Team:UChicago/Human Practices
Human Practices: Adapting to Industry and Input
Very early on in our attempt to computationally model and optimize our PET degradation pathway, we realized that there was no easily accessible software for biologists to build models and then use those models to inform the design of their system. We figured that if a small team of undergraduate researchers were running into this problem, many other biologists must face this same hurdle. After this realization we decided to make it part of our mission to make computational approaches to designing cell-free systems more accessible to biology researchers.
In developing Optizyme as an accessible software that could begin to connect wet lab experimental synthetic biology with computational design and optimization, we decided very early on that we would develop Optizyme based on feedback we received from our potential end users: biology researchers in both academia and industry.
We first met with Dr. Michael Jewett from Northwestern University, who had previously researched optimization of enzyme concentrations in cell-free systems through a wet lab approach, and he offered some important insight that allowed us to improve our computational solution. Dr. Jewett pointed out that in industry, it is typically hard to accurately control enzyme concentrations below a certain range of concentrations, so optimization of enzyme concentrations past this point is unnecessary and an unproductive use of computational power because it is not practically attainable. Based on Dr. Jewett’s feedback we were able to rewrite our algorithm to flexibly allow the user to control the amount of accuracy that they want out of the algorithm. If the algorithm begins to pass that degree of accuracy, it will automatically recognize what it is doing and terminate, avoiding unnecessary computational expense. Ultimately, our opportunity to discuss practical optimization of cell free systems with Dr. Jewett allowed us to make our algorithm more flexible and potentially faster by avoiding unnecessary steps.
The next researcher we met was Dr. Michael Köpke, VP of synthetic biology at Lanzatech, which is a biotechnology company in Illinois that works on converting carbon gas pollution into useful chemical products through fermentation in bioreactors. As a leading researcher in industry, Dr. Köpke was able to shed light on what kind of features should be included in software tools to make them the most easy to use for end users like himself. The algorithm at this point was capable of optimizing enzyme ratios for any model that the researcher could construct, but Dr. Köpke noted that even a hundred million dollar company like Lanzatech had a very small quantitative modelling team, and suggested that model construction might be a limiting step in system optimization. This feedback motivated us to construct a generalized function in Optizyme that would aid in model creation and visualization. At this point, we reflected on some additional advice we had received from Dr. Jewett: if we are designing software meant to make computational approaches more accessible to researchers, we should make the tool as easy to use as possible so that all researchers could use it. We envisioned and constructed a computer program that would 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. Now, as long as a researcher can find the kinetic constants they need, which is becoming easier and easier through enzyme databases like BRENDA, they can construct a quantitative model in Optizyme and immediately visualize the efficiency of their system, as well as optimize the concentrations of enzyme ratios.
We are very proud that we were able to implement such drastic improvements to Optizyme over the course of our project, but we believe our package can grow to encompass an even wider breadth of capabilities in the future. While the current form of Optizyme makes quantitative modeling and optimization more accessible to researchers without a deep mathematical background, it still requires the researchers to enter inputs into the functions as ordered lists (like vectors and matrices). Our end goal is to develop Optizyme to include an extremely user-friendly and convenient interface, like an app or an easy-to-use software program on the computer. We envision a drag-and-drop interface that allows the user to construct a flowchart for their enzyme system, which will allow the user to not only visualize their system easily, but will also be used as the input into the modeling and optimization software currently present. The graphic user interface (GUI) would turn Optizyme into a software that not only removes the need for a mathematical background, but also removes the need to understand coding and computational objects. Such a GUI streamlines computational modeling and optimization by taking inputs that synthetic biologists are comfortable and familiar with while handling the heavy mathematical and computational lifting behind the scenes. This is the future of Optizyme.
Check out the amazing people whose insights helped to improve Optizyme!
