Gene knockout identification for metabolite production improvement using a hybrid of genetic ant colony optimization and flux balance analysis

Analysis of the Propionate Metabolism in Bacillus subtilis during 3-Indolacetic Production

The genera Bacillus belongs to the group of microorganisms that are known as plant growth-promoting bacteria, their metabolism has evolved to produce molecules that benefit the growth of the plant, and the production of 3-indole acetic acid (IAA) is part of its secondary metabolism. In this work, Bacillus subtilis was cultivated in a bioreactor to produce IAA using propionate and glucose as carbon sources in an M9-modified media; in both cases, tryptophan was added as a co-substrate. The yield of IAA using propionate is 17% higher compared to glucose. After 48 h of cultivation, the final concentration was 310 mg IAA/L using propionate and 230 mg IAA/L using glucose, with a concentration of 500 mg Trp/L. To gain more insight into propionate metabolism and its advantages, the genome-scale metabolic model of B. subtilis (iBSU 1147) and computational analysis were used to calculate flux distribution and evaluate the metabolic capabilities to produce IAA using propionate. The metabolic fluxes demonstrate that propionate uptake favors the production of precursors needed for the synthesis of the hormone, and the sensitivity analysis shows that the control of a specific growth rate has a positive impact on the production of IAA.

Enhanced production of l-sorbose by systematic engineering of dehydrogenases in Gluconobacter oxydans

l-Sorbose is an essential intermediate for the industrial production of vitamin C (l-ascorbic acid). However, the formation of fructose and some unknown by-products significantly reduces the conversion ratio of D-sorbitol to l-sorbose. This study aimed to identify the key D-sorbitol dehydrogenases in Gluconobacter oxydans WSH-003 by gene knockout. Then, a total of 38 dehydrogenases were knocked out in G. oxydans WSH-003, and 23 dehydrogenase-deficient strains could increase l-sorbose production. G. oxydans-30, wherein a pyrroloquinoline quinone-dependent glucose dehydrogenase was deleted, showed a significant reduction of a by-product with the extension of fermentation time. In addition, the highest conversion ratio of 99.60% was achieved in G. oxydans MD-16, in which 16 different types of dehydrogenases were inactivated consecutively. Finally, the gene vhb encoding hemoglobin was introduced into the strain. The titer of l-sorbose was 298.61 g/L in a 5-L bioreactor. The results showed that the systematic engineering of dehydrogenase could significantly enhance the production of l-sorbose.

Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering

Producing 'designer cells' with specific functions is potentially feasible in the near future. Recent developments, including whole-cell models, genome design algorithms and gene editing tools, have advanced the possibility of combining biological research and mathematical modelling to further understand and better design cellular processes. In this review, we will explore computational and experimental approaches used for metabolic and genome design. We will highlight the relevance of modelling in this process, and challenges associated with the generation of quantitative predictions about cell behaviour as a whole: although many cellular processes are well understood at the subsystem level, it has proved a hugely complex task to integrate separate components together to model and study an entire cell. We explore these developments, highlighting where computational design algorithms compensate for missing cellular information and underlining where computational models can complement and reduce lab experimentation. We will examine issues and illuminate the next steps for genome engineering.

An Agent-Based Clustering Approach for Gene Selection in Gene Expression Microarray

Gene selection is a major research area in microarray analysis, which seeks to discover differentially expressed genes for a particular target annotation. Such genes also often called informative genes are able to differentiate tissue samples belonging to different classes of the studied disease. Despite the fact that there is a wide number of proposals, the complexity imposed by this problem remains a challenge today. This research proposes a gene selection approach by means of a clustering-based multi-agent system. This proposal manages different filter methods and gene clustering through coordinated agents to discover informative gene subsets. To assess the reliability of our approach, we have used four important and public gene expression datasets, two Lung cancer datasets, Colon and Leukemia cancer dataset. The achieved results have been validated through cluster validity measures, visual analytics, a classifier and compared with other gene selection methods, proving the reliability of our proposal.