Application of Computer Assisted Bimolecular Interaction Modelling in Predictive Microbiology, Current State and Future Prospects
With the rapid evolution of Biotechnology and Green Technology, Microbial synthesis of commercially relevant biological compounds has been emerging for the past few decades. These economically valuable bio-compounds that have numerous applications to food, agriculture, chemical, and pharmaceutical industries. These low yield high-value products include several antibacterial and anticancer drugs, organic acids, amino acids, vitamins, industrial chemicals, and even biofuels. Biological synthesis of these extremely complex products often employs complex biochemical pathways performed under controlled culture environments, exploiting live cells; Often followed by appropriate downstream extraction and purification unit operations depending upon the nature and type of the product. In the last few decades, the latest innovations enabling constant improvement of nucleotide sequencing and computational methods for downstream analysis of sequence data have attracted a great number of biologists, mathematicians, and programmers across the world, in form of a tool potentially capable of drawing important biological conclusions. The introduction of these novel approaches has greatly contributed to the abundance of publicly available sequence data and analytical algorithms through a plentiful and ever-increasing number of studies turning towards the big data approaches. The inception of ‘Multiomics’ gave birth to in silco methods for identification of vital bimolecular interactions accountable for major phenotypes including those which account for, biosynthesis of several expensive bio-compounds. Most unpretentious application of these constraint-based models is to prioritize target pathways for “knock-in” or “knock out” approaches, and also to identify important pathways that are to be built into industrially relevant production organism(s) in synthetic biology. However, a more fanciful application of this mechanistic predictions could be drawing an inference about the environment in which the organism is living or was grown to express a certain physiological state. However, multiple major questions need to be addressed before one starts predicting optimal culture conditions using Omics level information. Here we focus on the major technological and scientific concepts that make up the core of, major scientific questions that need to be answered to improve the predictive power of such technologies, future prospects, and challenges associated with such integrative technology and their potential effect on the global economy.