Understanding Metabolic Flux Behaviour in Whole-Cell Model Output

Precise metabolic modeling in post-omics era: accomplishments and perspectives

Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.

Data hazards in synthetic biology

Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.

Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology

Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.

Immobilised-enzyme microreactors for the identification and synthesis of conjugated drug metabolites

The study of naturally circulating drug metabolites has been a focus of interest, since these metabolites may have different therapeutic and toxicological effects compared to the parent drug. The synthesis of metabolites outside of the human body is vital in order to conduct studies into the pharmacological activities of drugs and bioactive compounds. Current synthesis methods require significant purification and separation efforts or do not provide sufficient quantities for use in pharmacology experiments. Thus, there is a need for simple methods yielding high conversions whilst bypassing the requirement for a separation. Here we have developed and optimised flow chemistry methods in glass microfluidic reactors utilising surface-immobilised enzymes for sulfonation (SULT1a1) and glucuronidation (UGT1a1). Conversion occurs in flow, the precursor and co-factor are pumped through the device, react with the immobilised enzymes and the product is then simply collected at the outlet with no separation from a complex biological matrix required. Conversion only occurred when both the correct co-factor and enzyme were present within the microfluidic system. Yields of 0.97 ± 0.26 μg were obtained from the conversion of resorufin into resorufin sulfate over 2 h with the SULT1a1 enzyme and 0.47 μg of resorufin glucuronide over 4 h for UGT1a1. This was demonstrated to be significantly more than static test tube reactions at 0.22 μg (SULT1a1) and 0.19 μg (UGT1a1) over 4 h. With scaling out and parallelising, useable quantities of hundreds of micrograms for use in pharmacology studies can be synthesised simply.

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications

Adaptive laboratory evolution (ALE) has long been used as the tool of choice for microbial engineering applications, ranging from the production of commodity chemicals to the innovation of complex phenotypes. With the advent of systems and synthetic biology, the ALE experimental design has become increasingly sophisticated. For instance, implementation of in silico metabolic model reconstruction and advanced synthetic biology tools have facilitated the effective coupling of desired traits to adaptive phenotypes. Furthermore, various multi-omic tools now enable in-depth analysis of cellular states, providing a comprehensive understanding of the biology of even the most genomically perturbed systems. Emerging machine learning approaches would assist in streamlining the interpretation of massive and multiplexed datasets and promoting our understanding of complexity in biology. This review covers some of the representative case studies among the 700 independent ALE studies reported to date, outlining key ideas, principles, and important mechanisms underlying ALE designs in bioproduction and synthetic cell engineering, with evidence from literatures to aid comprehension.