August 2006
Trends and Tools in Bioinformatics and Computational Biology
Rick Stevens, Associate Laboratory Director, Computing and Life Sciences – Argonne National Laboratory, Professor, Computer Science Department – The University of Chicago

What about petascale computing?

Large-scale computational methods can address fundamental biological problems:

  • The origins, function, structure, and evolutionary history of genes and genomes ⇒ large-scale sequence analysis, sequence-based phylogenic analysis

By studying the details of individual gene history and protein families, we can begin to understand the factors that influence molecular evolution, refine our strategies for building large-scale databases of protein structures, and lay the foundation for understanding the role of horizontal gene transfer in evolution.

  • The structure, function, dynamics, and evolution (SFDE) of proteins and protein complexes ⇒ large-scale molecular dynamics

Proteins are the building blocks for biological processes. Using modeling and simulation, we can begin to understand how proteins work, how they evolve to optimize their functions, how complexes are formed and function, and how we can modify proteins to alter their functions.

  • Predictive protein engineering ⇒ large-scale molecular dynamics and electronic structure

Many processes of interest to the biological community are mediated by proteins, ranging from biocatalysis of potential fuel stocks to the production of rare and unique compounds to the detoxification of organic waste products. Large-scale modeling and simulation can be used to attack the problem of rational protein design, whose solution may have long-term impact on our ability to address, in an environmentally sound manner, a wide variety of energy and environmental problems.

  • The SFDE of metabolic, regulatory, and signaling networks ⇒ graph-theoretic and network analysis methods and stochastic modeling and analysis techniques

Understanding the function of gene regulation is one of the major challenges of 21st century biology. By employing a variety of mathematical techniques coupled with large-scale computing resources, researchers are beginning to understand how to reconstruct regulatory networks, map these networks from one organism to another, and ultimately develop predictive models that will shed light on development and disease.

  • The SFDE of DNA, RNA, and translation and transcription machinery in the cell ⇒ large-scale molecular dynamics and stochastic modeling

The standard dogma of molecular biology relates the transcription of DNA to messenger RNA, which is then translated to produce proteins. This is the foundation of the information-processing operation in all living organisms. The molecular complexes that mediate these processes are some of the most complex nanomachines in existence. Via large-scale modeling and simulation of protein/RNA complexes such as the ribosome and the splisosome, we will improve our understanding of these fundamental processes of life.

  • The SFDE of membranes, protein and ion channels, cell walls, and internal and external cellular structures ⇒ large-scale molecular dynamics and mesoscale structural modeling

Membranes are the means that nature uses for partitioning biological functions and supporting complexes of proteins that are responsible for supporting the cell’s ability to interact with its neighbors and the environment. Large-scale modeling is the means by which we can understand the formation, function, and dynamics of these complex molecular structures.

  • Whole-genome scale metabolic modeling ⇒ linear-programming and optimization

With the number of completed genome sequences reaching 1,000 in the next few years, we are on the verge of a new class of biological problem; reconstructing the function of entire genomes and building models that enable the prediction of phenotypes from the genotype. With petascale modeling it will become feasible to quickly produce a whole genome scale model for a new sequenced organism and begin to understand the organism’s lifestyle prior to culturing the organism.

  • Population, community and ecosystem modeling ⇒ numerical solution of PDEs, ODEs, and SODEs

Large-scale computing is making it feasible to model ecosystems by aggregating models of individuals. With petascale computing capabilities, this technique can begin to be applied to natural environments such as soils and to artificial environments such as bioreactors, in order to understand the interactions between different types of organisms and their ability to cooperatively metabolize compounds important for carbon cycling.

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Reference this article
Stevens, R. "Trends in Cyberinfrastructure for Bioinformatics and Computational Biology," CTWatch Quarterly, Volume 2, Number 3, August 2006. http://www.ctwatch.org/quarterly/articles/2006/08/trends-in-cyberinfrastructure-for-bioinformatics-and-computational-biology/

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