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Systems biology: Bioinformatics on complete biological system

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Systems biology: Bioinformatics on complete biological system

  1. 1. Lars Juhl Jensen Systems biology Bioinformatics on complete biological systems
  2. 2. can a biologist fix a radio?
  3. 3. Lazebnik, Biochemistry, 2004
  4. 4. single gene studies
  5. 5. many experiments
  6. 6. knockout phenotype
  7. 7. Lazebnik, Biochemistry, 2004
  8. 8. everything about one gene
  9. 9. high-throughput biology
  10. 10. single technology
  11. 11. microarrays
  12. 12. one thing about every gene
  13. 13. systems biology
  14. 14. model complete systems
  15. 15. mathematical modeling
  16. 16. a simple system
  17. 17. Chen, Mol. Biol. Cell, 2004
  18. 18. simulation
  19. 19. Chen, Mol. Biol. Cell, 2004
  20. 20. many equations
  21. 21. Chen, Mol. Biol. Cell, 2004
  22. 22. many parameters
  23. 23. Chen, Mol. Biol. Cell, 2004
  24. 24. equires detailed knowledge
  25. 25. molecular networks
  26. 26. what is an interaction?
  27. 27. physical contact
  28. 28. stable interactions
  29. 29. transient interactions
  30. 30. interaction assays
  31. 31. yeast two-hybrid
  32. 32. fragment complementation
  33. 33. affinity purification
  34. 34. Jensen & Bork, Science, 2008
  35. 35. Jensen et al., Drug Discovery Today: TARGETS, 2004
  36. 36. spoke representation
  37. 37. Jensen et al., Drug Discovery Today: TARGETS, 2004
  38. 38. matrix representation
  39. 39. Jensen et al., Drug Discovery Today: TARGETS, 2004
  40. 40. interaction databases
  41. 41. BioGRID General Repository for Interaction Datasets
  42. 42. DIP Database of Interacting Proteins
  43. 43. IntAct
  44. 44. MINT Molecular Interactions Database
  45. 45. Exercise 1 Go to http://thebiogrid.org Query for human TYMS Find the interaction partners Check their sources Think of possible problems
  46. 46. possibly many errors
  47. 47. purely high-throughput
  48. 48. one assay
  49. 49. one study
  50. 50. functional associations
  51. 51. guilt by association
  52. 52. STRING
  53. 53. experimental data
  54. 54. physical interactions
  55. 55. genetic interactions
  56. 56. Beyer et al., Nature Reviews Genetics, 2007
  57. 57. gene coexpression
  58. 58. curated knowledge
  59. 59. complexes
  60. 60. pathways
  61. 61. Letunic & Bork, Trends in Biochemical Sciences, 2008
  62. 62. genomic context
  63. 63. operons
  64. 64. Korbel et al., Nature Biotechnology, 2004
  65. 65. bidirectional promoters
  66. 66. Korbel et al., Nature Biotechnology, 2004
  67. 67. gene fusion
  68. 68. Korbel et al., Nature Biotechnology, 2004
  69. 69. phylogenetic profiles
  70. 70. Korbel et al., Nature Biotechnology, 2004
  71. 71. visualization
  72. 72. Franceschini et al., Nucleic Acids Research, 2013
  73. 73. many databases
  74. 74. different formats
  75. 75. different identifiers
  76. 76. variable quality
  77. 77. not comparable
  78. 78. not same species
  79. 79. hard work
  80. 80. (students)
  81. 81. quality scores
  82. 82. von Mering et al., Nucleic Acids Research, 2005
  83. 83. calibrate vs. gold standard
  84. 84. von Mering et al., Nucleic Acids Research, 2005
  85. 85. homology-based transfer
  86. 86. Franceschini et al., Nucleic Acids Research, 2013
  87. 87. Exercise 2 Query STRING for human TYMS Show network in confidence mode Show up to 20 interaction partners Show only experimental evidence Show also low-confidence links
  88. 88. text mining
  89. 89. >10 km
  90. 90. too much to read
  91. 91. computer
  92. 92. as smart as a dog
  93. 93. teach it specific tricks
  94. 94. named entity recognition
  95. 95. comprehensive lexicon
  96. 96. cyclin dependent kinase 1
  97. 97. CDC2
  98. 98. flexible matching
  99. 99. cyclin dependent kinase 1
  100. 100. cyclin-dependent kinase 1
  101. 101. orthographic variation
  102. 102. CDC2
  103. 103. hCdc2
  104. 104. “black list”
  105. 105. SDS
  106. 106. co-mentioning
  107. 107. within documents
  108. 108. within paragraphs
  109. 109. within sentences
  110. 110. scoring scheme
  111. 111. NLP Natural Language Processing
  112. 112. grammatical analysis
  113. 113. Gene and protein names Cue words for entity recognition Verbs for relation extraction [nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1]
  114. 114. more precise
  115. 115. worse recall
  116. 116. related web resources
  117. 117. STITCH
  118. 118. STRING + 300k chemicals
  119. 119. drugs
  120. 120. metabolites
  121. 121. known drug targets
  122. 122. high-throughput screens
  123. 123. metabolic pathways
  124. 124. Exercise 3 Go to http://stitch-db.org Query for human TYMS What is the role of thymidylate? What is the role of dUMP? What is the role of Permetrexed?
  125. 125. general approach
  126. 126. suite of new resources
  127. 127. COMPARTMENTS
  128. 128. TISSUES
  129. 129. DISEASES
  130. 130. curated knowledge
  131. 131. experimental data
  132. 132. text mining
  133. 133. computational predictions
  134. 134. common identifiers
  135. 135. quality scores
  136. 136. visualization
  137. 137. compartments.jensenlab.org
  138. 138. tissues.jensenlab.org
  139. 139. thank you!

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