Washington State University has a rich history of fostering the kind of interdisciplinary science that is needed to advance the state of scientific discovery for many of the challenging biological problems of the 21st century.
Bioinformatics and computational biology projects at WSU include:
- Designing innovative algorithmic solutions for data-intensive life sciences applications. Applications include genome assembly and annotation for economically important plant crops, identification of proteins involved in bioenergy, and decoding gene regulatory and protein-protein interaction networks.
- Developing algorithms for microbiology applications including microbial evolution and phylogenetic tree reconstruction, epidemiology, vaccine development, and controlling antibiotic resistance.
- Developing scalable parallel algorithms for data-intensive biological applications using next-generation supercomputers. Target parallel architectures include massively parallel distributed and shared memory supercomputers, cloud computing platforms, and multicore hardware accelerators.
- Developing algorithms for protein and metabolite identification in complex mixtures by high-throughput mass spectrometry.
Research funding comes from the National Science Foundation, National Institutes of Health, Department of Agriculture, and Department of Energy.
Research projects and labs
Research: Algorithm development, bioinformatics, machine learning, data analysis, and mathematical modeling for microbiology applications
Research: Millimeter-Wave circuits and systems, RF beamformers signal generation circuits, wireless link for biomedical applications, power management system for energy harvesting, wireless sensor networks, CMOS power amplifiers, dynamic voltage frequency scaling for integrated system
Research: EBioinformatics/Computational biology, high-performance computing, data science, and graph and string algorithms
Research: On-chip wireless communication network, NoC-based hardware accelerators for Biocomputing, Sustainable Computing, Machine Learning inspired Three-dimensional (3D) NoC Architectures.