Glazer, B. | Search bioRxiv

bioRxiv2025

Bioplastic Production Potential of Azospira suillum PS: Growth-Associated PHB Production Under Aerobic and Anaerobic Conditions

Plastics are indispensable in modern society, yet less than 10% of the 380 million tons produced annually are recycled. This accumulation of non-degradable waste has intensified the search for sustainable alternatives. Polyhydroxyalkanoates (PHAs), particularly polyhydroxybutyrate (PHB), are biodegradable polymers produced by microorganisms. Here, we investigate the capacity of Azospira suillum PS, a model facultative perchlorate-reducing bacterium, to synthesize PHB. We show that unlike most organisms, PHB production is growth-associated both aerobically, and anaerobically with perchlorate as an electron acceptor under moderate nitrogen deplete conditions. Genomic analysis revealed four phaC homologs, with one, phaC4, co-localized with phaB and phaR, suggesting a conserved operon. While three of the phaC genes were successfully deleted, phaC4 could not be disrupted, indicating an essential role for growth. Redox profiling revealed a positive correlation between PHB accumulation and NADPH/NADP+ ratios, supporting the role of cytoplasmic reducing equivalents in PHB synthesis. This study presents the first demonstration of PHA production under perchlorate-respiring conditions and one of the few documented cases of growth-associated PHA synthesis. It offers new insights into the genetic and metabolic basis of PHB production in A. suillum PS, underscoring its potential as a platform organism for sustainable, growth-coupled bioplastic production.

microbiology↗

bioRxiv2022

Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo Tree Search

1MotivationMany important processes in biology, such as signaling and gene regulation, can be described using logic models. These logic models are typically built to behaviorally emulate experimentally observed phenotypes, which are assumed to be steady states of a biological system. Most models are built by hand and therefore researchers are only able to consider one or perhaps a few potential mechanisms. We present a method to automatically synthesize Boolean logic models with a specified set of steady states. Our method, called MC-Boomer, is based on Monte Carlo Tree Search (MCTS), an efficient, parallel search method using reinforcement learning. Our approach enables users to constrain the model search space using prior knowledge or biochemical interaction databases, thus leading to generation of biologically plausible mechanistic hypotheses. Our approach can generate very large numbers of data-consistent models. To help develop mechanistic insight from these models, we developed analytical tools for multi-model inference and model selection. These tools reveal the key sets of interactions that govern the behavior of the models. ResultsWe demonstrate that MC-Boomer works well at reconstructing randomly generated models. Then, using single time point measurements and reasonable biological constraints, our method generates hundreds of thousands of candidate models that match experimentally validated in-vivo behaviors of the Drosophila segment polarity network. Finally we outline how our multimodel analysis procedures elucidate potentially novel biological mechanisms and provide opportunities for model-driven experimental validation. AvailabilityCode is available at: www.github.com/bglazer/mcboomer

systems biology↗