Advanced Bioinformatics Lecture 1: Introduction to system biology

1. Advanced Bioinformatics Lecture 1: Introduction to system biology ZHU FENG zhufeng@cqu.edu.cn http://idrb.cqu.edu.cn/ Innovative Drug Research Centre in CQU 创新药物研究与生物信息学实验室

2. Table of Content An introduction How to survive What will be covered Signaling pathway Concluding remarks 2

3. Lecture: ZHU FENG Major: Bioinformatics and computer-aided drug design 2006-2013: System biology-based drug discovery 1999-2013: Computational simulation on biological system Please visit http://idrb.cqu.edu.cn/ to download the teaching material 3

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7. Whether will you pass the exam? • It depends! • But tell you the way to survive • One long presentation (40%, team work) • The organization; Team-work spirit; Achievement …… • One short presentation (20%, personal) • Clearance; The organization …… • One project report (40%, individual effort) • My observation (1. actively involved in every course; 2. come to this module on time; 3. creativity; 4. do not just listen, get familiar with the biological side of the topic; 5.good relationship with me ……) • Your ways of putting what you have done in English 7

8. What will be covered? Guaranteed! To learn the most-widely used bioinformatics tools • Basic understanding of the method in each tool (Normally required in a college module) • Capable of explaining the algorithm to a layperson (so that you are perceived as an expert!) • Knowing the application range and limitation of each tool (now the real expert!) To learn through project, focused on application and problem solving • Study of real and recently-emerged biological problems in system biology: 1. pathways simulation; 2. drug design; 3. drug target mutation (give you the experience to work for a life-science lab or a pharmaceutical company). 8

9. Lab and Text Book “Open-lab” policy: • Our lab assignments only uses internet tools and downloadable software (which means that you can do the projects “any-time, any-place”) • No need to show-up in the lab, as long as you submit lab-report on time. • Project-report submission system at: http://idrb.cqu.edu.cn/ Textbook: • As most of the topics are not covered by existing textbooks, you are not required to have a textbook. Recommended reference books: • Introduction to Bioinformatics. Arthur M. Lesk. 2002. Oxford University Press; ISBN: 0199251967 • Bioinformatics: The Machine Learning Approach (Adaptive Computation and Machine Learning). Pierre Baldi, Soren Brunak. 2001. The MIT Press; ISBN: 026202506X • Molecular modelling : principles and applications. Andrew R. Leach. Imprint Harlow, England • Most importantly: literature from PubMed (http://www.ncbi.nlm.nih.gov/pubmed) 9

10. Topics covered Lecture 1: Introduction to system biology (week 4th) • An introduction • How to survive • What will be covered • Signaling pathway • Concluding remarks Lecture 2: Cancer pathways and therapeutics (week 6th) • The nature of cancer • How cancer arises • Pathway involved in cancer • Cell cycle clock and cancer • Molecular target of cancer 10

11. Topics covered • Lecture 3: Protein-protein interaction (week 7th) • Protein-protein interaction • Interaction representations • Method A: Two-hybrid assay • Method B: Affinity purification • Spoke and matrix models of PPI • Lecture 4: Short presentation A (week 8th) • Opening remarks • G1: Student 11; Student 12 • G2: Student 21; Student 22 • G3: Student 31; Student 32 • Concluding remarks 11

12. Topics covered • Lecture 5: Signal transduction and simulation (week 9th) • Components in signal transduction • Growth factor and receptor • RTK signal transduction • Constructing a pathway model • Signaling oncogene & therapeutics • Lecture 6: Pharmacology and drug development (week 10th) • Modern drug development • Drug & corresponding target • Mechanism of drug binding • Mechanism of drug action • Adrenoceptor cardiac function 12

13. Topics covered • Lecture 7: Computer-aided lead identification (week 11st) • Schematic of DOCKing • Pharmacophore-based docking • INVDOCK Strategy • Ligand-based drug design • Classification of drugs by SVM • Lecture 8: Short presentation B (week 12nd) • Opening remarks • G1: Student 13; Student 14 • G2: Student 23; Student 24 • G3: Student 33; Student 34 • Concluding remarks 13

14. Topics covered • Lecture 9: Drug resistant & cancerous mutation (week 13rd) • Differential drug efficacy • Pharmacogenetics • Pharmacogenetic response • Drug resistance mutation • Prediction of drug resistance • Lecture 10: Examination and presentation (week 14th) • Opening remarks • G1: Biological pathway simulation • G2: Computer-aided drug design • G3: Cancerous mutations on targets • Concluding remarks 14

15. Generic signaling pathway Signal Receptor (sensor) Transduction Cascade Targets Response Metabolic Enzyme Cytoskeletal Protein Gene Regulator Altered Metabolism Altered Gene Expression Altered Cell Shape or Motility 15

16. Integrated circuit of the cell 16

17. EGFR-ERK/MAPK Signaling Pathways 17

18. Single target drug Multi-target drug EGFR MET PDGFR EGFR MET PDGFR Cancer growth Cancer growth stop C.L. Sawyers. Nature. 449(7165):993-996 (2007) Z. Chen. Journal of Medicinal Chemistry. 54(10):3650-3660 (2011) Gleevec: “Time magazine” reported as the “magic bullet” for anti-caner, which is a typical multi-target drug for Abl, Kit, Arg, PDGFR 18

19. Signaling Synergy effect (1+1>2) on system level Anti-counteractive Complementary Facilitating Potentiative 19

20. Cisplatin： DNAadduct, DNA damage, Cancer cell apoptosis Trastuzumab： Anti-HER2 antibody Synergy effect: Pietras et al. Oncogene 1998 Le et al. J. Biol. Chem. 2005 Lee et al. Cancer Res. 2002 Anti-counteractive synergistic effect DNA repair Anti-anti-caner 20

21. Methotrexate (MTX) – 5-FU Combination Anticancer S. Loi. Journal of Clinical Oncology. 31(7):860-867 (2013) Complex can enhance the interaction between 5-FUand TS Drug-drug interaction Complementary 21

22. Human T cell exposed to gp120 Up regulate RANKL 突厥蔷薇 Rosa damascena Anti-HIV active ingradent Kaempferol AIDS-058145 Synergy Effect Fakruddin et al. Clin. Exp. Immunol. (2004) Kaempferol Direct inhibit HIV protease AIDS-058145 Inhibit HIV protease substrate Up regulate HIV Transcription 22

23. Quantitative study not qualitative EGFR pathwaynet work (cancer related) ERK’sactivation dynamics will directly affect the cellproliferation and differentiation, and pushtumor genesis. Therefore, the understanding of EGFR-ERK pathway will understand how cancer signaling is proceed and developed. 23

24. 1. Single protein ODE equationconcentration (time) 2. Soleving the equations together 3. Sensitivity analysis, multi-targets synergy effect 24

25. Examples EGF binding to EGF receptor EGF∙EGFR dimerization Reaction rate producing EGF∙EGFR Reaction rate consuming EGF∙EGFR Determine the change in the concentration of EGF∙EGFR over time 25

26. Parameters • Gene expression level for different disease • Gene expression level for individual • Kinetic data for protein-protein interaction 26

27. Is quantitative study reliable? Model Validation 1： EGFRL858R/T790M mutation in lung cancer significantly hamper EGFR-Cbl interaction (Kf), therefore reduce EGFRendocytosis, and lead to the elongation of EGFR-ERK signal in lung cancer cell. • Oncogene, 26 (2007), pp. 6968–6978 Kf 27

28. Is quantitative study reliable? Model Validation 2： The initial concentration of EGF in cancer cell line PC12 is 50ng/ml, transient activation of ERK(peaks within 5 min and decays within 30–60 min) • Nat. Cell Biol., 7 (2011), pp. 365–373 Protein phosphatase 2A (PP2A, from 0.005 to 0.01 μM) that differ by 2-folds show little effect on the change of maximal amount of active ERK but substantially affect the duration of ERK activation • Biophys. J., 87 (2009), pp. L01–L02 28

29. Any questions? Thank you! 29