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The Bioinformatics and Computational Biology Program at Iowa State University consists of a diverse group of highly interactive scientists who are enthusiastic about the challenges and opportunities presented by modern biology. These scientists have expertise in six interdependent research areas:
- Bioinformatics - encompasses aspects each of the other three disciplines and provides the information science techniques necessary to integrate data from research in genomics, molecular evolution and macromolecular structure/function relationships to provide understanding of biological systems at new levels of complexity.
- Functional and Structural Genomics - including ISU faculty involved in Genomics initiatives with plants, animals and microbes. These scientists, along with their colleagues and collaborators from around the world, are producing the prodigious quantities of sequence and expression data that have opened up new avenues of biological inquiry.
- Genome Evolution - including ISU faculty with expertise in molecular evolution with emphasis on whole genome analyses and on understanding patterns and processes of change that occur among genes and genomes over time.
- Macromolecular Structure and Function - including ISU faculty whose research aims are to discern structural and functional meaning from DNA, RNA and protein sequences.
- Computational Biology
- Metabolic and Developmental Networking
- Mathematical Biology and Biological Statistics
The focus of the BCB major is computational molecular biology. The scope of the major includes research that addresses problems related to storage, retrieval and analysis of information about molecular sequence, structure, function, and evolution. Some specific areas within this scope include: genomics, proteomics, functional genomics, evolutionary genomics, characterization of macromolecular structure/function relationships, prediction of macromolecular structure, design and implementation of biological data warehouses, design and optimization of algorithms for sequence/structure alignment and analysis, development of machine learning/data mining algorithms.
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