Because of the explosive growth of Big Data, data-driven research is becoming increasingly important. The goal of the South Dakota 2D BEST Center Area-3 Modeling and Computational Core is to integrate disparate data and predictive models into an information framework that will assist scientists with their research questions.

This core will focus on the development of novel, interdisciplinary approaches and data analytics to track biofilm phenotypes on 2D materials, coupled with omics analyses to discover rules of biofilm assembly and organization governed by material surface features. Working with the research teams, the core will leverage and develop big-data-driven toolkits, such as machine learning, data mining, predictive modeling, and natural language processing in the development of a discovery infrastructure.

The integration of in-silico datasets, along with preexisting related resources (e.g., 2D materials, transcriptomics, genomics, proteomics, metabolomics, methylome, phenotypic, analytic tools, education, etc.), will allow researchers, educators, and students to discover, reuse, validate, share, reproduce, and exchange knowledge related to the project’s central hypothesis.

The discovery infrastructure developed by the project’s Area-3 Modeling and Computational Core will provide convenient, quick and efficient access to related resources and is expected to enhance the research productivity of bioengineering scientists and other engineers working with biological interfaces. The proposed machine learning predictive modeling tools will potentially yield non-invasive 2D coatings based on gained understanding of biology material interactions that include the passivation of corrosive effects.

The Modeling and Computational Core infrastructure will offer a series of educational, training, and workforce development opportunities in data analytics and informatics approaches customized to material and biofilm sciences.