Model based optimization of high cell density cultivation of nitrogen-fixing cyanobacteria

Process Intensification for Recombinant Protein Production in E. coli via Identification of Active Nodes in Cellular Metabolism and Dynamic Flux Balance Analysis

Complex media supplemented with a carbon source are commonly used in bioprocesses for recombinant protein production in Escherichia coli. Optimizing these processes is challenging and requires precise understanding of cellular metabolism and nutrient requirements. Compared to a design of experiments approach that necessitates extensive experimentation, metabolic modeling using a genome scale metabolic model (GEM) offers a more predictive and systematic approach to guide process optimization by identifying specific metabolic bottlenecks. In addition, spent media analysis (SMA) can unravel the preferential utilization of different media components during the bioprocess. Here, we integrated the updated E. coli GEM with time course SMA data from a fed-batch process and performed dynamic flux balance analysis (dFBA) to identify metabolites that function as active nodes and are vital for cellular function. These are potential target supplements to boost cellular activity and in turn the recombinant protein productivity. Using an iterative approach of performing fermentation, SMA, and metabolic modeling, we intensified the bioprocess in just five experimental trials, resulting in a six-fold increase in protein productivity. Our new feeding strategy involved yeast extract with amino acid supplementation (Ser, Thr, Asp, and Glu) and increased oxygen transfer rates. This approach demonstrates significant promise for application in bioprocess intensification.

Insufficient Acetyl-CoA Pool Restricts the Phototrophic Production of Organic Acids in Model Cyanobacteria

Cyanobacteria are promising biological chassis to produce biochemicals such as carboxylic acids and their derivatives from CO2. In this manuscript, we reflected on cyanobacterial acetyl-CoA pool and TCA cycle as an important source of precursor molecules for the biosynthesis of carboxylic acids such as 3-hydroxypropionate, 3-hydroxybutyrate, succinate, malate, fumarate and free fatty acids, each of which is an important platform chemical for bioeconomy. We further highlighted specific features of the cyanobacterial TCA cycle, how it differs in structure and function from widely described TCA cycles of heterotrophic model organisms, and methods to make it more suitable for the production of carboxylic acids from CO2. Currently, the yields of these compounds are significantly lower than those in heterotrophic organisms and it was concluded that the primary cause of this can be attributed to the limited flux toward acetyl-CoA. Strategies like overexpressing pyruvate dehydrogenase complex or introducing synthetic bypasses are being explored to overcome these limitations. While significant progress has been made, further research is needed to enhance the metabolic efficiency of cyanobacteria, making them viable for the large-scale, sustainable production of carboxylic acids and their derivatives.

Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications

Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.

Current states and challenges of salt-affected soil remediation by cyanobacteria

Natural and human activities lead to soil degradation and soil salinization. The decrease of farmlands threatens food security. There are approximately 1 billion ha salt-affected soils all over of world, which can be made available resources after chemical, physical and biological remediation. Nostoc, Anabaena and other cyanobacterial species have outstanding capabilities, such as the ability to fix nitrogen from the air, produce an extracellular matrix and produce compatible solutes. The remediation of salt-affected soil is a complex and difficult task. During the past years, much new research has been conducted that shows that cyanobacteria are effective for salt-affected soil remediation in laboratory studies and field trials. The related mechanisms for both salt tolerance and salt-affected soil remediation were also evaluated from the perspective of biochemistry, molecular biology and systems biology. The effect of cyanobacteria on salt-affected soil is related to nitrogen fixation and other mechanisms. There are complicated interactions among cyanobacteria, bacteria, fungi and the soil. The interaction between cyanobacteria and salt-tolerant plants should be considered if the cyanobacterium is utilized to improve the soil fertility in addition to performing soil remediation. It is critical to re-establish the micro-ecology in salt-affected soils and improve the salt affected soil remediation efficiency. The first challenge is the selection of suitable cyanobacterial strain. The co-culture of cyanobacteria and bacteria is also potential approach. The cultivation of cyanobacteria on a large scale should be optimized to improve productivity and decrease cost. The development of bio-remediating agents for salt-affected soil remediation also relies on other technical problems, such as harvesting and contamination control. The application of cyanobacteria in salt-affected soil remediation will reconstruct green agriculture and promote the sustainable development of human society.

Continuous cultivation of lipid rich microalga Chlorella sp. FC2 IITG for improved biodiesel productivity via control variable optimization and substrate driven pH control

A novel two-stage continuous heterotrophic cultivation of Chlorella sp. FC2 IITG was demonstrated for enhanced lipid productivity. Initially, effect of control variable e.g. dilution rate and feed stream substrate concentrations on biomass productivity was evaluated. This showed significant variation in biomass productivity from 2.4gL^-1day^-1 to 11.2gL^-1day^-1. Further, these control variables were optimized by using multi-nutrient mechanistic model for maximizing the biomass productivity. Finally, continuous production of lipid rich algal biomass was demonstrated in two sequential bioreactors for enhanced lipid productivity. The biomass productivity of 92.7gL^-1day^-1 was observed in the first reactor which was operated at model predicted optimal substrate concentrations of feed stream. The intracellular neutral lipid enrichment by acetate addition resulted in lipid productivity of 9.76gL^-1day^-1 in the second reactor. Both the biomass and lipid productivities obtained from current study are significantly high amongst similarly reported literatures.

Dynamic modelling of high biomass density cultivation and biohydrogen production in different scales of flat plate photobioreactors

This paper investigates the scaling‐up of cyanobacterial biomass cultivation and biohydrogen production from laboratory to industrial scale. Two main aspects are investigated and presented, which to the best of our knowledge have never been addressed, namely the construction of an accurate dynamic model to simulate cyanobacterial photo‐heterotrophic growth and biohydrogen production and the prediction of the maximum biomass and hydrogen production in different scales of photobioreactors. To achieve the current goals, experimental data obtained from a laboratory experimental setup are fitted by a dynamic model. Based on the current model, two key original findings are made in this work. First, it is found that selecting low‐chlorophyll mutants is an efficient way to increase both biomass concentration and hydrogen production particularly in a large scale photobioreactor. Second, the current work proposes that the width of industrial scale photobioreactors should not exceed 0.20 m for biomass cultivation and 0.05 m for biohydrogen production, as severe light attenuation can be induced in the reactor beyond this threshold. Biotechnol. Bioeng. 2015;112: 2429–2438. © 2015 The Authors. Biotechnology and Bioengineering Published by Wiley Peiodicals, Inc.

Influence of mixotrophic growth on rhythmic oscillations in expression of metabolic pathways in diazotrophic cyanobacterium Cyanothece sp. ATCC 51142

This study investigates the influence of mixotrophy on physiology and metabolism by analysis of global gene expression in unicellular diazotrophic cyanobacterium Cyanothece sp. ATCC 51142 (henceforth Cyanothece 51142). It was found that Cyanothece 51142 continues to oscillate between photosynthesis and respiration in continuous light under mixotrophy with cycle time of ∼ 13 h. Mixotrophy is marked by an extended respiratory phase compared with photoautotrophy. It can be argued that glycerol provides supplementary energy for nitrogen fixation, which is derived primarily from the glycogen reserves during photoautotrophy. The genes of NDH complex, cytochrome c oxidase and ATP synthase are significantly overexpressed in mixotrophy during the day compared to autotrophy with synchronous expression of the bidirectional hydrogenase genes possibly to maintain redox balance. However, nitrogenase complex remains exclusive to nighttime metabolism concomitantly with uptake hydrogenase. This study throws light on interrelations between metabolic pathways with implications in design of hydrogen producer strains.

Preferential utilization of intracellular nutrients supports microalgal growth under nutrient starvation: multi-nutrient mechanistic model and experimental validation

Microalgae are able to grow even under exhaustion of some key nutrients such as nitrogen and phosphorous. Here, we report a multi-nutrient mechanistic model to predict heterotrophic growth of Chlorella sp. FC2 IITG over two sequential phases of fermentation: nutrient sufficient condition to nutrient starved condition. The model assumes that the growth of the microorganism takes place via sequential utilization of extracellular nutrients (ECN) under nutrient replete condition followed by intracellular stored nutrients under exhaustion of limiting nutrients. Further, intracellular nutrient was assumed to be in three different forms: structural form of nutrient (SFN), readily utilizable nutrient (RUN) and non-readily utilizable nutrient (Non-RUN). After the exhaustion of ECN, microorganism switches to RUN followed by Non-RUN to continue its growth, which was experimentally validated by extracting intracellular nitrate and phosphate compounds. The model also incorporates variability in yield coefficients for nitrate and phosphate utilizations.