1. May 23, 2002Slide 1 Networks in Bioinformatics Lenwood S. Heath Virginia Tech Blacksburg, VA, USA heath@vt.edu I-SPAN’02 Manila, Philippines May 23, 2002

2. Slide 2 I. Some Molecular Biology II. Language of the New Biology III. Networks in Molecular Biology IV. Gene Expression and Expresso V. Stress and Response VI.Networks and Computation VII. Challenges for Bioinformatics Overview

3. May 23, 2002Slide 3 I. Some Molecular Biology The instruction set for a cell is contained in its chromosomes. Each chromosome is a long molecule called DNA. Each DNA molecule contains 100s or 1000s of genes. Each gene encodes a protein. A gene is transcribed to mRNA in the nucleus. An mRNA is translated to a protein on ribosomes.

4. May 23, 2002Slide 4 Transcription and Translation DNAmRNAProtein TranscriptionTranslation

5. May 23, 2002Slide 5 Elaborating Cellular Function DNAmRNAProtein TranscriptionTranslation Reverse Transcription Degradation Regulation Protein functions: Structure Catalyze chemical reactions Regulate transcription (Genetic Code) Thousands of Genes!

6. May 23, 2002Slide 6 Chromosomes Long molecules of DNA: 10 4 to 10 8 base pairs 26 matched pairs in humans A gene is a subsequence of a chromosome that encodes a protein. Only a fraction of the genes are in use at any time. Every gene is present in every cell.

7. May 23, 2002Slide 7 DNA Strand C (cytosine) complements G (guanine) CTCAATTGAGCG Bases A (adenine) complements T (thymine) 2’-deoxyribose (sugar) 5’ End3’ End

8. May 23, 2002Slide 8 Complementary DNA Strands Double-Stranded DNA C G TGA CTCAATTGAGCG C G C G A T A T A T A TA T A T C G C G C G GC C TTAA C G

9. May 23, 2002Slide 9 RNA Strand CUCAAUUGAGCG Bases U (uracil) replaces T (thymine) Ribose (sugar) 5’ End3’ End

10. May 23, 2002Slide 10 Transcription of DNA to mRNA C G C G C G A T A T A T A TA T A T C G C G C G TGAGC C TTAA C G CUCAAUUGAGCG mRNA Strand Template DNA Strand Coding DNA Strand Template DNA Strand

11. May 23, 2002Slide 11 Proteins and Amino Acids Protein is a large molecule that is a chain of amino acids (100 to 5000). There are 20 common amino acids (Alanine, Cysteine, …, Tyrosine) Three bases --- a codon --- suffice to encode an amino acid. There are also START and STOP codons.

12. May 23, 2002Slide 12 Genetic Code

13. May 23, 2002Slide 13 Translation to a Protein CUCAAUUGAGCG Phenylalanine ArginineHistidineAlanine mRNA Strand Nascent Polypeptide: Amino Acids Bound Together by Peptide Bonds

14. May 23, 2002Slide 14 Cell’s Fetch-Execute Cycle Stored Program: DNA, chromosomes, genes Fetch/Decode: RNA, ribosomes Execute Functions: Proteins --- oxygen transport, cell structures, enzymes, regulation Inputs: Nutrients, environmental signals, external proteins Outputs: Waste, response proteins, enzymes

15. May 23, 2002Slide 15 A new language has been created. Words in the language that are useful for today’s talks. Genomics Functional Genomics Microarrays Patterns in Gene Expression II. The Language of the New Biology

16. May 23, 2002Slide 16 Discovery of genetic sequences and the ordering of those sequences into individual genes; gene families; chromosomes. Identification of sequences that code for gene products/proteins; sequences that act as regulatory elements. Genomics

17. May 23, 2002Slide 17 Genome Sequencing Projects Drosophila Yeast Mouse Rat Arabidopsis Human Microbes …

18. May 23, 2002Slide 18 Drosophila Genome

19. May 23, 2002Slide 19 The biological role of individual genes. Mechanisms underlying the regulation of their expression (transcription). Regulatory interactions among them. Functional Genomics

20. May 23, 2002Slide 20 Metabolic Pathways: series of connected chemical reactions within a cell Signal Transduction Pathways: transfer signals from outside to inside the cell Transport Mechanisms: movement of substances across biological membranes and within the cell III. Networks in Molecular Biology

21. May 23, 2002Slide 21 Glycolytic Pathway, Citric Acid Cycle, and Related Metabolic Processes

22. May 23, 2002Slide 22 Responses to Environmental Signals

23. May 23, 2002Slide 23 Carbon Metabolism

24. May 23, 2002Slide 24 Gene Transcription into Messenger RNA Gene Regulation Microarray Technology: Taking a Snapshot of Gene Expression IV. Gene Expression and Expresso

25. May 23, 2002Slide 25 Production of Messenger RNA DNAmRNAProtein TranscriptionTranslation Reverse Transcription Degradation Regulation (Genetic Code)

26. May 23, 2002Slide 26 Only certain genes are “turned on” at any particular time. When a gene is transcribed (copied to mRNA), it is said to be expressed. The total mRNA population of a cell can be isolated. The relative proportion of individual mRNAs within the total RNA give a snapshot of the genes currently being expressed. Correlating gene expression patterns with experimental conditions gives insights into the dynamic functioning of the cell. Gene Expression

27. May 23, 2002Slide 27 Microarray Technology In the past, gene expression and gene interactions were examined known gene by known gene, process by process. With microarray technology: –Simultaneous examination of large groups of genes and associated interactions –Possible discovery of new cellular mechanisms involving gene expression

28. May 23, 2002Slide 28 Flow of a Microarray Experiment Hypotheses Select cDNAs PCR Test of Hypotheses Extract RNA Replication and Randomization Reverse Transcription and Fluorescent Labeling Robotic Printing HybridizationIdentify SpotsIntensitiesStatisticsClusteringData Mining, ILP

29. May 23, 2002Slide 29 Spots: (Sequences affixed to slide) 123 1 1 2 2 1 3 12 2 3 3 3 TreatmentControl Mix 123 Excitation Emission Detection Relative Abundance Detection Hybridization

30. May 23, 2002Slide 30 Gene Expression Varies Pseudocoloring of the combined images illustrates the Cy5 to Cy3 intensity ratios and differential gene expression.

31. May 23, 2002Slide 31 Expresso System for Microarray Experiment Design, Management, and Data Analysis

32. May 23, 2002Slide 32 Boris Chevone (VT-PPWS) Ron Sederoff (NCSU) Dawei Chen (CS) Ruth Grene (VT-PPWS) Lenny Heath (VT-CS) Naren Ramakrishnan (VT-CS) Keying Ye (VT-STAT) Len van Zyl (NCSU) Carol Loopstra (Texas A & M) Jonathan Watkinson (VT-PPWS) Margaret Ellis (CS) Logan Hanks (CS) Senior Collaborators Students: VT Cecilia Vasquez (PPWS) PostDocs Allan Sioson (CS) Layne Watson (VT-CS) Jennifer Weller (VBI) Maulik Shukla (CS)

33. May 23, 2002Slide 33 Microarray Experiment Flow

34. May 23, 2002Slide 34 Biotic and Abiotic Stress: pathogens, insects, drought, heat, cold, salt, toxins Stress Response and Defense Stress Signal Transduction and Downstream Events V. Stress and Response

35. May 23, 2002Slide 35 Responses to Environmental Signals

36. May 23, 2002Slide 36 ROS Response

37. May 23, 2002Slide 37 Network of Munnik and Meijer

38. May 23, 2002Slide 38 Network of Shinozaki and Yamaguchi-Shinozaki

39. May 23, 2002Slide 39 VI. Networks and Computation Issues Approaches

40. May 23, 2002Slide 40 Mathematical Model(s) for Biological Networks Representation: What biological entities and parameters to represent and at what level of granularity? Operations and Computations: What manipulations and transformations are supported? Presentation: How can biologists visualize and explore networks? Issues for Biological Networks

41. May 23, 2002Slide 41 Partial differential equations Boolean networks Bayesian networks Logic programs Neural networks Petri nets Fuzzy cognitive maps Weak or none (ad hoc) Mathematical Models for Biological Networks

42. May 23, 2002Slide 42 Textual or diagrammatic from the biology literature A graph of some kind: - Undirected, directed, or mixed - Simple or multigraph or hypergraph - Nodes and edges labeled with biological information Representation of Biological Networks

43. May 23, 2002Slide 43 Chemical Reaction Molecules: proteins (enzymes and others), DNA, RNA, organic molecules, water, etc. Cellular components: membranes, chromosomes, nucleus, ribosomes, etc. Processes: metabolism, environmental sensing Environmental Condition Time or Stage What a Node Might Represent

44. May 23, 2002Slide 44 Transformation in a Chemical Reaction: Substrate to product Catalytic Relationship: Enzyme to substrate or reaction Protein/Protein Interaction Signal Transduction Regulation of Transcription Regulation of Translation Activation and Deactivation What an Edge Might Represent

45. May 23, 2002Slide 45 VII. Challenges for Bioinformatics Representing uncertainty or missing data Reconciling multiple networks or introducing new biological data into an existing network Deriving conclusions and hypotheses from networks Network visualization and exploration

46. May 23, 2002Slide 46 Missing biological data is a fact of life As a consequence, a network can be lacking in some details, biologically wrong, or even self- contradictory Ability to reason computationally with uncertainty and with probabilities is essential Uncertainty can suggest hypotheses that can be tested experimentally to refine a network Uncertainty in Networks

47. May 23, 2002Slide 47 Reconciling Networks

48. May 23, 2002Slide 48 Nodes and edges have flexible semantics to represent: - Time - Uncertainty - Cellular decision making; process regulation - Cell topology and compartmentalization - Rate constants, etc. Hierarchical Multimodal Networks

49. May 23, 2002Slide 49 Create Refine and extend Check for consistency Combine: union, intersection, incorporation Reconcile Evaluate Simulate Operations on a Database of Multimodal Networks

50. May 23, 2002Slide 50 Help biologists find new biological knowledge Visualize and explore Generating hypotheses and experiments Predict regulatory phenomena Predict responses to stress Incorporate into Expresso as part of closing the loop Using Multimodal Networks