Bioinformatics and translational medicine

Transcription

1 Bioinformatics and translational medicine Ueng-Cheng Yang Institute of Biomedical Informatics National Yang-Ming University New challenge of bioinformatics: Information-driven research Knowledge Making observations Information integration & data mining Testing hypotheses Computational / systems biology The knowledge discovery spiral: The in silico approach Proposing hypotheses Knowledge management & inference engine 1

2 One possible application of bioinformatics Molecular and cellular level BioInformatics Health Informatics (molecular and (population level) cellular level) Diseases Health Imaging Informatics (tissue & organ level) Population level Clinical Informatics (individual level) Tissue and organ level Individual level Revised from Der-Ming Liou s slide Cholesterol metabolism and cardiovascular disease Picture taken from sample/ch0902-f4.htm Picture taken from sbinarede/sbinarede31/ 2

3 The rate-limiting step of a clinical research or trial Recruiting the patient & interpreting the data Translational medicine Translate the success in genomic medicine into clinical applications Evidence-based medicine => develop rationale and theory for drug design and therapeutic intervention Develop personal medicine and preventive medicine Picture taken from 3

4 The detail mechanism of a disease relies on flux prediction and verification Genotype Gene variation Qualitative change Quantitative change Black box Protein activity (K M vs V max ) Pathway flux Phenotype Disease(s) Molecular medicine and genomic medicine normal Difference Abnormal (disease) Differential library Cause / effect relation Differential display Main control factor Microarray 4

5 Alternative splicing and maintenance of cancer state Major diseases and approaches Genetic diseases Linkage analysis, assoc. Studies, etc. disease candidate gene predication Cancers signaling pathways diagnosis by microarray data analysis Metabolic diseases metabolic pathways Infectious diseases information collection comparative bioinformatics normal TNF pathway in lung cancer 5

6 Family 3 Clinical database Challenges (I): information integration Disease / risk factor candidate region Candidate gene database Genotype SNP/haplotype RNAi ENU mouse Animal model Phenotype Drug discovery Challenges (II): tool development 6

7 Example: Integration of epidemiology data with sequence data Future: Preventive medicine and personal medicine Genotype Genome Std: bottom-up Disease gene New: top-down Variations Genome Normal gene Phenotype Chromosome Candidate gene Disease gene localization Linkage / assoc Genotyping Diseases Risk factor Specific genotype Pathway analysis Diseases Literature analysis 7

8 The end Competitive inhibitor of cholesterol biosynthesis Picture taken from 8

9 國內的生醫發展策略 基因型 - 基因體 - 蛋白質體 發現 共通的資訊平台 轉譯醫學 倫理 法律 社會問題的共識 臨床前試驗 表現型 - 健康紀錄 - 檢驗結果 臨床試驗 臺灣基因資料庫 基因體醫學計畫 國家衛生資訊基礎建設 臨床研究中心 改自林美雪博士投影片 Clinical research vs clinical trial Trial will observe the difference between treated and nontreated 9

10 How to conduct a clinical research / trial? Recruit patient screening Assign to a protocol Collect data Protocol violation? No Severe Adverse Effect (SAE)? No Data analysis Yes Yes Report to IRB Example: taxol coated stent 10

11 Use stent to improve restenosis Picture taken from castellot-lab-fig2.htm Picture taken from Instent restenosis * SMC = arterial smooth muscle cells Picture taken from /graphics/artery_images/ Instent_restenosis.gif 11

12 New hypothesis: microtubule will stop the SMC proliferation %20in%20our%20lab%20FINAL.html The above picture was taken from Genome-Wide Association Study GWAS Haplotype block Candidate region Select SNP Linkage anal. in candidate region Look for functionally significant SNP 12