Session 1

Jihed Bentahar

University of Quebec at Rimouski (UQAR)

A Generalized Method for Enhancing Microalgal Biomass Production: Taguchi-Based Mathematical Modeling of Nutritional and Growth Kinetics - A Case Study on Tetradesmus obliquus

This study presents a novel methodology for improving the biomass productivities of microalgae cultured under mixotrophic conditions, with focus on nutrient concentrations in the media composition. We apply this methodology to the case of the green alga Tetradesmus obliquus, a very promising specie for various applications. Overall, we investigated the effects of eight key nutrients (factors) and their interactions on biomass production: glucose, nitrate, phosphate, sulfate, magnesium, iron, manganese, and calcium. The Taguchi method was applied for the design of two subsequent sets of experiments: a first to identify the main influential variables and near-optimal conditions, and a second to further the investigation and obtain a detailed descriptive mathematical model of the nutritional and growth kinetics. The Taguchi method was thus combined with mathematical modeling to maximize the usefulness of the experiments. Results showed a substantial enhancement in biomass yields, reaching a maximum of 7.98 g L-1, one of the highest reported yields for this specie. Nitrate had the most pronounced impact on biomass production, while iron and phosphate had a significant influence on short-term cultivations (6-9 days). Glucose and calcium had more impact on long-term cultivations (15 days). The mathematical model successfully integrated the effects of nitrate, phosphate, glucose, and iron in its equations, with a total of five state variables and nine model parameters. Model calibration was conducted using 12 out of 16 experimental data sets, and the reliability of parameter estimation was verified by 95% confidence intervals and relative error values. Model validation was then conducted on the four remaining data sets: results showed high accuracy predictions, with R2 correlation values ranging from 0.77 to 0.99. Overall, this methodology provided a powerful tool to improve biomass production. Future work will focus on developing real-time operation strategies for photobioreactors through simulation scenarios and real-life validation of results.

Aaron Yip

University of Waterloo

Engineering Aquatic Microbes to Degrade PET Plastics

Every year, 8-10 million metric tons of plastic enter the oceans, and 10 thousand metric tonnes enter the Great Lakes. Pollution of these water sources poses threats to aquatic ecosystems and human health. Plastic pollution in water could be dealt with by engineering aquatic microorganisms to biodegrade plastics. Polyethylene terephthalate (PET) plastics are a good candidate to target as they ubiquitous in single-use plastic products. With this rationale, we sought to engineer microbes from an environmental water sample to produce FAST-PETase, a state-of-the-art engineered enzyme that degrades PET. We developed a broad-host conjugating plasmid containing the FAST-PETase gene and a fluorescent reporter, and we successfully transferred the plasmid to endemic wastewater bacteria. We screened for isolates producing FAST-PETase by purifying the enzyme from concentrated cultures and visualizing the protein using SDS-PAGE. We then screened for activity of the produced FAST-PETase by incubating the enzyme with commercial PET plastic for up to one week at 50 C. Our work demonstrated the extent to which engineered microbes from an environmental sample could produce functional FAST-PETase for bioremediation applications. We believe this could have major implications for developments in environmental biotechnology focused on biodegradation of plastics in general.

Dalton Thomas Ham

University Of Western Ontario

A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets

The CRISPR/Cas9 nuclease from Streptococcus pyogenes (SpCas9) can be used with single guide RNAs (sgRNAs) as a sequence-specific antimicrobial agent and as a genome-engineering tool. However, current bacterial sgRNA activity models struggle with accurate predictions and do not generalize well, possibly because the underlying datasets used to train the models do not accurately measure SpCas9/sgRNA activity and cannot distinguish on-target cleavage from toxicity. We solved this problem by using a two-plasmid positive selection system to generate high-quality data that more accurately reports on SpCas9/sgRNA cleavage and that separates activity from toxicity. We developed a new machine learning architecture (crisprHAL) that can be trained on existing datasets and that shows marked improvements in sgRNA activity prediction accuracy when transfer learning is used with small amounts of high-quality data. The crisprHAL model recapitulates known SpCas9/sgRNA-target DNA interactions and provides a pathway to a generalizable sgRNA bacterial activity prediction tool.

Xinyue Wang

University of Ottawa

Effects of spacer length on immobilization of lipase on magnetic microparticles

In this study, we investigated the immobilization of lipase on polydopamine (PDA) modified magnetic microparticles with polyethyleneimine (PEI) of different length, i.e., 600, 1800, and 10,000 kDa, as the spacer and the effects of other immobilization conditions. We found that the PEI with a molecular weight of 600 kDa was the most effective in immobilizing lipase in terms of protein loading density, while the with PEI of 1800 kDa the highest total activity was achieved due to much higher specific activity of the enzyme. The optimal immobilization conditions are as follows: lipase concentration 4.25 mg/ml, pH 6, immobilization time 5 hours, and temperature 10°C. The immobilized lipase showed a broadened optimal pH range and improved thermal stability compared to the free lipase. The immobilized lipase maintained 50% of its activity after being used 10 times. These findings demonstrated the potential of PDA-modified magnetic microparticles with PEI as the spacer for immobilizing lipase and improving its performance for various industrial applications.