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ACMP07 - High Performance Computing Derived Biological Multiplex Network Uncovers Distinct Pathways Underlying Opioid and Nicotine Addiction
Description
Leveraging High-Performance Computing (HPC) for biological network generation, key insights into the genetic and epigenetic mechanisms supporting opioid and nicotine addition have been uncovered. Using distributed network generation software on the Frontier supercomputer, the authors processed 700 single-cell RNA sequencing (scRNAseq) data sets to construct biologically robust multiplex networks. By employing an MPI task farm as a scheduling method, the network generation software computed networks in 3 real-time hours compared to the average 76 day CPU-time, using iterative Random Forest (iRF) Leave One Out Prediction (LOOP). Network layers were validated using RWR with k Fold cross validation against independently curated GO terms and clustered using MENTOR, an algorithm developed for the clustering and visualization of RWR rank order embeddings. The findings highlight transcriptional regulation via transcription factors and epigenetic mechanisms implicated in neural development. This research not only illuminates our current understanding of nicotine and opioid addiction, but also demonstrates the importance of HPC network generation and validation techniques.
Presenter(s)
Presenter
Matthew Lane is a Graduate Research Assistant in the Computational and Predictive Biology Group at Oak Ridge National Laboratory working under Dr. Dan Jacobson.His current research centers around the application of statistical, graph theoretical, machine and deep learning methods for prediction and analysis of complex biological systems using the leadership class systems Andes, Summit, and Perlmutter.Research undertakings currently include:- Using Explainable AI to create massive Predictive Expression Networks for use in Multiplex Omics models.- Employing geometric deep learning for node embedding and link prediction on large and sparse networks.- Metabolomic profile network creation through peak extraction and statistical processing of LC/GC-MS data.- Scientific Software engineering for the production of well-tested and documented packages for publication.- Topological Perturbation of networks for the phenotypic prediction of genetic modulation.Matthew Lane earned his M.S. in Computer Science under Dr. Sharlee Climer at the University of Missouri in St. Louis, working on network theory techniques for the analysis of cerebrospinal fluid metabolites in patients of Alzheimer’s disease. After his tenure at the University of Missouri, he worked as a software engineer at Bayer Crop Science, developing software for the collection, storage, and analysis of crops in the field.He actively volunteers to teach coding and robotics to local high school students and community members working with the East Tennessee STEM Hub.