“There is only so much you can do with laboratory experiments, and then you need predictive modeling,” said a prominent university alumnus.
The chemical and biomolecular engineering alumnus learned to program as an undergraduate student to support his graduation thesis.
“As a doctoral student, I wrote programs that were utilized to make predictions, which were then tested in the laboratory. My interest and skills in programming continued to grow, and I began focusing on scientific software engineering as a postdoctoral researcher to create the kind of robust predictive modeling tools I needed for understanding and improving chemical and biomolecular processes,” he noted.
“In biomolecular engineering research, the challenge is understanding the mechanism and speed by which reactions are occurring in living cells. We can observe the end results in the laboratory, but what we are really trying to understand, via modeling, is how cell growth and division, temperature, and other conditions impact the changes. And that is where stochastic simulations can be used to make predictions about which biomolecules will emerge, in which way, and how to get the cells to perform a certain way in the end.”
The stochastic prediction method was originally developed to improve decision-making in the midst of many uncertainties and unknowns. The method has since been adopted for widespread use across scientific disciplines, such as accelerating the development of new materials by predicting outcomes.
He said, “During my doctoral research, I used this computational approach to explore bacterial populations to better learn how heterogeneity influences the synthesis of certain biochemical products. Not every bacterial cell does the same thing as other cells. We use predictions to determine how these differences will impact our desired outcome. If you don’t take these influencers into consideration, you may come up with a design or process that does not yield the type or amount of product you wanted. Those would be inefficient biochemical processes.”
At the research university where he now serves as a faculty member, his laboratory uses modeling to guide the development of materials called catalysts, which facilitate chemical transformations at the molecular level. For a given chemistry, such modeling requires the investigation of hundreds of simultaneous reactions, many of which have little or no bearing on the final product.
Predictive modeling helps him better understand the fundamentals of reactions on the catalytic surface, to deduce which of these reactions are relevant, followed by experimental validation. Based on that foundation, he can potentially improve the outcome of the chemical process by engineering the material in a different way or changing its elemental composition.
“In a large design space where we try to create new catalytic materials with different combinations of, say, ten elements, the number of possible outcomes can appear limitless. Rather than spend months or years trying different combinations in the laboratory, I can make a prediction for how each of those materials will perform and choose the best one. There is no sense in wasting time on the materials that will not perform the way we want!”
To explain this blend of predictions, programming, and chemical processes, the researcher leans on lessons he learned during his studies. He said he was fortunate to have an advisor who sent him to conferences early in his doctoral program, forcing him to communicate with other researchers and diverse audiences.
“Strong and clear communication comes with experience,” he said. “In addition to sending me to conferences, my advisor also spent a lot of time helping edit my papers. But what was really key was the institutional communication initiative that introduced engineers to professional communication coaches.”
“Not only did we learn how to structure our papers and become better writers, we also had individual sessions with a specialized communication coach, who was great at reading our texts and spotting weaknesses. A typical mistake for our group was writing unreferenced demonstrative pronouns. Immediately, the coach would ask us to clarify exactly what concept we were referencing. We had to go back and clarify. That was surprising to most of us; we weren’t used to writing in such a specific way.”
“The coach also taught us to break down our concepts into individual paragraphs, not pack multiple concepts into a single paragraph like I had been doing. Best of all, it was our own text that we reviewed paragraph by paragraph, sentence by sentence. Even though the coach was not a biologist or physicist by training, she had given so many workshops for engineers that she was exceptional at pointing out deficiencies.”
He uses the same approach with his own students when they give him a draft to review or set up a mock presentation before a conference. Sharing information means considering not just the information the research has produced, but also how to put that information on paper or a screen in such a way that others can understand it.
“The way we think is different than the way we read or present papers. Scientists make connections between radically different concepts. Knowledge is often a very convoluted network of concepts and relational properties from here to there, like a complex web. To take this complicated network down to a paper or slide requires the researcher to disentangle the network in their head and convey it in a linear way where one concept follows another,” he noted.
He said communicating science and engineering concepts takes practice and recommends beginning by explaining even the most basic or fundamental concepts to a non-technical audience like friends or family members. Writers and presenters should expect to revise a first draft several times.
“Each time you review a paper or a presentation, determine if the current draft flows in a logical order. Unexperienced paper writers and presenters tend to jump from one thing to another, or dive into concepts without preparing the audience,” he said.
“So reverse, review, revise until you can both inform the audience and keep them engaged. In presentations, I sometimes see people using too much text, sharing too many results and complicated diagrams. A leaner presentation with fewer points will engage the audience more fully, so keep the presentation on a level appropriate for them. It is the same for papers — consider first if you are writing for a technical journal or an engaging piece of work for dissemination to the public. Either way, introduce concepts in a way that flows and makes logical sense.”
He discovered images to be the best way to engage non-technical audiences in aspects of his own work, and because he invents new materials, he learned to create his own graphical representations.
“I enjoy making colorful illustrations of the molecular structures we work on, and several of these illustrations have been featured on journal covers. Remember to use color! Nice graphics and images that convey the concept can save hundreds of words.”