Transcript Document

Advanced Bioinformatics
Lecture 1: Introduction to system biology
ZHU FENG
[email protected]
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
Table of Content
1. An introduction
2. How to survive
3. What will be covered
4. Signaling pathway
5. Concluding remarks
2
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
4
5
Introduction to this module
Credits
32
Schedule
10 lectures (week 4th, 6th to 10th, 3 months)
Methods
English-teaching & Project-based (3 projects)
1. Biological pathway simulation
2. Computer-aided anti-cancer drug design
3. Disease-causing mutation on drug target
6
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
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
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
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
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
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
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
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
Generic signaling pathway
Signal
Receptor
(sensor)
Transduction
Cascade
Targets
Response
Metabolic
Enzyme
Gene Regulator
Cytoskeletal Protein
Altered
Metabolism
Altered Gene
Expression
Altered Cell
Shape or Motility
15
Integrated
circuit of the cell
16
EGFRERK/MAPK
Signaling
Pathways
17
Single target drug
EGFR
MET
Cancer growth
Multi-target drug
PDGF
R
EGFR
MET
PDGF
R
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
Signaling
Synergy effect (1+1>2) on system level
Therapeutic effect
Pharmacodynamic
combination
Anti-counteractive
Complementary
Facilitating
Pharmacokinetic
combination
Potentiative
19
Cisplatin：
DNA adduct, 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
DNA repair
Anti-anti-caner
Anti-counteractive
synergistic effect
20
Methotrexate (MTX) – 5-FU Combination
Anticancer
S. Loi. Journal of Clinical Oncology. 31(7):860-867 (2013)
Complex
can
enhance the
interaction
between 5FU and TS
Drug-drug
interaction
Complementary
21
Up regulate
RANKL
Human T cell
exposed to gp120
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
Up regulate HIV Transcription
substrate
22
Quantitative study not qualitative
EGFR pathway net work (cancer related)
ERK’s activation dynamics will directly affect the cell proliferation and
differentiation, and push tumor genesis. Therefore, the understanding of
EGFR-ERK pathway will understand how cancer signaling is proceed and
developed.
23
1. Single protein ODE equation concentration (time)
2. Soleving the equations together
3. Sensitivity analysis, multi-targets synergy effect
24
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
Parameters
 Gene expression level for different disease
 Gene expression level for individual
 Kinetic data for protein-protein interaction
26
Is quantitative study reliable?
Model Validation 1：
EGFR L858R/T790M mutation in lung cancer significantly hamper EGFR-Cbl
interaction (Kf), therefore reduce EGFR endocytosis, and lead to the elongation of
EGFR-ERK signal in lung cancer cell.
Oncogene, 26 (2007), pp. 6968–6978
Kf
27
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
Enjoy more on the next lecture
1. Biological pathway simulation
2. Computer-aided anti-cancer drug design
3. Disease-causing mutation on drug target
Any questions? Thank you!
29