Systems biology - Bioinformatics on complete biological systems

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This document discusses systems biology and bioinformatics. It describes how systems biology takes a holistic approach to study complete biological systems and all of their components and interactions. In contrast, earlier approaches in biology focused on studying one gene or protein at a time. The document outlines several key subfields and approaches within systems biology, including mathematical modeling of biological networks and pathways, data integration from various sources, and the use of association networks to predict functional relationships between biomolecules. It provides examples of publicly available databases like STRING and STITCH that compile interaction and association data from multiple sources for large numbers of organisms. The challenges of data integration are also discussed due to issues like incompatible identifiers and variable data quality across sources. The document then focuses on

Lars Juhl Jensen
Systems biology
Bioinformatics on complete biological
systems

Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011

Exercise 1
Go to http://string-db.org
Query for CDC28 in budding yeast
Try different evidence views
Show only high-confidence links
Show only experimental evidence

Korbel et al., Nature Biotechnology, 2004

Beyer et al., Nature Reviews Genetics, 2007

Letunic & Bork, Trends in Biochemical Sciences, 2008

Exercise 2
Go to http://stitch-db.org
Query for TYMS in human
What is the role of thymidylate?
What is the role of dUMP
What is the role of Permetrexed

von Mering et al., Nucleic Acids Research, 2005

Exercise 3
Go to http://diseases.jensenlab.org
Find TYMS disease associations
nspect the text-mining evidence
Find genes linked to colorectal cance
Explore the gene network

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
O’Donoghue et al., Journal of Web Semantics, 2010

I elaborated these slides for an introductory class on Network Medicine given at UPV (Valencia) in October 2017. The fundamental principle behind Network Medicine is that disease phenotypes emerge from genotypes via the network properties of interactions between the underlying biological components. These phenotypes are best conceptualized as consequences of perturbations to disease modules of the biological networks in the cell, whether at the node level (disease genes) or the link level (disease edgotypes). With the further analysis of drug-disease association and drug-target association data, one can investigate the effects - therapeutic and undesired - of the associated medication. Understanding the molecular level networks allows to understand the connections between different diseases and the effects of drugs designed to target them, paving the way for personalized treatments based on one's own interactome.

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“Organisms function in an integrated manner-our senses, our muscles, our metabolism and our minds work together seamlessly. But biologists have historically studied organisms part by part and celebrated the modern ability to study them molecule by molecule, gene by gene. Systems biology is critical science of future that seeks to understand the integration of the pieces to form biological systems” (David Baltimore, Nobel Laureate)

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In interactome, basically for interaction of proteins there is certain key elements requited, they are: Interactomics and Proteomics, Complementation groups, Modifier screens 1. Interactomics and Proteomics Field of interactomics is concerned with interactions between genes or proteins. They can be genetic interactions, in which two genes are mainly involved in the same functional pathway (leading to a particular phenotype), or physical interactions, in which there is direct physical contact between two proteins (or between protein and DNA) (Janga et al.,2008). 2. Complementation groups Using forward saturation genetics, one may recover several independent mutants with the same (or similar) phenotype (Hernández et al., 2007). There are two possibilities: a) Mutations are in the same gene b) Mutations are in different genes involved in the same pathway. Scenario (b) Can be tested genetically with a complementation test:  Cross two homozygous mutants (samples) and observe heterozygous offspring phenotypes(samples) Mutations in the same gene will not complement  offspring have mutant phenotype Mutations in different genes will complement  offspring have wild Type phenotype  Do pairwise crosses for all mutants to identify complementation groups  Typically each complementation group represents a different gene  If many mutations are recovered in the same genes, this implies saturation

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Systems biology - Bioinformatics on complete biological systems

1. Lars Juhl Jensen Systems biology Bioinformatics on complete biological systems

2. can a biologist fix a radio?

3. Lazebnik, Biochemistry, 2004

4. one gene

5. one postdoc

6. knockout phenotype

7. name the gene

8. Lazebnik, Biochemistry, 2004

9. all aspects

10. one gene

11. high-throughput biology

12. one technology

13. one lab

14. all genes

15. one aspect

16. systems biology

17. complete systems

18. all aspects

19. all genes

20. systems-level properties

21. two subfields

22. mathematical modeling

23. small systems

24. data integration

25. large systems

26. mathematical modeling

27. small systems

28. Chen, Mol. Biol. Cell, 2004

29. many equations

30. Chen, Mol. Biol. Cell, 2004

31. simulation

32. Chen, Mol. Biol. Cell, 2004

33. many parameters

34. Chen, Mol. Biol. Cell, 2004

35. equires detailed knowledge

36. network biology

37. association networks

38. guilt by association

39.

40. protein networks

41. STRING

42. >1100 organisms

43. ~2.6 million proteins

44. Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011

45. Exercise 1 Go to http://string-db.org Query for CDC28 in budding yeast Try different evidence views Show only high-confidence links Show only experimental evidence

46. genomic context

47. gene fusion

48. Korbel et al., Nature Biotechnology, 2004

49. operons

50. Korbel et al., Nature Biotechnology, 2004

51. bidirectional promoters

52. Korbel et al., Nature Biotechnology, 2004

53. phylogenetic profiles

54. Korbel et al., Nature Biotechnology, 2004

55. a real example

56.

57.

58.

59. Cell Cellulosomes Cellulose

60. experimental data

61. gene coexpression

62.

63. protein interactions

64. Jensen & Bork, Science, 2008

65. genetic interactions

66. Beyer et al., Nature Reviews Genetics, 2007

67. curated knowledge

68. complexes

69. pathways

70. Letunic & Bork, Trends in Biochemical Sciences, 2008

71. chemical networks

72. STITCH

73. STRING + 300k chemicals

74. drugs

75. metabolites

76. known drug targets

77. high-throughput assays

78. metabolic pathways

79.

80. Exercise 2 Go to http://stitch-db.org Query for TYMS in human What is the role of thymidylate? What is the role of dUMP What is the role of Permetrexed

81. many databases

82. different formats

83. different identifiers

84. variable quality

85. not comparable

86. hard work

87. quality scores

88. von Mering et al., Nucleic Acids Research, 2005

89. calibrate vs. gold standard

90. missing most of the data

91. text mining

92. >10 km

93. too much to read

94. computer

95. as smart as a dog

96. teach it specific tricks

97.

98.

99. named entity recognition

100. comprehensive lexicon

101. cyclin dependent kinase 1

102. CDK1

103. CDC2

104. flexible matching

105. spaces and hyphens

106. cyclin dependent kinase 1

107. cyclin-dependent kinase 1

108. orthographic variation

109. CDC2

110. hCdc2

111. “black list”

112. SDS

113. information extraction

114. count co-mentioning

115. within documents

116. within paragraphs

117. within sentences

118. scoring scheme

119.

120.

121. proteins

122. small molecules

123. compartments

124. tissues

125. phenotypes

126. diseases

127. adverse drug reactions

128. organisms

129. environments

130. Exercise 3 Go to http://diseases.jensenlab.org Find TYMS disease associations nspect the text-mining evidence Find genes linked to colorectal cance Explore the gene network

131. text corpus

132. ~22 million abstracts

133. no access

134.

135. ~4 million full-text articles

136. augmented browsing

137. Reflect

138. browser add-on

139. real-time text mining

140. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009 O’Donoghue et al., Journal of Web Semantics, 2010

141. localization and disease

142. suite of web resources