Applications of genome-scale metabolic network models in the biopharmaceutical industry

Abstract

Biotechnology is currently evolving through the era of big data, thanks to advances in the high-throughput technologies for rapid and inexpensive genome sequencing and other genome-wide studies [1]. With the daunting amount of data, it has been possible to put them together into a coherently organized biological network that provides counterintuitive insights on biological systems [2]. Among such biological networks, a genome-scale metabolic network model is expected to play an increasingly important role in the biopharmaceutical industry [3]. Before enumerating their specific strengths, it is important to note that principles underlying genome-scale metabolic network models are consistent with the holistic perspective of systems biology, the aim of which is to unveil hidden factors causing diseases and to find relevant treatment strategies [4]. Despite the importance of metabolism in a biological system, studies on diseases in relation to metabolism were far fewer in number than those performed on signaling and transcriptional regulatory networks [5]. However, metabolism, highly linked with observable phenotypes, is a biological network that is more comprehensively characterized when compared with the other two types of networks [6]. Metabolism is, therefore, amenable to large-scale mathematical modeling and simulation. It is with this motivation that the genome-scale metabolic simulation deserves more attention in drug discovery campaigns and optimization of a host strain for the production of biopharmaceuticals. Reconstruction and application of genome-scale metabolic network models have been forged as a major research strategy of systems biology. Over the last decade, genome-scale metabolic models have been built for almost all biologically important organisms across the domains of archea, bacteria and eukaryotes [3]. They range from simple micro organisms such as Escherichia coli [7] and Saccharomyces cerevisiae [8] to higher organisms including Chinese hamster ovary (CHO) cells [9,10] and a generic human cell [11,12]. It should be noted that all these organisms that have been subjected to metabolic modeling are important cellular hosts for biopharmaceutical production or medically meaningful organisms that need to be cured (e.g., specific cancer cells) or destroyed (e.g., pathogens). A recent notable development of importance in the genomescale metabolic modeling would be the newly updated human metabolic network Recon 2 [12]. Recon 2 is a result of efforts from a group of researchers, going over a vast amount of literature and biochemical data and reconciling conflicting information. Scope of the hitherto reconstructed genome-scale metabolic models manifest high expectations for their potential contributions to biopharmaceutical industry. Genome-scale metabolic network models are not just a simple pileup of biochemical reactions, but allow mathematical simulation under precisely defined conditions of constraints [13]. Once the experimentally Applications of genome-scale metabolic network models in the biopharmaceutical industry