Sulfate limitation increases specific plasmid DNA yield and productivity in E. coli fed-batch processes

Predictive dynamic control accurately maps the design space for 2,3-butanediol production

2,3-Butanediol is a valuable raw material for many industries. Compared to its classical production from petroleum, novel fermentation-based manufacturing is an ecologically superior alternative. To be also economically feasible, the production bioprocesses need to be well optimized. Here, we adapted and applied a novel process optimization algorithm, dynamic control flux-balance analysis (dcFBA), for 2,3-butanediol production in E. coli. First, we performed two-stage fed-batch process simulations with varying process lengths. There, we found that the solution space can be separated into a proportionality and a trade-off region. With the information of the simulations we were able to design close-to-optimal production processes for maximizing titer and productivity, respectively. Experimental validations resulted in a titer of Image 1 and a productivity of Image 2. Subsequently, we optimized a continuous two-reactor process setup for 2,3-butanediol productivity. We found that in this mode, it is possible to increase the productivity more than threefold with minor impact on the titer and yield. Biotechnological process optimization is cumbersome, therefore, many processes are run in suboptimal conditions. We are confident that the method presented here, will help to make many biotechnological productions economically feasible in the future.

From Bioreactor to Bulk Rheology: Achieving Scalable Production of Highly Concentrated Circular DNA

DNA serves as a model system in polymer physics due to its ability to be obtained as a uniform polymer with controllable topology and nonequilibrium behavior. Currently, a major obstacle in the widespread adoption of DNA is obtaining it on a scale and cost basis that accommodates bulk rheology and high-throughput screening. To address this, recent advancements in bioreactor-based plasmid DNA production is coupled with anion exchange chromatography producing a unified approach to generating gram-scale quantities of monodisperse DNA. With this method, 1.1 grams of DNA is obtained per batch to generate solutions with concentrations up to 116 mg mL-1. This solution of uniform supercoiled and relaxed circular plasmid DNA, is roughly 69 times greater than the overlap concentration. The utility of this method is demonstrated by performing bulk rheology measurements at sample volumes up to 1 mL on DNA of different lengths, topologies, and concentrations. The measured elastic moduli are orders of magnitude larger than those previously reported for DNA and allowed for the construction of a time-concentration superposition curve that spans 12 decades of frequency. Ultimately, these results can provide important insights into the dynamics of ring polymers and the nature of highly condensed DNA dynamics.

Logistic PCA explains differences between genome-scale metabolic models in terms of metabolic pathways

Genome-scale metabolic models (GSMMs) offer a holistic view of biochemical reaction networks, enabling in-depth analyses of metabolism across species and tissues in multiple conditions. However, comparing GSMMs Against each other poses challenges as current dimensionality reduction algorithms or clustering methods lack mechanistic interpretability, and often rely on subjective assumptions. Here, we propose a new approach utilizing logisitic principal component analysis (LPCA) that efficiently clusters GSMMs while singling out mechanistic differences in terms of reactions and pathways that drive the categorization. We applied LPCA to multiple diverse datasets, including GSMMs of 222 Escherichia-strains, 343 budding yeasts (Saccharomycotina), 80 human tissues, and 2943 Firmicutes strains. Our findings demonstrate LPCA's effectiveness in preserving microbial phylogenetic relationships and discerning human tissue-specific metabolic profiles, exhibiting comparable performance to traditional methods like t-distributed stochastic neighborhood embedding (t-SNE) and Jaccard coefficients. Moreover, the subsystems and associated reactions identified by LPCA align with existing knowledge, underscoring its reliability in dissecting GSMMs and uncovering the underlying drivers of separation.

OptMSP: A toolbox for designing optimal multi-stage (bio)processes

One central goal of bioprocess engineering is to maximize the production of specific chemicals using microbial cell factories. Many bioprocesses are one-stage (batch) processes (OSPs), in which growth and product synthesis are coupled. However, OSPs often exhibit low volumetric productivities due to the competition for substrate for biomass and product synthesis implying trade-offs between biomass and product yields. Two-stage or, more generally, multi-stage processes (MSPs) offer the potential to tackle this trade-off for improved efficiency of bioprocesses, for example, by separating growth and production. MSPs have recently gained much attention, also because of a rapidly growing toolbox for the dynamic control of metabolic fluxes. Despite these promising advancements, computational tools specifically tailored for the optimal design of MSPs in the field of biotechnology are still lacking. Here, we present OptMSP, a new Python-based toolbox for identifying optimal MSPs maximizing a user-defined process metrics (such as volumetric productivity, yield, and titer or combinations thereof) under given constraints. In contrast to other methods, our framework starts with a set of well-defined modules representing relevant stages or sub-processes. Experimentally determined parameters (such as growth rates, substrate uptake and product formation rates) are used to build suitable ODE models describing the dynamic behavior of each module. OptMSP finds then the optimal combination of those modules, which, together with the optimal switching time points, maximize a given objective function. We demonstrate the applicability and relevance of the approach with three different case studies, including the example of lactate production by E. coli in a batch setup, where an aerobic growth phase can be combined with anaerobic production phases with or without growth and with or without enhanced ATP turnover.