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The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.

Cordyceps militaris is an entomopathogenic fungus which is often used in Asia as a traditional medicine developed from age-old wisdom. Presently, cordycepin from C. militaris is a great interest in medicinal applications. However, cellular growth of C. militaris and the association with cordycepin production remain poorly understood. To explore the metabolism of C. militaris as potential cell factories in medical and biotechnology applications, this study developed a high-quality genome-scale metabolic model of C. militaris, iNR1329, based on its genomic content and physiological data. The model included a total of 1329 genes, 1821 biochemical reactions, and 1171 metabolites among 4 different cellular compartments. Its in silico growth simulation results agreed well with experimental data on different carbon sources. iNR1329 was further used for optimizing the growth and cordycepin overproduction using a novel approach, POPCORN, for rational design of synthetic media. In addition to the high-quality GEM iNR1329, the presented POPCORN approach was successfully used to rationally design an optimal synthetic medium with C:N ratio of 8:1 for enhancing 3.5-fold increase in cordycepin production. This study thus provides a novel insight into C. militaris physiology and highlights a potential GEM-driven method for synthetic media design and metabolic engineering application. The iNR1329 and the POPCORN approach are available at the GitHub repository: https://github.com/sysbiomics/Cordyceps_militaris-GEM.

Metabolic engineering modifies cellular function to address various biochemical applications. Underlying metabolic engineering efforts are a host of tools and knowledge that are integrated to enable successful outcomes. Concurrent development of computational and experimental tools has enabled different approaches to metabolic engineering. One approach is to leverage knowledge and computational tools to prospectively predict designs to achieve the desired outcome. An alternative approach is to utilize combinatorial experimental tools to empirically explore the range of cellular function and to screen for desired traits. This mini-review focuses on computational systems biology and synthetic biology tools that can be used in combination for prospective in silico strain design.