Dynamic metabolic network modeling of a mammalian cell cycle using time-course multi-omics data

Cell cycle is a fundamental process for cell growth and proliferation, and its dysregulation leads to many diseases. How metabolic networks are regulated and rewired during the cell cycle is unknown. Here we apply a dynamic genome-scale metabolic modeling framework (DFA) to simulate a cell cycle of cytokine-activated murine pro-B cells. Phase-specific reaction activity predicted by DFA using time-course metabolomics were validated using matched time-course proteomics and phospho-proteomics data. Our model correctly predicted changes in methionine metabolism at the G1/S transition and the activation of lysine metabolism, nucleotides synthesis, fatty acid elongation and heme biosynthesis at the critical G0/G1 transition into cell growth and proliferation. Metabolic fluxes predicted from proteomics and phosphoproteomics constrained metabolic models were highly consistent with DFA fluxes and revealed that most reaction fluxes are regulated indirectly. Our model can help predict the impact of changes in nutrients, enzymes, or regulators on this critical cellular process.

Metabolic Network Modeling for Rational Drug Design against Candida albicans

The growing evidences of Candida albicans (C. albicans) infections are slowly becoming a threat to public health. Moreover, prevalence of antifungal resistant strains of C. albicans has emphasized the need for identification of potent targets for rational drug designing. In this aspect, traditional methods for target identification with validation have been found to be expensive and time-consuming. To overcome the concern, genome scale metabolic model construction provides a promising platform that allows novel target identification in combination with subtractive genome analysis. Thus, the chapter details current advancement in model construction, target identification and validation. In brief, it elucidates the overall strategies of C. albicans metabolome draft preparation, gap filling, curation of model, simulation followed by model validation, target identification and host pathogen interaction analysis. Finally, several examples of successful metabolic model construction and their utility in rational drug designing also have been discussed.

Engineering microbial chemical factories using metabolic models

Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such as bacteria) and higher organisms (such as plants) alike can be exploited to convert low value inputs into high value outputs. Unlike conventional chemical factories, microbial production chassis are not necessarily tuned for a single product overproduction. Despite the same end goal, metabolic and industrial engineers rely on different techniques for achieving productivity goals. Metabolic engineers cannot affect reaction rates by manipulating pressure and temperature, instead they have at their disposal a range of enzymes and transcriptional and translational processes to optimize accordingly. In this review, we first highlight how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed in systems and control engineering. Specifically, how algorithmic concepts derived in operations research can help explain the structure and organization of metabolic networks. Finally, we consider the future directions and challenges faced by the field of metabolic network modeling and the possible contributions of concepts drawn from the classical fields of chemical and control engineering. The aim of the review is to offer a current perspective of metabolic engineering and all that it entails without requiring specialized knowledge of bioinformatics or systems biology.