CZ5225 Modeling and Simulation in Biology (Elective, 4MC)

Synopsis
Prerequisite: CZ3252
Proteins: sequence => structure =>function. Protein structural organizations and families. Basic modeling and simulation techniques. Protein structural modeling: homology modeling, threading, ab initio methods. Cell as a complex machine: Genetic and protein circuits (pathways). Development
of a mathematical model of pathways. Computer simulation of pathways.

Instructor
Dr. Chen Yu Zong
Department of Computational Science
National University of Singapore
Office: Blk S17 Room 07-24
Tel: 6874-6877. Fax: 6774-6756
E-mail: yzchen@cz3.nus.edu.sg
Web: http://www.cz3.nus.edu.sg/~yzchen (Lots of info about biocomputing)

Lectures:

• Lecture 1: Introduction
• Lecture 2: Gene Expression Profiles and Microarray Data Analysis
• Lecture 3: Clustering Analysis for Microarray Data I
• Lecture 4: Clustering Analysis for Microarray Data II
• Lecture 5: Clustering Analysis for Microarray Data III
• Lecture 6: Microarray Cancer Classification
• Lecture 7: Microarray Class Classification by Machine learning Methods
• Lecture 8: Microarray disease predictor-gene selection by feature selection methods
• Lecture 9: Next Generation Sequencing
• Lecture 10: Copy Number Variations
• Project of CZ5225
• Case study using SOFT format
• Project of CZ5225 II
• groups.xls

Archived Lectures and Projects in 2007/2009:

• Lecture 1: Introduction
• Lecture 2: Gene Expression Profiles and Microarray Data Analysis
• Lecture 3: Clustering Analysis for Microarray Data I
• Lecture 4: Clustering Analysis for Microarray Data II
• Lecture 5: Clustering Analysis for Microarray Data III
• Lecture 6: Microarray Cancer Classification
• Lecture 7: Microarray Class Classification by Machine learning Methods
• Lecture 8: Microarray disease predictor-gene selection by feature selection methods
• Lecture 9: Biological Pathways I: Metabolic Pathways
• Lecture 10: Biological Pathways II: Signaling Pathways
• Lecture 11: Biological Pathways III: Pathway Simulation

Archived Lectures and Projects in 2005/2006:

• Lectures:
  • Lecture 1: Introduction
    (Right click to save)
  • Lecture 2: Proteins
    (Right click to save)
  • Lecture 3: Sequence alignment methods
    (Right click to save)
  • Lecture 4: Practical use of sequence alignment methods and introduction of projects
    (Right click to save)
  • Lecture 5: Drugs
    (Right click to save)
  • Lecture 6: Drug resistance mutations and model developments
    (Right click to save)
  • Lecture 7: Protein structure and structural modeling
    (Right click to save)
  • Lecture 8: Protein structural prediction methods
    (Right click to save)
  • Lecture 9: Pharmacogenetics and individual variation of drug response
    (Right click to save)
  • Lecture 10: Prediction system for pharmacogenetic response to drugs
    (Right click to save)
  • Lecture 11: Other modeling and simulation methods in biology
    
• Project Assignments:
• Grade:

Archived Lectures and Projects in 2004/2005:

• Lectures:
  • Lect 1 Introduction
  • Lect 2-3 Protein Families and Family Prediction Methods
    (Right click to save)
  • Lect 4-5 Protein Structure and Structural Modeling
    (Right click to save)
  • Lect 6-8 Protein structural prediction methods
    (Right click to save)
  • Lect 9 Biological pathways and pathway simulation methods
    (Right click to save)
• Project Assignments:
  • Project 1:
    • Option 1: Protein family classification by SVM
    • Option 2: Develop a program of pair-wise sequence alignment using a simple scoring scheme.
  • Project 2 : Design of structural modeling program

References:

Protein families:

• A genomic perspective on protein families. Science 278, 631-637 (1997)
• Unification of protein families. Curr. Opin. Struct. Biol. 8, 372-379 (1998)
• Evolution of function in protein superfamilies, from a structural perspective. J. Mol. Biol. 307, 1113-1143.
• Transporters: TC-DB (Microbiol Mol Biol Rev. 64:354-411)

Protein family prediction methods:

• Multiple sequence alignments. JMB 235, 1501-153 (1994); JMB 301, 173-190.
• Neural network: ProtFun. J. Mol. Biol. 319:1257-1265, (2002)
• Support vector machines: SVMProt. Nucleic Acids Res., 31: 3692-3697 (2003)

Sequence alignment:

• Needleman-Wunsch method: JMB 48, 443-453 (1970)
• PAM: A model of evolutionary change in proteins. In: Atlas of protein sequence and structure (Natl. Biomed. Res. Found. Washington) Vol 5, Suppl 3. pp.345-358 (1978).
• Smith-Waterman method: JMB 147, 195-197 (1981)
• FASTA: Proc. Natl. Acad. Sci. USA 85, 2444 (1988)
• BLAST: JMB 215, 403-410 (1990).
• BLOSUM: Proc. Natl. Acad. Sci. USA 89, 10915-10919 (1992)
• HMM: JMB 235, 1501-153 (1994)

Support vector machines:

• C. Burges, "A tutorial on support vector machines for pattern recognition", Data Mining and Knowledge Discovery, Kluwer Academic Publishers,1998 (on-line).
• R. Duda, P. Hart, and D. Stork, Pattern Classification, John-Wiley, 2nd edition, 2001 (section 5.11, hard-copy).
• S. Gong et al. Dynamic Vision: From Images to Face Recognition, Imperial College Pres, 2001 (sections 3.6.2, 3.7.2, hard copy).

Protein structure

• SCOP  database. J. Mol. Biol. 247, 536-540

Molecular modelling:

• AMBER: J. Am. Chem. Soc. 117, 5179-5197
• CHARMM: J. Comp. Chem. 4, 187-217
• Hydrogen bond model and parameters: Nucleic Acids Res. 20, 415-419. Biophys. J. 66, 820-826
• Biomolecular simulations: Recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein nucleic acid noncovalent interactions. Annu. Rev. Biophys. Biomol. Struct. 30, 211-243 (2001).

Protein structure prediction methods:

• Protein folding theory: From lattice to all-atom models. Annu. Rev. Biomol. Struct. 30, 361-396 (2001).
• Knowledge-based protein modelling. Critical Rev. Biochem. Mol. Biol. 29, 1-68 (1994).
• Comparative protein structure modelling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 29, 291-325 (2000).
• Ab initio protein structure prediction: Progress and prospects. Annu. Rev. Biophys. Biomol. Struct. 30, 173-189.

Biological pathway databases:

• KEGG: Kyoto Encyclopedia of Genes and Genomes.
• SPAD: Signaling Pathway Database.
• CSNDB: Cell Signaling Networks Database.
• PFBP: Protein Function and Biochemical Pathways.

Pathway simulation methods:

• Dynamic biochemical reaction process analysis and pathway modification predictions. Biotechnol. Bioeng. 68, 285-297 (2000)
• A mathematical model of caspase function in apoptosis. Nature Biotech. 18, 768-774 (2000)