Team:UChicago/Description
Description * Design * Engineering PoC
Optizyme Inspiration and Description
Inspiration
The United Nations sustainable development goals lists 17 broad issues that plague humanity today, and are of the utmost importance to resolve. When brainstorming for our project, a quick glance through the UN SDGs inspired us to tailor our project towards addressing a few of these major issues. Specifically, our project aims to address the issue of microplastic pollution that poses a threat to marine life and biodiversity as well as human water and food sources. These microplastics are pieces of larger plastics found in water bottles, textiles, and other manufactured goods that are physically broken down into smaller pieces without being chemically degraded. Because of their small size, microplastics are difficult to remove from water, and are also easily consumed by marine life, and results in devastating biomagnification that can ultimately be harmful to humans.
In the past couple years, labs across the world have discovered and characterized strains of bacteria that are capable of degrading plastic polymers and using them as an energy source. Starting with the original discovery of plastic degrading bacteria in soil near a plastic recycling site in Japan, and continuing to the discovery and optimization of the specific plastic degrading enzyme, a biological solution to the plastic pollution problem appears more in reach than ever.
This year our team has used information about plastic degradation pathways, specifically polyethylene terephthalate (PET) degradation pathways, as well as other pathways found in biological metabolism, to construct a novel four enzyme pathway that converts PET into catechol, a biodegradable compound that is used in the synthesis of central nervous system drugs.
After COVID-19 forced us out of the lab and into a remote work environment, we aimed to computationally model and optimize the cell-free system we would ultimately construct. We immediately noticed the lack of modelling and optimization software available to biologists, so we set out to create our own both to address our need, as well as to make computational methods more accessible to biologists in the future. The end product of our efforts is a package in the computer language R called Optizyme.
Description
Our project this year involves 2 components:
(1) To computationally optimize our original cell-free system so that we can ultimately construct a more efficient device.
(2) To develop a readily available and easy-to-use package that would allow synthetic biologists to more readily use computational approaches in their projects.
Back in our winter quarter we designed a pathway that converts PET into catechol with the goal of expressing this pathway inside a bacteria over the summer.
We originally planned on incorporating this pathway into a water filtration system for water processing plants, so that microplastics that moved through the plant could be degraded into catechol. However, a significant amount of plastic pollution exists in the ocean and away from water processing plants, including in the well known Pacific Garbage Patch. To tackle this issue, we envision our degradation pathway constructed inside large bioreactors, and plastic debris from oceanic garbage patches are fed in as material to be transformed into catechol. Using this approach, multiple degradation pathways can be constructed inside a bioreactor to give a single bioreactor the ability to degrade many kinds of plastics into different kinds of useful end compounds. Such an approach would turn pollutants into useful commodities, alleviating environmental stress in an economically productive way.
Unfortunately, because of COVID-19 we did not have access to a lab for the summer, so our project shifted towards the computational modelling and optimization of the system we had originally planned to construct. The specific computational models and optimized solutions are presented under the “Modelling and Optimization of PET Pathway” tab.
As databases for enzyme rates and mechanisms become larger and more accurate as experimental data is added to them, we envision that computational modelling and optimization will become an increasingly important skill in the biologist’s toolkit. We believe that the workflow of engineering biological systems will be grounded in experimental determination of enzyme mechanisms and kinetic parameters or collection of this information from a database, followed by model construction, and ending with computational optimization.
Our software package has been designed around the envisioned workflow and includes both modelling and optimization capabilities. Our package can take biological inputs familiar to researchers without a heavy mathematical background (kinetic parameters, competitive inhibitors, and noncompetitive inhibitors), and transform that biological information into a quantitative model without the end user having to write a single differential equation or mathematical formula. The optimization algorithm in our package can then use this model to optimize enzyme concentrations in the designed system. Our modelling function is capable of modelling cell-free systems of any length, for both one and two substrate enzymes, and can account for both competitive and noncompetitive inhibition. If an interaction is unaccounted for by our modelling function, then the user is free to construct their own model of the system, which can also be used by the algorithm to optimize enzyme concentrations. Furthermore, the package also includes functions that allow the user to visualize the time-course of their system. The user has the option to simply receive the data the model generates if they want to use more advanced visualization techniques or simply require more flexibility in their plotting. A more technical explanation regarding the mathematical formulation of our optimization algorithm is included under the “Design” tab.
Modelling and Optimization of PET Pathway
The greatest sites of plastic pollution are the garbage patches in the world’s oceans, so the most efficient places for our cell-free system to be employed is in these pollution hotspots.To begin modelling the efficiency of our cell-free system we first did research on what amounts of microplastic the system would be employed to deal with. Isobe et al reported on the density of microplastic particles per unit volume in the ocean and the data point with the highest microplastic content was approximately one piece (<5mm diameter) per cubic meter. Approximating the particles as perfect spheres allows us to estimate the ocean microplastic density as .723 grams of microplastic per cubic meter. This is equivalent to roughly .003763 moles of PET per cubic meter, or .000003763 moles of PET per liter. This is the first scenario that we will model: the use of our system to remediate naturally occurring densities of microplastic. In all the models that we will construct, we hold enzyme concentrations constant at 1 micromolar, which is a concentration that is certainly achievable in the lab.
Our algorithm maximizes the product yield after a given amount of time. The results of our optimization for each scenario is compared to a non optimized system with a 1:1 ratio of all enzymes. The enzyme concentrations are presented above each of the time courses below. The first two time courses below are calculated with .000003763 M as the starting PET concentration.
.00000025 M: .00000025 M: .00000025 M: .00000025 M
6.655420 x 10-7 M: 1.910984 x 10-7 M: 6.258616 x 10-8 M: 8.077347 x 10-8 M
The optimized solution results in a higher yield of catechol than the non-optimized solution after 1500 seconds.
The next couple of situations that we will model is a scenario in which we have control over the inputs to our cell-free system, and can introduce PET in concentrations higher than in naturally occurring scenarios. One of the ways we can see our cell-free system implemented is in large bioreactors to process plastic from large oceanic garbage patches, and in these scenarios the concentration of plastic in the bioreactors can be controlled. For example, if plastic pollution is removed from the ocean and introduced into the bioreactor in somewhat predictable amounts, then the concentration of PET can be roughly controlled. To make the bioreactors efficient, we would like to introduce higher concentrations of PET than naturally occurs in the ocean. We do not yet know what the concentration of PET will be in our bioreactors, so we model and perform optimization on .1M, .01M, and .001M as some simple examples of what our system activity looks like at PET concentrations various order of magnitude different from each other. The two time courses presented below represent the system activity on .001M starting PET concentration.
.00000025 M: .00000025 M: .00000025 M: .00000025 M
4.292450 x 10-7 M: 3.988600 x 10-7 M: 8.658209 x 10-8 M: 8.531294 x 10-8 M
The two time courses below demonstrate system activity on .01 M starting PET concentration.
.00000025 M: .00000025 M: .00000025 M: .00000025 M
2.617812 x 10-7 M: 5.739184 x 10-7 M: 9.303208 x 10-8 M: 7.126838 x 10-8 M
The two time courses below demonstrate system activity on .1 M starting PET concentration.
.00000025 M: .00000025 M: .00000025 M: .00000025 M
9.17265910-8M: 7.60958110-7M: 1.07506410-7 M: 3.98089110-8M
In all the time courses visualized above, the optimized solution produced by our algorithm resulted in higher yields than the non-optimized solutions. An interesting trend is that the optimal solutions make more of a difference as the starting PET concentration increases. Furthermore, we notice that as starting concentration of catechol increases to .01M and .1M, the enzyme rates become more linear. This is consistent with our expectation from examining the Michaelis-Menten rate equation:
We see that if substrate concentration is significantly greater than the Michaelis constant, then the rate equation simplifies to Vmax, which results in the linear time course observed.
For different starting concentrations of catechol, the time courses have different total times attached to them, and we want to address how we determined what total time to use. In our optimization workflow, we first determined how long it would take for the non-optimized system to approach 100% yield, then we performed successive optimizations with gradually shortening lengths of time until product yield no longer reached 100%. The intervals we used were roughly decreasing total time by 25%. For example, if we began with a time of 100,000 seconds for .1M starting PET, our optimization at this time reached nearly 100% yield far before 100,000 seconds, so we reduced the total time to 80,000 seconds. We repeated this process until our optimal solution did not approach 100% yield in the total time frame, then began experimenting in that neighborhood of total times. The time courses presented have total times that were roughly determined in this manner.
When looking at all our models, it is important to note that the model we construct is using kinetic parameters from the literature, which may be determined under different conditions that we imagine our cell-free system being implemented under. As such, it is important for us to confirm or recalculate the kinetic parameters we use when we get back into the lab next year.
Works Referenced
1. Austin, H. P.; Allen, M. D.; Donohoe, B. S.; Rorrer, N. A.; Kearns, F. L.; Silveira, R. L.; Pollard, B. C.; Dominick, G.; Duman, R.; Omari, K. E.; Mykhaylyk, V.; Wagner, A.; Michener, W. E.; Amore, A.; Skaf, M. S.; Crowley, M. F.; Thorne, A. W.; Johnson, C. W.; Woodcock, H. L.; McGeehan, J. E.; Beckham, G. T. Characterization and engineering of a plastic-degrading aromatic polyesterase. https://www.pnas.org/content/115/19/E4350 (accessed Oct 27, 2020).
2. Dinger, T. F.; Chen, O.; Dittfeld, C.; Hetze, L.; Hüther, M.; Wondrak, M.; Löck, S.; Eicheler, W.; Breier, G.; Kunz-Schughart, L. A. Microenvironmentally-driven Plasticity of CD44 isoform expression determines Engraftment and Stem-like Phenotype in CRC cell lines. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7359088 (accessed Oct 27, 2020).
3. https://2018.igem.org/Team:Yale/Description (accessed Oct 27, 2020).
4. https://2019.igem.org/Team:Toronto/Description (accessed Oct 27, 2020).
5. Information on EC 4.1.1.63 - protocatechuate decarboxylase. https://www.brenda-enzymes.org/enzyme.php?ecno=4.1.1.63 (accessed Oct 27, 2020).
6. Palm, G. J.; Reisky, L.; Böttcher, D.; Müller, H.; Michels, E. A. P.; Walczak, M. C.; Berndt, L.; Weiss, M. S.; Bornscheuer, U. T.; Weber, G. Structure of the plastic-degrading Ideonella sakaiensis MHETase bound to a substrate. https://www.nature.com/articles/s41467-019-09326-3 (accessed Oct 27, 2020).
7. Sasoh, M.; Masai, E.; Ishibashi, S.; Hara, H.; Kamimura, N.; Miyauchi, K.; Fukuda, M. Characterization of the terephthalate degradation genes of Comamonas sp. strain E6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1393238/ (accessed Oct 27, 2020).
8. Spectral Database for Organic Compounds,SDBS. https://sdbs.db.aist.go.jp/sdbs/cgi-bin/direct_frame_top.cgi (accessed Oct 22, 2020).
9. Take Action for the Sustainable Development Goals – United Nations Sustainable Development. https://www.un.org/sustainabledevelopment/sustainable-development-goals/ (accessed Oct 27, 2020).