Machine learning for metabolic pathway optimization: A review

Understanding Cytochrome P450 Enzyme Substrate Inhibition and Prospects for Elimination Strategies

Cytochrome P450 (CYP450) enzymes, which are widely distributed and pivotal in various biochemical reactions, catalyze diverse processes such as hydroxylation, epoxidation, dehydrogenation, dealkylation, nitrification, and bond formation. These enzymes have been applied in drug metabolism, antibiotic production, bioremediation, and fine chemical synthesis. Recent research revealed that CYP450 catalytic kinetics deviated from the classic Michaelis-Menten model. A notable substrate inhibition phenomenon that affects the catalytic efficiency of CYP450 at high substrate concentrations was identified. However, the substrate inhibition of various reactions catalyzed by CYP450 enzymes have not been comprehensively reviewed. This review describes CYP450 substrate inhibition examples and atypical Michaelis-Menten kinetic models, and provides insight into mechanisms of these enzymes. We also reviewed 3D structure and dynamics of CYP450 with substrate binding. Outline methods for alleviating substrate inhibition in CYP450 and other enzymes, including traditional fermentation approaches and protein engineering modifications. The comprehensive analysis presented in this study lays the foundation for enhancing the catalytic efficiency of CYP450 by deregulating substrate inhibition.

Promising non-model microbial cell factories obtained by genome reduction

The development of sustainable processes is the most important basis to realize the shift from the fossil-fuel based industry to bio-based production. Non-model microbes represent a great resource due to their advantageous traits and unique repertoire of bioproducts. However, most of these microbes require modifications to improve their growth and production capacities as well as robustness in terms of genetic stability. For this, genome reduction is a valuable and powerful approach to meet industry requirements and to design highly efficient production strains. Here, we provide an overview of various genome reduction approaches in prokaryotic microorganisms, with a focus on non-model organisms, and highlight the example of a successful genome-reduced model organism chassis. Furthermore, we discuss the advances and challenges of promising non-model microbial chassis.

Tunable translation-level CRISPR interference by dCas13 and engineered gRNA in bacteria

Although CRISPR-dCas13, the RNA-guided RNA-binding protein, was recently exploited as a translation-level gene expression modulator, it has still been difficult to precisely control the level due to the lack of detailed characterization. Here, we develop a synthetic tunable translation-level CRISPR interference (Tl-CRISPRi) system based on the engineered guide RNAs that enable precise and predictable down-regulation of mRNA translation. First, we optimize the Tl-CRISPRi system for specific and multiplexed repression of genes at the translation level. We also show that the Tl-CRISPRi system is more suitable for independently regulating each gene in a polycistronic operon than the transcription-level CRISPRi (Tx-CRISPRi) system. We further engineer the handle structure of guide RNA for tunable and predictable repression of various genes in Escherichia coli and Vibrio natriegens. This tunable Tl-CRISPRi system is applied to increase the production of 3-hydroxypropionic acid (3-HP) by 14.2-fold via redirecting the metabolic flux, indicating the usefulness of this system for the flux optimization in the microbial cell factories based on the RNA-targeting machinery.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.