Genome-scale metabolic model-based engineering of Escherichia coli enhances recombinant single-chain antibody fragment production

Modulating Microbial Materials - Engineering Bacterial Cellulose with Synthetic Biology

The fusion of synthetic biology and materials science offers exciting opportunities to produce sustainable materials that can perform programmed biological functions such as sensing and responding or enhance material properties through biological means. Bacterial cellulose (BC) is a unique material for this challenge due to its high-performance material properties and ease of production from culturable microbes. Research in the past decade has focused on expanding the benefits and applications of BC through many approaches. Here, we explore how the current landscape of BC-based biomaterials is being shaped by progress in synthetic biology. As well as discussing how it can aid production of more BC and BC with tailored material properties, we place special emphasis on the potential of using BC for engineered living materials (ELMs); materials of a biological nature designed to carry out specific tasks. We also explore the role of 3D bioprinting being used for BC-based ELMs and highlight specific opportunities that this can bring. As synthetic biology continues to advance, it will drive further innovation in BC-based materials and ELMs, enabling many new applications that can help address problems in the modern world, in both biomedicine and many other application fields.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.