of suitable microorganisms that achieve high product yields while maintaining some minimal growth. [1][2][3] Due to the inherent trade-off between the production of biomass (needed as catalyst to synthesize the product) and the actual target product, yield, and volumetric productivity cannot be maximized simultaneously. [3][4][5][6] In this regard, two-stage fermentations, i.e., the separation of growth and production within a biotechnological process, in combination with dynamic metabolic engineering have recently gained much interest due to their high potential for improving the productivity of biotechnological production processes.…”

EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

“…As a relevant product we chose ethanol which has previously been used as a “benchmark” case in other studies2728. Different computational methods for calculating metabolic engineering strategies from stoichiometric models have been described in the literature9293031. Here we make use of the (constrained) minimal cut sets (MCS) approach which, among other applications, can be used for enumerating knockout strategies that couple growth with product synthesis (the product becomes a mandatory byproduct of biomass synthesis).…”