Introduction to Computational Systems Biology and Bio-Medicine Laboratory

1. Computational Biology Laboratory Chuan Yi Tang CS Department, NTHU [email_address]

4. Coregulated genes Gene 1 Gene 2 Gene 3 Transcription factor atgaccgggatactgattaat a caa g gt tgggtataatggagtacgataa attgaga t caa t gt acggcgggtgctctcccgattggaag a caa c gt ggg gcaatcgggatc a caa c gt agaattggatgtcaaaataatggagtggcac gtcaatcgaaaaaacggtggtgagc g caa a gt aaagggattggaccgctt S1 S2 S3 S4

5. SP 1 5 0 0 0 9 0 0 0 g 4 9 9 6 0 9 9 4 c 0 0 0 3 0 0 0 5 t 0 0 0 0 0 0 0 0 a 8 7 6 5 4 3 2 1

6. IUPAC code Sp1 binding site Y CCG Y CC S

9. e.g. l =3, d =1 k =4 W ij = ATA All possible set of degenerate positions : {P1, p2,p3} _ TA, A _ A, AT _ For each possible set X = { p 1, …, pd } of degenerate positions, all Wpq with V ( Wij , Wpq )  X are collected. K=4 K=5 K=2 _TA ATA (S1) CTA (S2) ATA (S3) CTA (S3) TTA (S4) A_A ATA (S1) ATA (S2) ATA (S3) ACA (S4) AAA (S4) ACA (S5) AT_ ATC (S2) ATT (S3) ATA (S3) ATA(S3) AAA(S3)

10. Background letter probabilities are P A = 0.22, P T = 0.22 P C = 0.28, and P G = 0.28. A negative ( p , q )-entry means that the letter p at position q is weakly conserved in G ( Wij | X ). L pq = log[(observed probability of p at position q in G ( W ij | X )) / P p ] Pseudo occurrence elimination

11. Motif scoring methods s 1 = (  Lij / pj ) / l , This fact is used to measure the conservation and the significance of each reported motif. (1.51+1.51+1.51+1.51+(0.31+0.31)/2+1.51+(0.31+0.82)/2)

12. The measure used for comparison is the performance coefficient | K  P | / | K  P |. (Pevzner P. A. and Sze, S. H. (2000) Combinatorial approaches to finding subtle signals in DNA sequences. Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology (ISMB 2000), 269-278.) K is the set of positions of the known motif occurrences in the input sequences. P is the set of predicted positions. The best performance coefficients among the top ten motifs found by these tools are compared. Evaluation of performance on synthetic data atgaccgggatactgattaat a caa g gt tgggtataatggagtacgataa attgaga t caa t gt acggcgggtgctctcccgattggaag a caa c gt ggg gcaatcgggatc a caa c gt agaattggatgtcaaccaaagtggagtggcac Red words the set of positions of the known motif occurrences ( K ) the set of predicted positions ( P ) | K  P | = 21 | K  P | = 35 | K  P | / | K  P |= 21/35 = 0.6 S1 S2 S3

13. Evaluation of performance on synthetic data

14. MotifSeeker Specificity : | K  P | / | P | false positive Sensitivity : | K  P | / | K | false negative

15. The best performance coefficient among the top ten motifs selected.

20. 雞群育種 育種計劃 篩選 基因型 表現型

27. Exp (I) Fig. 1. Egg production rate of TRFCC (n=157). (A) Total egg number of all hens, (B) hens in four groups (A) (B)

28. Fig. 3. Association of relative protein levels with total egg number. (A) Vitellogenin (B) Apo A-I

29. (C) Ovotransferrin (D) X protein

32. Fig. 1. Egg production rate of batch A (n=77) and batch B (n=78) of TRFCC.

34. Code-selection Step 1: selection 20% of low egg number of birds in batch B of TRFCC

35. Step 2: Transform codes in batch A of birds

38. 刀鋒式伺服器在尖端科學計算 領域的研發 ( 廣達產學 ) 子計畫二 : 建置叢集計算技術於理論物理及生物資訊的環境 國家實驗研究院 : 莊哲男院長 國家高速網路與計算中心 : 張西亞博士 國家理論科學研究中心 : 張圖南主任 清華大學資訊工程學系 : 唐傳義教授

45. 核醫影像銀行的病史探勘及其在癌症診斷上的應用 唐傳義 閻紫宸 ( 長庚核醫科主任 ) 王速貞 (FDA USA)

49. Genome-wide Interpretation: Informatics of Immune Responses -The Concept of Immunometer 林口長庚紀念醫院 內科部 感染醫學科 黃景泰醫師 Ching-Tai Huang, M.D., Ph.D. Infectious Diseases, Medicine Chang Gung Memorial Hospital

50. 自體抗原 腫瘤 傳染性微生物 環境抗原 Immune Tolerance & Immune Activation - Balance between Physiology & Pathology Tolerance Activation 移植器官

51. Transgenic Mouse Model -Adoptive Transfer System Recipients HA expressing Transgenic Mice Pooled splenocytes & lymph node cells C3-HA Low Donors HA specific TCR Transgenic Mice a) CD4 + : 6.5 (I-E d HA 110-120 ) b) CD8 + : clone 4 (K d HA 518-526 ) C3-HA High Non-Tg

52. Immune Tolerance & Immune Activation -in CD4+ T Cells Tolerance Memory Anergic/Regulatory Activated/Memory Naive

53. Immune Tolerance & Immune Activation -Dynamic genomic approach (With Affymetrix Gene Chips) Day 2 Day 3 Day 4 Naive Tolerance Memory Anergic/Regulatory Activated/Memory RNA RNA RNA RNA RNA RNA RNA

56. NF- κ B pathway model

57. NFκB IKK NF- κ B

58. NFKB ICAM1