Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering

Abstract

Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including the multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.

A Retrospective Literature Evaluation of the Integration of Stress Physiology Indices, Animal Welfare and Climate Change Assessment of Livestock

“…Currently, the development of new-age technologies (e.g., remote sensing technology for reproductive and general health and well-being; artificial intelligence, [ 37 ] for the assessment of animal welfare and positive affective states (cognitive emotional well-being) has allowed animals to be assessed with greater ease than ever before [ 38 ].…”

Systems Biology to Understand and Regulate Human Retroviral Proinflammatory Response

“…Machine learning approaches were used in combination with systems biology to make prediction models where traditional modeling approaches failed due to their limitations or the unavailability of sufficient data. This was demonstrated in different fields including cancer, metabolic engineering and proinflammatory disease (87)(88)(89). In metabolic engineering, systems biology approaches combined with machine learning models were able to overcome several limitations in data quality and modeling scale (87).…”

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

“…This target is restricted due to the heterogeneous and high-dimensional datasets [111]. Second, despite the advances in genome-scale metabolic reconstructions, appropriate high-throughput data are only available for a small group of microorganisms [19]. Third, the results of the integrated models, although very accurate, are not necessarily appropriate for large-scale industrial fermentations.…”

“…Because of the rapid increase in omics datasets, many researchers prefer to use machine learning independently to interpret systems biology and metabolic engineering datasets. For instance, genome annotation, host strain selection, pathway discovery, metabolic pathway reconstruction, metabolic flux optimization, multi-omic data integration, and protein modeling can be obtained through machine learning methods [3,19]. Besides, due to the availability of the large amounts of fermentation parameter values from empirical studies, machine learning algorithms can be implemented directly to this multivariate system to fine-tune the fermentation conditions [20,21].…”