1. Complete construction of orthologous and paralogous groups of genes

  2. Structure determination of large macromolecular assemblies/complexes

  3. Dynamical simulation of realistic oligomeric systems

  4. Rapid structural/topological clustering of proteins

  5. Prediction of unknown molecular structures; protein folding

  6. Computer simulation of membrane structure and dynamic function

  7. Simulation of genetic networks and the sensitivity of these pathways to component stoichiometry and kinetics

  8. Integration of observations across scales of vastly different dimensions and organization to yield realistic environmental models for basic biology and societal needs

B.5 TECHNOLOGIES FOR BIOLOGICAL COMPUTER-AIDED DESIGN (Masaru Tomita)5

  1. Enzyme engineering: to refine enzymes and to analyze kinetic parameters in vitro

  2. Metabolic engineering: to analyze flux rates in vivo

  3. Analytical chemistry: to determine and analyze the quantity of metabolites efficiently

  4. Genetic engineering: to cut and paste genes on demand, for modifying metabolic pathways

  5. Simulation science: to efficiently and accurately simulate a large number of reactions

  6. Knowledge engineering: to construct, edit and maintain large metabolic knowledge bases

  7. Mathematical engineering: to estimate and tune unknown parameters

B.6 TOP BIOINFORMATICS CHALLENGES (Chris Burge et al.)6

  1. Precise, predictive model of transcription initiation and termination: ability to predict where and when transcription will occur in a genome

  2. Precise, predictive model of RNA splicing/alternative splicing: ability to predict the splicing pattern of any primary transcript

  3. Precise, quantitative models of signal transduction pathways:ability to predict cellular response to external stimuli

  4. Determining effective protein-DNA, protein-RNA and protein-protein recognition codes

  5. Accurate ab initio structure prediction

  6. Rational design of small molecule inhibitors of proteins

  7. Mechanistic understanding of protein evolution: understanding exactly how new protein functions evolve

  8. Mechanistic understanding of speciation: molecular details of how speciation occurs

  9. Continued development of effective gene ontologies-systematic ways to describe the functions of any gene or protein

  10. (Infrastructure and education challenge)

  11. Education: development of appropriate bioinformatics curricula for secondary, undergraduate, and graduate education

B.7 EMERGING FIELDS IN BIOINFORMATICS (Patricia Babbitt)7

  1. Data storage and retrieval, database structures, annotation

  2. Analysis of genomic/proteomic/other high-throughput information

5  

M. Tomita, “Towards Computer Aided Design (CAD) of Useful Microorganisms,” Bioinformatics 17(12):1091-1092, 2001.

6  

C. Burge, “Bioinformaticists Will Be Busy Bees,” Genome Technology, No. 17, January, 2002. Available (by free subscription) at http://www.genome-technology.com/articles/view-article.asp?Article=20021023161457.

7  

P. Babbitt et al., “A Very Very Very Short Introduction to Protein Bioinformatics,” August 22-23, 2002, University of California, San Francisco, available at http://baygenomics.ucsf.edu/education/workshop1/lectures/w1.print2.pdf.



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