Simulation-based Reconstructed Diffusion unveils the effect of aging on protein diffusion in Escherichia coli

We have developed Simulation-based Reconstructed Diffusion (SbRD) to determine diffusion coefficients corrected for confinement effects and for the bias introduced by two-dimensional models describing a three-dimensional motion. We validate the method on simulated diffusion data in three-dimensional cell-shaped compartments. We use SbRD, combined with a new cell detection method, to determine the diffusion coefficients of a set of native proteins in Escherichia coli. We observe slower diffusion at the cell poles than in the nucleoid region of exponentially growing cells, which is independent of the presence of polysomes. Furthermore, we show that the newly formed pole of dividing cells exhibits a faster diffusion than the old one. We hypothesize that the observed slowdown at the cell poles is caused by the accumulation of aggregated or damaged proteins, and that the effect is asymmetric due to cell aging.

Synergistic experimental and computational approach identifies novel strategies for polyhydroxybutyrate overproduction

Polyhydroxybutyrate (PHB) is a sustainable bioplastic produced by bacteria that is a potential replacement for conventional plastics. This study delivers an integrated experimental and computational modeling approach to decipher metabolic factors controlling PHB production and offers engineering design strategies to boost production. In the metabolically robust Rhodopseudomonas palustris CGA009, PHB production significantly increased when grown on the carbon- and electron-rich lignin breakdown product p-coumarate (C₉H₈O₃) compared to virtually no PHB titer from acetate (C₂H₃NaO₂). The maximum yield did not improve further when grown on coniferyl alcohol (C₁₀H₁₂O₃), but comparison of the PHB profiles showed that coniferyl alcohol's higher carbon content resulted in a higher rate of PHB production. Combined experimental results revealed that cytoplasmic space may be a limiting factor for maximum PHB titer. In order to obtain a systems-level understanding of factors driving PHB yield, a model-driven investigation was performed. The model yielded several engineering design strategies including utilizing reduced, high molecular weight substrates that bypass the thiolase reaction (phaA). Based on these strategies, utilization of butyrate was predicted and subsequently validated to produce PHB. Model analysis also explained why nitrogen starvation was not essential for PHB production and revealed that renewable and abundant lignin aromatics are ideal candidates for PHB production. Most importantly, the generality of the derived design rules allows them to be applied to any PHB-producing microbe with similar metabolic features.

Computational Resources and Strategies to Construct Single-Molecule Models of FISH

Currently, the interactions occurring between oligonucleotides and the cellular envelope of bacteria are not fully resolved at the molecular level. Understanding these interactions is essential to gain insights on how to improve the internalization of the tagged oligonucleotides during fluorescence in situ hybridization (FISH). Agent-based modeling (ABM) is a promising in silico tool to dynamically simulate FISH and bring forward new knowledge on this process. Notably, it is important to simulate the whole bacterial cell, including the different layers of the cell envelope, given that the oligonucleotide must cross the envelope to reach its target in the cytosol. In addition, it is also important to characterize other molecules in the cell to best emulate the cell and represent molecular crowding. Here, we review the main information that should be compiled to construct an ABM on FISH and provide a practical example of an oligonucleotide targeting the 23S rRNA of Escherichia coli .

Computational resources and strategies to assess single-molecule dynamics of the translation process in S. cerevisiae

This work provides a systematic and comprehensive overview of available resources for the molecular-scale modelling of the translation process through agent-based modelling. The case study is the translation in Saccharomyces cerevisiae, one of the most studied yeasts. The data curation workflow encompassed structural information about the yeast (i.e. the simulation environment), and the proteins, ribonucleic acids and other types of molecules involved in the process (i.e. the agents). Moreover, it covers the main process events, such as diffusion (i.e. motion of molecules in the environment) and collision efficiency (i.e. interaction between molecules). Data previously determined by wet-lab techniques were preferred, resorting to computational predictions/extrapolations only when strictly necessary. The computational modelling of the translation processes is of added industrial interest, since it may bring forward knowledge on how to control such phenomena and enhance the production of proteins of interest in a faster and more efficient manner.

Particle-Based Simulation Reveals Macromolecular Crowding Effects on the Michaelis-Menten Mechanism

Many computational models for analyzing and predicting cell physiology rely on in vitro data collected in dilute and controlled buffer solutions. However, this can mislead models because up to 40% of the intracellular volume—depending on the organism, the physiology, and the cellular compartment—is occupied by a dense mixture of proteins, lipids, polysaccharides, RNA, and DNA. These intracellular macromolecules interfere with the interactions of enzymes and their reactants and thus affect the kinetics of biochemical reactions, making in vivo reactions considerably more complex than the in vitro data indicates. In this work, we present a new, to our knowledge, type of kinetics that captures and quantifies the effect of volume exclusion and other spatial phenomena on the kinetics of elementary reactions. We further developed a framework that allows for the efficient parameterization of these kinetics using particle simulations. Our formulation, entitled generalized elementary kinetics, can be used to analyze and predict the effect of intracellular crowding on enzymatic reactions and was herein applied to investigate the influence of crowding on phosphoglycerate mutase in Escherichia coli, which exhibits prototypical reversible Michaelis-Menten kinetics. Current research indicates that many enzymes are reaction limited and not diffusion limited, and our results suggest that the influence of fractal diffusion is minimal for these reaction-limited enzymes. Instead, increased association rates and decreased dissociation rates lead to a strong decrease in the effective maximal velocities V_max and the effective Michaelis-Menten constants K_M under physiologically relevant volume occupancies. Finally, the effects of crowding were explored in the context of a linear pathway, with the finding that crowding can have a redistributing effect on the effective flux responses in the case of twofold enzyme overexpression. We suggest that this framework, in combination with detailed kinetics models, will improve our understanding of enzyme reaction networks under nonideal conditions.

Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling

Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale.