We initially decided to knock out two genes -Alk2 and FAA1 from our Po1g strain. We were successfully able to knock out the FAA1 gene, this was proved by the LC-HRMS results which showed a high increase in the fatty acid content of the cell. We had amplified the ALK2 deletion cassette but we couldn't integrate the cassette into Po1g strain due to time constraints. We intend to complete this knockout and create the strain with both FAA1 and ALK2 genes deleted in the future. We would then integrate the pYLEX-Jc plasmid into this strain with the engineered hydrocarbon pump also expressed, giving us the final required strain with increased hydrocarbon production.

Till now the knockouts in the strain have been done by swapping the genes we want to delete with antibiotic resistance genes. Since we want to scale up the production on an industrial scale, having strains with antibiotic resistance is not economically and environmentally very feasible. So we would carry out the deletions again with the Cre-loxP method enabling selectable markers to be removed from the strain resulting in antibiotic resistance-free genes.

We would also like to carry out a genetic stability analysis of our final strain as part of measurements to know the stability of our integrated genes in the Y.lipolytica genome.

Media Optimizations

We intend to further reduce the cost of production and since the cost of the culturing media constituents makes up a significant part of the costs, we intend to culture our strain in different distilled waste media like wastewater, agriculture feedstock waste, etc, and carry out media optimization for these different media with DoE principles like we did in the measurements page for our current media and find the optimal conditions for the growth of our engineered strain; thus helping us reduce the costs further.

Dry Lab

Hydrocarbon Efflux Pump Expression

We propose to improve this primary design with further in-silico engineering interventions and to validate the results more quantitatively using GROMACS MM-PBSA Binding Energy estimation. Further analysis of its efficiency will be performed by expressing the variant pump in Y.lipolytica and comparing the concentration of alkanes and fatty acids in the media with the untransformed strain as the control.A higher alkane to fatty acid concentration in the medium for the test as compared to the control will be the expected result and will serve as the final proof of concept for the project.

Once the efficiency of the pump is validated in the wet lab, the variant pump will be expressed along with JcFatA, JcFatB and CvFAP in Y.lipolytica, enabling the extraction of the produced hydrocarbons from the medium feasible. This becomes essential to the operation of the bioreactor as the extraction of the desired hydrocarbons via lysing the cells will be unsustainable at this scale.

TE Engineering-based on the FAS integration model

Since the Thioesterase (TE) from J.curcas and the FAS complex from Y.lipolytica belong to two structurally different fatty acid synthesis systems, the interaction between them brought in by our genetic interventions are ‘unusual‘ from an evolutionary perspective. Unlike other natural protein - protein interactions, the TE - FAS interaction is not optimized by the evolutionary force and we will be focusing our engineering efforts to improve the efficiency of this interaction.

The TE - FAS integration model that we characterized will be utilized to guide these efforts. We propose to subject the identified interacting residues of JcFatB to a combination of random mutations and analysis of the change in the binding energy to design mutant varieties of the protein with the desired quality of improved binding without affecting its the catalytic function.

Once the optimized JcFatB is designed, it will be expressed in our chassis and its activity compared with the strains transformed with wild-type JcFatB. Obtaining the fatty acid composition closer to that of Jatropha curcas in the case of the engineered JcFatB as compared to the wild-type will be final proof of concept. Once the improved interaction is quantified in wet - lab, Y.lipolytica with the variant JcFatB expression will be used in the bioreactors, improving the overall expression of the production.

We also propose to undertake a similar approach in modelling the interaction between JcFatA and the FAS complex and to engineer the former protein based on this model to improve the interaction efficiency.


Feedstock Utilisation to Make the Project More Sustainable and Impactful

The utilization of sustainable feedstock and waste materials for biomanufacturing biofuels offers several significant benefits, which contribute to environmental, economic, and social sustainability. Here are some of the key significances:

With that, our current strain can use both glucose and sucrose as its main carbon sources. But research has shown that Yarrowia is capable of using multiple other ones

Biomass residues after harvesting of feed and food part of the forestry such as small branches leaves, decayed flowers and fruits and agricultural crops such as corn stover, corn cobs, wheat, small grain straw, etc. can be converted to renewable fuels.

Generally rice, wheat and corn straw as well as sugarcane bagasse considered as major agro-waste feedstocks for biofuel production. Looking to the composition of bagasse there is 19–24% lignin, 27–32% hemicelluloses, 32–44% cellulose and 4.5–9.0% ashes as well as small fraction of minerals, waxes and other compounds. Looking to biochemical composition of rice straw, it comprises 32–47% cellulose, 19–27% hemicelluloses, 5–24% lignin and 19% ashes. Carbohydrate portion of rice straw contains 41–43% glucose, 15–20% xylose, 3–5% arabinose, 2% mannose and 0.4% galactose.

It has been seen that Yarrowia can already assimilate all of these carbon sources. Under, future implementation we plan to use these cheaper sources of carbon in a bioreactor.

Making the Fuel Carbon Neutral

We propose a co-culture system involving Synechococcus elongatus and Yarrowia lipolytica. Synechococcus, a cyanobacterium, demonstrates the capability to utilize atmospheric CO2, converting it into sucrose. Through genetic engineering, we will introduce a non-native pump into Synechococcus, allowing it to produce and secrete excess sucrose when grown under high salt conditions, thanks to efflux pumps.Yarrowia lipolytica and Synechoccus elongatus have very similar growth rates. Yarrowia lipolytica, proficient in metabolizing sucrose, is an ideal partner in this co-culture.

UTEX 2973, a rapid-growing strain of cyanobacteria, has been selected for the future implementation of the project, as it intracellularly accumulates sucrose when exposed to salt stress. This sucrose originates from the fixation of atmospheric CO2 during photosynthesis. By incorporating a non-native sucrose transporter, cscB, into this cyanobacterium, it can continuously secrete the sucrose it produces. Remarkably, S. elongatus UTEX 2973 can secrete nearly 90% of its absorbed carbon as sucrose, in contrast to sugarcane, which only secretes around 15% of its carbon content.

The two organisms will be cultivated in separate bioreactors, with a shared air circulation system connecting them. This design enables the transfer of CO2 released by Y. lipolytica into the growth chamber of S. elongatus, where it can be converted into sucrose. Conversely, the oxygen generated by S. elongatus will be transferred to the growth chamber of Yarrowia to support its respiratory processes.

This secreted sucrose serves as a sustainable carbon feedstock for chemical synthesis, particularly when combined with Yarrowia lipolytica.


The evident next thing to do is to try to apply for a patent for our product. There are some future experiments that have been designed but can’t be disclosed here for the sake of IP. This plan will be carried out in the coming months. This will also mark the inception of JetroEco as a start-up. To allow us to be more certain with the product, we did an elaborate cost analysis with multiple iterations to reduce the cost.

Learn more about our plans in our Entrepreneurship Page.


Product maximisation algorithm

We have attempted to develop a versatile algorithm designed to maximise the concentration of a user-defined product within a given SBML model. This algorithm empowers users to input their model, specify a kinetic parameter for variation, and define a symmetric range and resolution tailored to their requirements. The algorithm systematically adjusts the kinetic parameter values across the designated range, computing product concentrations using the model's rate laws. The final product concentration, after a specified time interval (defaulted to 15 minutes but adjustable), is stored and graphically presented, effectively optimising the selected parameter to maximise product concentration.

Our overarching objective in developing this algorithm was to reduce the production cost of palmitate under constant initial conditions. By maximising palmitate concentration, we aim to perform a refined cost analysis compared to the original estimate, aiming to address one of the significant challenges hindering the widespread adoption of biofuels—their comparatively high production costs. While our approach is theoretical, it lays the foundation for future experimental optimisation strategies, including media and resource optimisation, to position biofuels as a competitive and sustainable alternative in the fuel industry.

Due to the time constraints of the iGEM cycle, we were unable to complete the algorithm to its fully generalised functioning. We certainly intend to complete this, as it is would serve as a crucial method to optimise our product in order to reduce the cost further. Furthermore, it would also serve as a reliable, useful resource for other users to optimise their model parameters in order to maximise a product. The complete algorithm would offer a flexible platform to users, where they can use the baseline program and modify factors such as the time interval, the product to be maximised, the parameter to be varied, and its range. With more complex modifications, users could modify our program to incorporate multiple parameters or more complex objective functions to be maximised.

More details on the logic behind the algorithm can be found in the SSoftware page in the Model section. The GitHub repository with the current version of the algorithm is also linked there, which will further be updated.