OVERVIEW

The CBDynamics project seeks to produce cannabidiol (CBD) by integrating genetic circuits that incorporate the biosynthetic pathway of Cannabis into the yeast Saccharomyces cerevisiae. This approach aims to provide a less bureaucratic and more economically viable method for drug production. In the current year, the USP-EEL-Brazil 2024 team has concentrated its efforts on executing wet lab experiments previously outlined while simultaneously developing methodologies to optimize the production of the target compound. These mechanisms are designed to enhance its competitiveness in comparison to traditional extraction methods from plant sources.

To achieve this optimization, the Design-Build-Test-Learn (DBTL) cycle was employed—a foundational concept in synthetic biology that offers engineering principles for project execution and the construction of biological systems with novel or improved functions. The DBTL cycle was systematically applied throughout the various stages of the project to establish a reliable platform for enhancing CBD production (KITANO et al., 2023; MENG et al., 2023).

Building upon the activities conducted in the previous project, the research team developed RNA interference and overexpression cassettes aimed at enhancing the accumulation of geranylpyrophosphate (GPP) within the cells. Following this, kinetic modeling was performed to ascertain the ideal theoretical substrate concentration required for cellular supply. Finally, we developed a model to optimize the growth of S. cerevisiae yeast, our chassis. The production of CBDA is directly linked to the quantity of cells, as it is an intracellular product, so if we can increase the quantity of cells to be produced, we will consequently improve our production.

Working alongside 2023 CBDynamics’ work and findings, in 2024 we were able to change lanes with our goals and expectations. This time, our main focus was on optimizing CBD production, with an emphasis on enhancing our biomanufacturing processes. By doing so, we aimed to find a more efficient and competitive approach for future market integration. To achieve this, our modeling team played a pivotal role, driving us toward our objectives and helping us attain the desired outcomes.

This time, we divided our modeling structure into four parts: Optimization Cassettes, Kinetic Modeling , and Biorreactor. All of which were integrated and interconnected in some form, to ensure its thoroughness and show our meticulously thought out efforts.

OPTIMIZATION CASSETTES

DESIGN

Our biological circuit design was the culmination of extensive modeling efforts. Through meticulous analysis, we were able to engineer expression cassettes that not only maintained the structural integrity and catalytic activity of the pathway enzymes but also ensured their optimal stability. In 2024, our primary goal was to improve upon what we had already worked on throughout 2023, managing to create cassettes that, when used in our model, significantly increased the production of CBD in the yeast S. cerevisiae.

For that, we worked on various aspects of the production output and improvement of stability. To enhance CBD production in our yeast, we developed cassettes that optimize enzyme activity: we focused on increasing GPP levels, a precursor to CBDA, as shown in figure 3 (Zirpel, Degenhardt, Martin, Kayser, & Stehle, 2017). Our strategy involved either reducing geranyl-diphosphate (GPP) conversion into farnesyl- diphosphate (FPP) (Chen, Guo, Zhang, & Si, 2020) and overproducing GPP (“super-expression”) (Luo et al., 2019). For better understanding of our activities, you can check our flowchart (Figure 2)

BUILD

With that goal in mind, we constructed cassettes using homologous recombination sites in the yeast genome. To target specific genes, we identified regions that could be silenced without harming the cell and employed knockout protein sites in each cassette (Chia et al., 2018).

To minimize the detour of GPP to the terpenoid pathway, we used the RNAi mechanism. The RNA interference mechanism is mediated by a series of key components, such as the Argonaute and Dicer proteins, which form a complex responsible for mediating the RNAi silencing mechanisms. Dicer belongs to the RNase III family of nucleases that process long dsRNA into 21- to 26-nucleotide (nt)-long RNA duplexes, which are fundamental specificity determinants in the RNA interference mechanism. The Argonaute protein is an essential component of protein complexes such as the RNA-induced silencing complex (RISC), and several studies suggest that Argonaute cleaves target messenger RNA in the RNAi pathway (MOAZED, 2009).

Our specific target was ERG20, an enzyme involved in GPP conversion into FPP. To ensure cell viability, however, since completely silencing its production would significantly harm the cells, we only partially silenced ERG20.

For that reason, we also used the following: Tet, a promoter inducible by the antibiotic tetracycline - in the RNAi cassette, the second one is imputed to complete the inverse of the first one to create a “hairpin” that will generate the “silencing” effect (Chen, Guo, Zhang, & Si, 2020); URA3, a selection mark and IRES, an internal site in the ribosome entryway (RBS in the middle of the circuit).

For GPP super-expression, we created a cassette targeting GPPS, the enzyme converting IPP to GPP through the pathway shown in image 3. Super-expression is a technique used to significantly increase the production of a specific protein in a cell (Luo et al., 2019). By manipulating the genetic material of the cell, we can boost the expression of the target gene, leading to higher levels of protein production. Our cassette, thus, serves that purpose, improving the CBD production inside the yeast cell.

In the cassette, HXK2 is the hexokinase promotor, that will generate more GPPS transcripts and, consequently, more proteins and GPPS is the CDS of the enzyme that makes the conversion of IPP into GPP.

Finally, we added polylinkers to both cassettes for future modifications. These restriction enzyme sites allow for easy manipulation of the cassettes, although they do not interfere with the current functionality of our cassettes (Amen & Kaganovich, 2017).

TEST

To better see and understand how our mechanism would possibly work, we built our cassettes linearly, so that we could view its size and entirety more clearly. Besides, we can also view it through the additions that were made as we worked through building the foundation for our finalized cassettes.

LEARN

In conclusion, our research has demonstrated the potential of synthetic biology to enhance CBD production in yeast. By meticulously designing and optimizing biological circuits, we were able to find ways we believe could significantly increase GPP levels and, consequently, CBD output. Our findings provide a promising foundation for further advancements in the field of plant-derived cannabinoid production.

KINETIC MODELING

DESIGN

In order to scale the synthesis pathway of cannabidiol (CBD) for industrial production, the USP-EEL-BRAZIL 2024 team proposed optimizing the efficiency of enzymatic reactions involved in the biosynthesis of this compound. To this end, kinetic modeling of the enzymes responsible for CBD production was carried out, with the aim of obtaining an in-depth understanding of the speed profile of each of them. In addition, it was investigated how different substrate and product concentrations influence enzyme activity in order to minimize enzyme inhibition. In this context, the ideal theoretical concentration of hexanoic acid, one of the substrates supplied to the cells, was calculated in order to maximize the production of cannabidiol acid (CBDA). This study is based on the search for better results for future experiments to be conducted at the wetlab, and for better understanding of our activities, you can check our flowchart (Figure 13).

The enzymes analyzed to elucidate their kinetic properties included cannabidiolic acid synthase (CBDAS), olivetolic acid cyclase (OAC), prenyltransferase 4 (CsaPT4), hexanoyl-CoA synthase (CsHCS1), and olivetol synthase (OLS). To achieve this objective, the research team conducted a literature review to gather data on the kinetic constants for each enzyme involved in the study and employed kinetic models, notably the Michaelis-Menten equation, to understand the initial velocity profiles of enzymatic reactions. This approach enabled the prediction of enzyme functionality under varying conditions and facilitated the identification of potential bottlenecks within the biosynthetic pathway.

The Michaelis-Menten model is a fundamental principle in biochemistry that describes the relationship between the rate of an enzymatic reaction and substrate concentration. The equation that expresses the initial reaction velocity v₀ as a function of substrate concentration [S], is given by the following formula (1) (Leow & Chan, 2019):

(1)

In this equation, Vmax represents the maximum reaction rate, achieved when the enzyme is saturated with the substrate, while Km is the Michaelis constant, indicating the substrate concentration at which the reaction rate is half of Vmax. The Km constant is particularly important because it provides a measure of the substrate’s affinity for the enzyme's active catalytic site, reflecting the enzyme’s efficiency in converting the substrate into product. To determine the velocity profiles of each enzyme, several critical assumptions were established. First, all other reactants involved in the reactions, with the exception of the substrates, were maintained in excess. Second, the conversion of reactants into products was assumed to occur without significant losses. Third, it was assumed that all cannabigerolic acid was completely converted into cannabidiolic acid. These assumptions were formulated to facilitate the calculations and to provide parameters that more accurately reflect real-world conditions. This approach acknowledges the inherent challenges associated with measuring conversions between products and reactants, as well as the velocities and kinetic constants of enzymes within living organisms.

BUILD

The process commenced with the collection of kinetic data from reputable sources, including the Brenda Enzyme and KEGG databases, as well as peer-reviewed literature (Wiles et al., 2022; Stout et al., 2022). The research team identified key enzyme codes and extracted kinetic parameters, such as Michaelis constants (Km) and maximum reaction rates (Vmax). The literature review yielded kinetic constant values for three enzymes (CBDAS, CsHCS1, and OLS). However, for the OAC and CsaPT4 enzymes, it was necessary to adopt a range of approaches to acquire these data. Furthermore, the review indicated that the enzymes CBDAS, CsaPT4, and OLS adhere to the Michaelis-Menten kinetics model, whereas the CsHCS1 enzyme exhibited substrate inhibition at elevated concentrations of CoA ( Stout et al., 2022).

1. CBDAS

The specific Km and Vmax values for the substrate, cannabigerolic acid (CBGA), are 0.137 mM and 2.57 nmol/sec/mg, respectively. These values were sourced from the Brenda Enzyme and Uniprot databases, as well as the study conducted by Wiles et al. (2022). The substrate concentration of 200 μM and the enzyme concentration of 0.00132 g/L were also obtained from the same article (Wiles et al., 2022). Prior to calculating the initial velocity, the maximum velocity corresponding to the enzyme amount used (0.66 μg) was determined using the rule of three, resulting in a value of 1.696 × 10⁻³ nKat. Subsequently, the initial velocity was calculated based on the Michaelis-Menten model.

1. CsaPT4

For the CsaPT4 enzyme, although the Km value has been documented in the literature as well as in the Brenda Enzyme and UniProt databases no Vmax data were available. To address this gap, a BLAST alignment was conducted using the NCBI database, which included 14 enzymes exhibiting over 40% similarity to CsaPT4. These enzymes were selected based on the criteria of not being specific to Cannabis sativa and having reported Km and Vmax values. The selected enzymes for this analysis comprised proteins from the genus Humulus lupulus, Aquilaria malaccensis homogentisate solanesyl transferase (HST1) and homogentisate phytyltransferase VTE2-2, among others.

Subsequently, a phylogenetic analysis was conducted using the sequences of the aforementioned enzymes, employing T-Coffee Muscle software with ClustalW alignment and a conservation threshold of 90%. The primary objective of this analysis was to compare the alignments of the catalytic sites of the selected enzymes with those of CsaPT4, aiming to identify conserved regions among them. Following the confirmation of homologous enzymes, a phylogenetic tree was constructed to assess the homology of each enzyme in relation to CsaPT4. Based on the similarity values, the mean and standard deviation of the Km and Vmax values for comparable enzymes were calculated and subsequently utilized as estimates for CsaPT4. The extrapolated values of Km and Vmax specific for olivetolic acid were determined to be 5.7 µM and 0.1 µmol/g/min, respectively. The substrate concentration of 200 μM was ascertained through the stoichiometry of the reaction, given that the ratio of cannabigerolic acid produced to olivetolic acid utilized is 1:1.

1. OAC

For the enzyme OAC, no Km or Vmax data were found in primary sources, including the BRENDA and UniProt databases. To address this limitation, a strategy was developed to estimate the kinetic parameters, similar to the methodology used for CsaPT4. The objective was to identify enzymes exhibiting at least 40% similarity to OAC, which were not specific to Cannabis Sativa, and for which Km and Vmax values had been reported in the literature. However, no enzymes satisfying all three criteria were identified. .

To bridge this gap, we expanded our search to compare two distinct families of enzymes in relation to OAC: DABB (α+β barrel polyketide cyclases) and chalcone isomerase. The selection of these enzymes was based on their structural and functional roles in cyclization reactions. Prior studies indicate that OAC is a functionally characterized polyketide cyclase responsible for catalyzing the C2-C7 aldol cyclization of linear pentyl tetra-β-ketide CoA into olivetolic acid (OA) (Yang et al., 2016; Gagne et al., 2012).

It is also important to note that OLS (olivetol synthase) works in conjunction with OAC, converting hexanoyl-CoA and malonyl-CoA into olivetol. This olivetol is then transferred into OAC's catalytic pocket and immediately converted into olivetolic acid. Due to this mechanism, the rate-limiting step between these two reactions is governed by OLS. As such, the kinetic parameters of OLS serve as an indirect but valid approximation for OAC's behavior, which allows for the omission of calculations pertaining to OAC’s initial reaction rate. The focus, therefore, shifted to analyzing the OLS kinetics.

To proceed with this estimation, we first determined the concentration of the substrate 3,5,7-trioxododecanoyl-CoA, which is the product of the OLS-catalyzed reaction and the direct substrate for OAC. Given the 1:1 stoichiometry ratio of the reaction and the known concentration of olivetolic acid at 200 μM, we conclude that the concentration of 3,5,7-trioxododecanoyl-CoA is also 200 μM.

1. OLS

The Km and Kcat values for the OLS enzyme were sourced from the UniProt database and the study conducted by Wiles et al. (2022), which reported a Km of 60.8 µM and a Kcat of 2.96 min⁻¹. To ascertain the concentration of the substrate hexanol-CoA, a stoichiometric analysis of the reaction was performed, revealing a 1:1 ratio. Given that the concentration of 3,5,7-trioxododecanoyl-CoA is 200 µM, it can be inferred that the concentration of hexanoyl-CoA is also 200 µM.

The concentration of the enzyme was calculated based on the findings of Taura et al. (2009), which indicated a pure enzyme mass of 5 mg dissolved in a volume of 1000 mL. To convert the enzyme mass from milligrams to daltons and subsequently determine the concentration in mol/L, a proportionality calculation was employed, yielding a concentration of 1.174 × 10⁻⁷ mol/L. Finally, to calculate the maximum velocity (Vmax) for the substrate hexanol-CoA, the appropriate formula was utilized:

By substituting the known values into the relevant equation, the calculated value of Vmax is determined to be.

1. CsHCS1

The Km and specific Vmax for the hexanoic acid substrate of the CsHCS1 enzyme were obtained from the UniProt database, with respective values of 3.7 M and 6.8 pmol/sec/g. In the study by Stout et al. (2022), the Ki value and the mass of the enzyme utilized were determined to be 5.101 mM and 0.1 μg, respectively. Utilizing this information, the enzyme concentration was calculated to be 5 x 10⁻³ g/L, based on the provided mass and a solution volume of 20 μL. The enzyme concentration in mol/L was subsequently calculated as 6.272 x 10⁻⁸ mol/L, employing the molar mass of the enzyme through a rule of three. Additionally, the maximum velocity, accounting for the supplied substrate amount, was also derived via the rule of three, yielding a value of 6.81 x 10⁻⁷ pKat. The substrate concentration was determined based on the stoichiometry of the reaction; considering a 1:1 ratio between hexanoyl-CoA and hexanoic acid, the concentration of hexanoic acid was established at 200 μM. SAs noted in the literature (Stout et al., 2022), the CsHCS1 enzyme exhibits inhibition at elevated CoA concentrations, prompting the application of the Michaelis-Menten equation for substrate inhibition, the following kinetic equation was employed (Leow & Chan, 2019):

TEST

The next step involved the application of kinetic models to calculate reaction velocities and identify the optimal substrate concentrations. For each enzyme, the inicial reaction rates were determined using the Km and Vmax values, supplemented by graphical representations that illustrate the kinetic rate profiles for each enzyme. As previously indicated, calculations of the initial rate and graphical analyses for the OAC enzyme were not conducted, as the OLS enzyme serves as the rate-limiting step in the metabolic pathway. Moreover, the sequential operation of both enzymes, characterized by a minimal time interval between reactions, permits simplification of the calculations by focusing solely on the initial rate of OLS. Furthermore, during the calculation of reaction rates, the concentration of each substrate involved in the reactions was also evaluated, employing a reverse methodology to ascertain the concentration of hexanoic acid to be supplied to the cell. Given the conservation of a 1:1 stoichiometric ratio among the substrates involved in the reactions, it is reasonable to assume that, under conditions of excess reagent, without significant losses throughout the process, and with the complete conversion of GPP to CBDA, the concentration of hexanoic acid will correspond to the concentration of cannabigerolic acid utilized by the cell, which, in this instance, is 200 µM. The initial velocity values and corresponding graphs for each enzyme are presented below.

1. CBDAS

The calculation of the initial velocity for the enzyme CBDA, based on its adherence to the Michaelis-Menten kinetic model, was conducted utilizing the following procedure:

1. CsaPT4

The determination of the specific initial velocity of the CsaPT4 enzyme, which adheres to the kinetic profile characterized by the Michaelis-Menten equation, was conducted using the following procedure:

1. OLS

The determination of the initial velocity of the OLS enzyme, which conforms to the kinetic profile defined by the Michaelis-Menten equation, was carried out utilizing the following procedure:

1. CsHCS1

The calculation of the initial velocity of CsHCS1 was performed using the Michael Menten formula for substrate inhibition:

LEARN

The analysis conducted in this study revealed several key findings. Kinetic modeling helped the identification of optimal conditions for the synthesis of CBDA, with particular emphasis on the theoretically ideal concentration of hexanoic acid. This determination is anticipated to facilitate dry laboratory testing by providing an estimate that enhances the efficiency of CBDA production. Furthermore, through systematic data collection and the development of enzyme rate equations, the research team successfully constructed reaction graphs that elucidates the initial rates of each enzyme, thereby offering a more comprehensive understanding of potential bottlenecks within the enzymatic reactions.

Nonetheless, challenges arose in the acquisition of kinetic data for OAC and PT4. By using bioinformatics tools such as BLAST and phylogenetic analysis, the team managed to estimate missing kinetic parameters for CsaPT4. However the retrieval of Km and Vmax values for enzymes analogous to OAC remained problematic, primarily due to the limited availability of data on enzymes that are not specific to Cannabis sativa. The kinetic model for CsHCS1, which incorporates substrate inhibition, proved essential in understanding how to avoid reduced enzyme activity under high substrate concentrations.

BIOREACTOR MODELING

As you know, engineering is an intrinsic part of any iGEM project, still, we feel a bit closer to it since we are from an engineering school and our team is fully composed of engineering students. Approaching problems with this perspective comes naturally to us, and we hope in this page we can convene the tools we applied and the methods with which we conducted each part of our work. From this, it is inevitable that we think and worry about increasing the scale of production, with the aim of making the process more optimized and applicable to industries. With this in mind, this year we focused our energy on developing the modeling for the bench-scale bioreactor.

DESIGN

As stated before, one of main goals with this project is to find an alternative way to supply the market demand for CBD that tackles the main issues with the current traditional production by extraction. In order to scale up the production to get it to an industrial level, it was imperative to seek knowledge on subjects such as the best bioreactor for our goals and the growth dynamics of our chassis. We tackled specifically an optimized celular growth, since we expect an intracellular production of our final product, the CBDA. To organize our steps better, our dry-lab leaders designed the Bioreactor Course of Action Flowchart.

Our modeling approach followed the steps outlined by Borowiak et al. (2012), with the aim of determining whether the model proposed by the authors could be applied to our yeast strain, despite being from different strains. In other words, we wanted to assess whether the model would be effective in promoting adequate growth in our S. cerevisiae.

With this in mind, over the course of the project, we developed several ideas focusing on key aspects such as cost-effectiveness, optimization and the circular economy. One of our initial proposals was to cultivate the yeast in cheese whey, a by-product widely produced by the food industry in Brazil. As our promoters from last year are inducible by galactose (see recap), this would align two major interests: cost-effectiveness and reuse of by-products. However, our yeast cannot metabolize lactose, the sugar present in whey, which would require the addition of an enzyme to break it down. Due to this limitation, we decided to explore other alternatives for the project.

Our team also did a lot of research to find the best reactor to conduct the process, thinking that we needed to have good stirring and mixing conditions. So, following in the author's footsteps (Borowiak et al., 2012), we opted for a stirred tank reactor (STR), as this is already widely described in the literature for yeast cultivation.

BUILD

The experiment we based ourselves on (Borowiak et al., 2012) proposed a logistic equation and defined the parameters a, b, and c based on the initial biomass concentration, using dissolved oxygen (DOT) as the control parameter for nutrient flow in the medium. From these initial parameters, the author calculated G(t) using equation 4, and with these values, he developed a modifier and applied analytical treatments to find optimized parameters at different initial cell concentrations. The goal was to adjust the original model to optimize nutrient inflow, aiming to maximize K (where K = Yμ, Y being the yeast biomass yield and μ the specific growth rate). However, the author found that simultaneously maximizing yield and specific growth rate is practically unfeasible, as these objectives are conflicting. This is because biomass yield is higher when glucose concentration is below a critical level, avoiding the Crabtree effect. On the other hand, the specific growth rate is maximized when glucose concentration exceeds this level, which results in partial conversion of glucose into ethanol, reducing biomass yield (Borowiak et al., 2012). Thus, our goal became finding a middle ground in this process, allowing for the highest possible biomass production.

(4)

For our project, we used the optimized parameters for an initial cell concentration of 3g/L (Table 1), as described by the author, to evaluate the efficiency of the proposed model and determine the best feeding conditions for the reactor to achieve maximum cell growth efficiency. This way, when the yeast is transformed and producing CBDA, we would already know how to control and feed the reactor to obtain the highest possible cell productivity, and consequently, greater cannabinoid production.

The entire process was conducted in a stirred-tank reactor through the fed-batch system, where controlling nutrient supply can increase both growth and biomass yield (Borowiak et al., 2012), making this an ideal strategy. To calculate the feed flow, we first used the logistic equation (4) to determine the glucose supply and then applied the nutrient supply time (ton) equation (equation 5) with a peristaltic pump supply rate of 0.96 L/h and a glucose concentration of 100 g/L in the medium. Based on this information, we calculated the volume of culture medium to add every 5 minutes (See in model). All these calculations were carried out using Python code, available on our contributions page.

We used the Saccharomyces cerevisiae SC9721 strain, available in our laboratory, a Mini Fors 2 bioreactor and YPD medium at 30°C, with the parameters already calculated for a biomass concentration of 3 g/L, which were similar to the conditions of the original experiment.

Although the experiment in the article was designed for small and medium-sized companies producing baker's yeast, our goal is industrial-scale production. However, we considered it important to start on a small scale to learn the details of the process and possible complications, with a view to proposing a model for expanded production. For this reason, we chose to work with a useful volume of 1 liter. In the microbiology laboratory, we followed the detailed bioreactor protocol and then gathered the results to obtain the growth curve, the yield and the relationship between the yield of yeast biomass and the specific growth rate.

TEST

First of all, we made sure to use the International System of Units for most of our calculations and, when necessary, we made practical conversions, such as changing the time from hours to minutes and the volume from liters to milliliters, since the values were very small. We also wrote down the units of measurement used throughout the process, in order to avoid errors of this kind and make the experiment easily replicable (See in model)

Initially, the reactor feed time was determined to be within 5 minutes, but this proposal does not suit our reality since the reactor to be used cannot be programmed to carry out this process. Therefore, using the known flow rate (W=0.96 L/h) we determined the volume of medium to be added every 30 minutes, and conducted the process by feeding at this interval. To further complement the process, we also dimensioned the bioreactor to find the best distance for the impellers used, the details of which can be seen in model.

In all, the process was carried out for a full 8 hours (Figure 20) and samples of 2 ml were taken from the reactor every 1 hour to determine the concentration of cells and glucose in the medium.

LEARN

All the samples taken from the reactor were subjected to optical density experiments to determine cell concentration, and the DNS test to determine glucose concentration (Table 2). In this way, it was possible to assess both substrate consumption and cell growth through curves constructed as a function of running time.

In figure 22, we can see three different curves as a function of time: the first, in yellow, is the ideal glucose mass of 0.1 g. This mass would maintain the ideal glucose concentration of 0.1 g/L, so the yeast metabolism would not be diverted towards ethanol by the CreebTree effect. The second curve, in blue, shows the glucose fed calculated using the volume of medium inoculated into the reactor every 30 minutes. As you might expect, this curve tends to increase as the calculation was made to add more and more glucose as the cells consumed it. Finally, the last curve in red is the mass of glucose remaining, obtained by the DNS test carried out on the samples taken from the reactor; this glucose represents that which has not been consumed by the yeasts. From this analysis, we can conclude that the initial concentration of cells added was possibly not enough to consume all the glucose fed into the reactor, which leads us to conclude that for a next test it would be interesting to increase the initial concentration of cells to be inoculated so that it is able to consume all the nutritional medium in the proposed time interval. .

It is also interesting to compare the concentration of cells and the concentration of glucose over time, as we can see the profile of this bioprocess and understand what can be improved. As we can see in Figure 23, there is an initial drop in cell concentration at the same time as there is an increase in the concentration of glucose remaining in the reactor, while there is exponential cell growth between the intervals of 2 hours and 6 hours accompanied by a drop in glucose concentration. So we can conclude that initially, due to instrumental problems, we had a drop in cell concentration and therefore the added glucose was not completely consumed, but after a few hours, we can see a typical curve of exponential cell growth accompanied by a drop in substrate concentration, which shows that this glucose was consumed more ferociously by the cell.

Source: Authors, 2024.

Finally, we can see in Figure 24 a graph of glucose mass per time, as is to be expected, since it is a fed batch, the glucose concentration does not decrease constantly, but at some points it exceeds the value of 0.1 g of ideal glucose previously established, which reinforces our theory that for this experiment, under these conditions, it would be interesting to increase the initial concentration of cells so that more glucose is consumed and kept constant at the value of 0.1 g throughout the test. Thus, through the data presented, we can conclude that we had cell growth, which was desirable, but there are still other points that can be improved in the process to obtain even more performance.

As good engineering students, after conducting this experiment, we compiled a list of points that can be improved in this process, in order to make a complete assessment of everything that can be enhanced or optimized with a view to large-scale production:

• Obtain specific microbial growth data at shorter time intervals to find a better growth curve, allowing us to determine a specific microbial growth rate.
• Study oxygen transfer, which is of utmost importance for aerated bioprocesses.
• Apply the model to other initial cell concentrations and assess whether glucose consumption is more consistent. We believe that during the course of the experiment, we had problems with cell growth due to sudden power outages at our university, and this may have affected the functioning of the equipment. Thus, while we were growing the cells for inoculum, we believe that in the end we obtained a smaller quantity than desired, and due to the short period of time it was not possible to repeat the experiment.

Finally, we understand that the union between the tools of synthetic biology and mathematical modeling has made it possible to bring an optimization proposal both on the molecular side, with the construction of the RNA interference and overexpression cassettes, together with the kinetics of the enzymes which allows a possible optimization of these main reactions for the process, and the modeling of the bioreactor which finishes this project with a golden key, bringing an approach aimed at increasing scale. At the end of the day, all these stages work together with the clear and unique goal of making this project viable for a real application. It's a big step that shows a promising future, thanks to engineering and synthetic biology coming together for one purpose. As engineering students and synthetic biology enthusiasts, we were very excited to bring this approach this year, and without a doubt we know that this function of areas is capable of revolutionizing processes and creating something unique and innovative.

PLASMID CONSTRUCTION

Problem Identification

Our project required the construction of a new plasmid, named pRS423, to simultaneously investigate two distinct genetic circuits in a single Saccharomyces cerevisiae cell. Although we already had the plasmid pRS426 with a selection marker for uracil, we needed a second plasmid with a different marker. This would allow us to work with both plasmids simultaneously, increasing the flexibility and scope of our experimental design.

DESIGN

To achieve this goal, we planned to integrate the selection marker ScHIS3 into the plasmid pRS426, creating the new plasmid pRS423s. We utilized the Gibson Assembly technique due to its efficiency and precision in DNA cloning. Initially, we transformed the plasmid pSB1A3-ScHIS3 into Escherichia coli through heat shock. After cloning confirmation by colony PCR, we made a glycerol stock of the positive colonies and purified the new plasmid to use it as a template for amplifying the marker ScHIS3, adding homology regions to facilitate assembly by Gibson Assembly.

BUILD

With the amplified and purified fragments of ScHIS3 and the pRS426 backbone, we proceeded to assemble the plasmid pRS423s. Using the Gibson Assembly protocol, we combined the fragments in a reaction incubated at 50°C for one hour. After assembly, we transformed the reaction product into competent E. coli cells and performed colony PCR to identify positive clones. These clones indicated a successful assembly of the new plasmid.

TEST

To verify the integrity of the plasmid pRS423, we performed a digestion with the restriction enzyme KpnI. We expected to observe two fragments on the agarose gel, corresponding to the marker ScHIS3 and the backbone of the plasmid. The analysis by electrophoresis showed the expected band pattern, confirming that the plasmid pRS423 was correctly assembled.

LEARN

During the construction process, we encountered an issue where the assembly failed, resulting in no positive clones. Upon reviewing our protocol, we realized that some reagents were not at optimal concentrations. After adjusting the reagents and repeating the assembly with more attention to detail, we successfully obtained the desired plasmid.

Cassette Cloning

Design

To achieve our goal, we planned to clone synthetic DNA cassettes into the plasmids pRS423 and pRS426. These cassettes, which contained specific gene sequences, were designed to include homology regions that would allow for efficient assembly using the Gibson Assembly technique. Gibson Assembly was chosen for its accuracy and ability to join multiple DNA fragments in a single reaction.

The cassettes were synthesized as gBlocks from IDT, and the plasmids were prepared by linearizing pRS423 and pRS426 through restriction digestion. Each cassette was designed with regions of overlap specific to the plasmid backbone, facilitating seamless integration during the assembly process. After assembly, the goal was to transform the plasmids into Escherichia coli and screen for successful integration through colony PCR and restriction digestion analysis.

Build

Once the plasmid construction was complete, the next step was to clone the synthetic DNA cassettes into the plasmids pRS423 and pRS426. We amplified the cassette pieces we got as synthetic DNA ensuring that each cassette had regions of homology for seamless insertion into the plasmids using Gibson Assembly. After assembling the plasmids with their respective cassettes, we transformed them into E. coli and screened for positive colonies by PCR and restriction digestion. Positive colonies were cultured and stocked for further testing.

In this phase, we encountered challenges with the larger cassettes, particularly with amplifying CBD_syn. We went through a series of PCRs and digestions, which necessitated restarting the entire process from the initial cultivation several times. Another adversity we faced was discovering a way to optimize the cloning process, as a well-executed cloning procedure is crucial for the successful integration of the cassettes. However, through careful optimization of the assembly process, we ultimately succeeded in integrating the cassettes into the plasmids.

Testing

To confirm the successful insertion of the cassettes into the plasmids, we performed restriction digestion experiments with miniprepped recombinant plasmids . The digested plasmids were analyzed on an agarose gel, where we expected to see distinct band patterns corresponding to the inserted cassettes and the plasmid backbones. The analysis showed the expected bands, confirming the successful integration of the cassettes into both pRS423 and pRS426.

Learning

During the amplification of the synthetic DNA cassettes (CBD_sub and CBD_syn), we encountered difficulties with IDT’s gBlocks. To address this, we performed a "mini Gibson Assembly," which allowed for the amplification and insertion of the cassettes into the plasmids pRS423 and pRS426. We adapted the protocol by optimizing the fragment concentrations and extending the reaction time to maximize the yield of the cassette fragments. Additionally, issues with the amplification of pRS423 led to us finding issues in the construction of this plasmid, requiring us to repeat the cloning process for generating pRS423s. After repeating it with greater care, we successfully confirmed the results through digestion and PCR.

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