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Genome-scale models (GEMs) have been expanded to compute both the metabolic and proteomic states of a cell in different conditions. These multi-scale models called genome-scale models of metabolism and macromolecular expression (ME-models) expand the scope of GEMs and provide greater predictive abilities, and therefore are being increasingly used in systems understanding of microbial biology. In this review, we describe ME-models, their advantages, and computational advances. We then describe extensions of ME-models to model stress response, and a related human red blood cell model that integrate metabolism and macromolecular mechanisms. Finally, we discuss how ME-models were recently used to explain resource allocation tradeoffs for microbial growth in adaptively evolved strains, in synthetic communities, and under stress.

© 2016 Elsevier Inc. Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host. This workflow incorporates complementary approaches from computational systems biology, metabolic engineering, and synthetic biology; provides molecular insight into how the host organism microenvironment changes due to pathway engineering; and demonstrates how biological mechanisms underlying strain variation can be exploited as an engineering strategy to increase product yield. As a proof of concept, we present the analysis of eight engineered strains producing three biofuels: isopentenol, limonene, and bisabolene. Application of this workflow identified the roles of candidate genes, pathways, and biochemical reactions in observed experimental phenomena and facilitated the construction of a mutant strain with improved productivity. The contributed workflow is available as an open-source tool in the form of iPython notebooks.

Systems-level design of cell metabolism is becoming increasingly important for renewable production of fuels, chemicals, and drugs. Computational models are improving in the accuracy and scope of predictions, but are also growing in complexity. Consequently, efficient and scalable algorithms are increasingly important for strain design. Previous algorithms helped to consolidate the utility of computational modeling in this field. To meet intensifying demands for high-performance strains, both the number and variety of genetic manipulations involved in strain construction are increasing. Existing algorithms have experienced combinatorial increases in computational complexity when applied toward the design of such complex strains. Here, we present EMILiO, a new algorithm that increases the scope of strain design to include reactions with individually optimized fluxes. Unlike existing approaches that would experience an explosion in complexity to solve this problem, we efficiently generated numerous alternate strain designs producing succinate, l-glutamate and l-serine. This was enabled by successive linear programming, a technique new to the area of computational strain design.

Computationally-aided metabolic engineering is an important, complementary strategy to combinatorial strain design for enhanced biochemical production by microbes. Bilevel optimization problems have been formulated for optimal strain design via reaction removal, activation, and inhibition. Deterministic global optimization of the resulting mixed integer linear programs (MILPs) requires extensive computational effort, especially for genome-scale models of metabolism. Improving the computational efficiency of such algorithms is an ongoing challenge. Here, we present Enhancing Metabolism with Iterative Linear Optimization (EMILiO)–a novel bilevel optimization-based algorithm that includes all possible flux modifications and is solved with remarkable computational efficiency via iterative linear programming. The resulting solution is recursively pruned to generate alternate, parsimonious strain designs with maximal biochemical production rates. We demonstrate our algorithm for succinate production using the i AF1260 genome-scale model of Escherichia coli metabolism. Our algorithm identifies aerobic succinate-producing strains with increased glyoxylate shunt activity, which is consistent with experiments in the literature. We also identified novel strain design strategies that may have implications for the control of industrial bioreactors to maximize succinate production.

Constraint-based models of metabolism seldom incorporate tight bounds, or capacity constraints, on intracellular fluxes due to the lack of experimental data. This can sometimes lead to inaccurate growth phenotype predictions. Meanwhile, other forms of data such as fitness profiling data from growth competition experiments have been demonstrated to contain valuable information for elucidating key aspects of the underlying metabolic network. Hence, the optimal capacity constraint identification (OCCI) algorithm is developed to reconcile constraint-based models of metabolism with fitness profiling data by identifying a set of flux capacity constraints that optimally fits a wide array of strains. OCCI is able to identify capacity constraints with considerable accuracy by matching 1155 in silico-generated growth rates using a simplified model of Escherichia coli central carbon metabolism. OCCI is expected to be a useful tool for integrating high-throughput fitness measurements with constraint-based models for elucidating metabolic network capacities.