Clemson University researchers are helping lay the groundwork for computer simulations that could eventually be used to match cancer patients with the medicine that will help them get well.
A team of 11 researchers that included six from Clemson recently reported on its work in the journal Nature Communications. The research is a step forward for personalized medicine, helping raise hopes that clinicians will one day be able to plug patients’ data into a computer model to find the best possible medicine for each individual.
The paper’s authors built on previous work to develop a new way of creating and altering mechanistic models that bring together large datasets with minimal computer coding
Cemal Erdem, a postdoctoral fellow in Clemson’s Department of Chemical and Biomolecular Engineering, said the paper will be of most interest to other researchers, especially those at pharmaceutical companies and those studying computational modeling or signaling networks.
“The impact of this paper is that we are trying to make it much, much easier to create these types of models,” he said. “We have this open-source tool now, with the code available on the internet. Researchers can take this code, create their own models and run simulations on their desktop computers or supercomputers.”
That code can be accessed here.
Marc Birtwistle, an associate professor of chemical and biomolecular engineering at Clemson, said his overarching goal in building models is to match drugs to patients. He said that his work is aimed at helping medical researchers answer design questions, similar to how an airplane manufacturer would run computer models of airplane designs before building an actual airplane.
“Medicine and pharma don’t have those types of design tools because they don’t exist yet,” Birtwistle said. “In a broad sense, that’s why this kind of work can be impactful. We’re trying to build that foundation so that those sorts of simulation models could help clinicians make decisions about patients.”
The team reported its findings in a paper titled, “A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling.”
“This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models,” researchers wrote in the abstract.
Most published mechanistic models are small in scale and limited in their abilities, and it can be a struggle to incorporate multiple datasets, researchers wrote. Large-scale models “can provide a more extensive representation of cellular interactions and are thus well-poised for data integration that complement shortcomings of machine learning approaches,” they wrote.
Birtwistle said that computational biology researchers now have a way to very easily build on the team’s work.
“The way the model is programmed and built is very simple and easy to use,” he said. “Before, it was very difficult and almost inaccessible, but now it’s accessible. I think we’re going to be able to recruit much more of a research community into building these sorts of approaches.”
Aurore Amrit, a fifth year pharmacy student at the University of Paris, was able to start running a model within days of starting her work as an exchange student in Birtwistle’s lab. Before the team developed its new system, it took a postdoctoral researcher six months to do the same work.
“I added some models for anti-cancer drugs,” Amrit said. “It’s very straightforward.”
David Bruce, chair of the Department of Chemical and Biomolecular Engineering, congratulated the team on publishing its work.
“This collaborative, multidisciplinary research helps keep Clemson at the forefront of health innovation,” Bruce said. “Thanks to the team’s work, the research community is better positioned to answer one of medicine’s most challenging questions– how to treat cancer as painlessly and effectively as possible.”
Corresponding authors on the paper were Erdem and Birtwistle. Co-authors from Clemson also included: Arnab Mutsuddy, a Ph.D. student in the Department of Chemical and Biomolecular Engineering; Ethan M. Bensman, who was an undergraduate in the School of Computing at the time of the research; William B. Dodd, an undergraduate in Clemson’s Department of Chemical and Biomolecular Engineering; and Alex Feltus, a professor in the Department of Genetics and Biochemistry.
Co-authors from other institutions were: Michael M. Saint-Antoine of the Center for Bioinformatics and Computational Biology at the University of Delaware; Mehdi Bouhaddou, of the Department of Cellular and Molecular Pharmacology at the University of California, San Francisco; Robert C. Blake of the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory; and Sean M. Gross and Laura M. Heiser of the Department of Biomedical Engineering at Oregon Health & Science University.
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