Applications of Bioinformatics

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Applications of Bioinformatics

• Systems Biology

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Systems Biology

• Human Genome Project leads to new view of
  biology
• No longer investigate one gene at a time
• Investigates the behavior and relationships of
  all of the elements in a particular biological
  system
• Integration/Display/Modeling/Simulation

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Discovery Science

• Complete characterization of genes and proteins
  in human and model organisms
• Information science
• High throughput perturbation and monitor of
  biological systems
• Simulation with computational methods
• In contrast to hypothesis-driven science

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Genomic Sequence

• Genes, transcription regulatory elements, motifs,
  functional domains
• Comparative genomics
• Polymorphism (SNP)
• Model organisms provide hints to decipher human
  genome

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Biological Science Full With Information

• DNA ? mRNA ? protein ? protein interactions ?
  information pathways ? information networks ?
  cells ? tissues ? organism ? populations ?
  ecologies
• Other molecules (metabolites)
• Driven by genes and their interaction with the
  environments

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Biological Information

• Multiple hierarchical levels of organization
• Processed in complex networks
• Biological networks are robust, tolerant to small
  perturbations
• Key components has profound effects offer as
  targets to understand/manipulate the system

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Yeast genetic perturbation with YAC
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Applications of YAC

• Gene knockout
• Promoter fusion
• Protein fusion
• Epitope tags

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Mammalian genetic perturbation with RNA
interference
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High-Throughput Tools

• DNA sequencing
• Microarray
• Protein Chip
• Proteomics
• With the following stages
• Proof of principle
• Creation of reliable instrument
• Development of automatic procedure

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DNA sequencing
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Microarray
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Microarray

• cDNA array
• Double strand cDNA or PCR products
• Oligonucleotide array
• More specific than cDNA array
• Possible to distinguish single nucleotide
  difference (SNP)
• Not as mature as sequencing

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High Density Protein Chip

• Dimension 1 in. x 3 in.
• More than 4000 proteins
• More than 7000 proteins in the end of the year

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Proteomics
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Information revealed in Proteomics

• Identity
• Abundance
• Processing
• Modification
• Interaction
• Localization
• Turnover rate

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Identification of proteins

• Mass spectrometry
• Quantitative
• Determine protein sequence
• Detect differentiated protein expressions in
  different cell types

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Mass Spectrometry
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Mass Spectrometry
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Cell Sorter
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Computational Databases

• Protein-protein interaction
• DIP, BIND, MIPS, MINT, IntAct, POINT
• Protein-DNA interaction
• TRANSFAC, SCPD
• Metabolic pathways
• KEGG, EcoCyc, WIT, Reactome
• Gene Expression
• GEO, GNF, NCI60, commercial
• Gene Ontology

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Protein-protein interaction
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Gene Regulatory Network
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Metabolic Pathways
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Gene Ontology

• The Gene Ontology project provides a controlled
  vocabulary to describe gene and gene product
  attributes in any organism
• Annotations
• Molecular Function
• Cellular Components
• Biological Processes

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Challenges of Databases

• Provide information other than simple entries
  (e.g. PPI with functional annotation or binding
  strength)
• Data maintenance update
• Integration with other databases

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Importance of Global Analysis

• Using gene-expression data to identify genes
  involved in cancer, development, aging, cellular
  responses
• Clustering
• Other analysis
• Regulatory elements
• Protein-protein interaction
• Phylogenetic profiles

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Importance of Computer Models

• Interactions in cell is too complex to handle by
  pen-and-paper
• With high-throughput tools, biology shifts from
  descriptive to predictive
• Computers are required to store, processing,
  assemble, and model all high-throughput data into
  networks

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Tools for Simulation

• E-cell
• Cell Illustrator
• Virtual Cell
• Standardizing efforts
• BioJake
• SBML (systems biology markup language)
• Facilitate the exchange of models

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E-Cell System

• A software to construct object models equivalent
  to a cell system or a part of the cell system
• Employing Structured Variable-Process model
  (previously called the Substance-Reactor model,
  or SRM)
• Objects
• Variables, Processes, Systems

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Cell Illustrator
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Types of Computer Models

• Chemical Kinetic Model
• Defined by concentrations of different molecular
  species in the cell
• Represented with a number of equations
• Some processes may be stochastic
• Simplified Discrete Circuit
• Network with nodes and arrows
• Nodes represent quantity or other attributes
• Directed edges represent effect of nodes on other
  nodes

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Different Mathematical Formulations

• Differential Equations
• Linear (ordinary)
• Partial
• Stochastic
• S-Systems
• Power-law formulation
• Captures complicate dynamics
• Parameter estimation is computation intensive

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Model details

• Selection of genes, gene products, and other
  molecules to be included
• Cellular compartments nucleus, golgi, or other
  organelles
• Too much details may lead to more noises
• Minimal model able to predict system properties
  (mRNA level, growth rate, etc) is sufficient

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Construct Model from Global Patterns

• Microarray gene expression patterns
  Up-regulated/down-regulated
• Gene expression profiles under different
  conditions Tumor/normal, cell cycle, drug
  treatment,
• Methods
• Bayesian Inferences
• Machine learning (clustering, classification)

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Framework for Systems Biology
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Steps in Systems Biology Frameworks

• Define all components of system
• Systematically perturb and monitor system
  components
• Refine models to reflect experimentally
  observations as close as possible
• Design and perform new perturbation experiments

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Examples

• Galactose system
• Sea urchin cis-regulatory network

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Summary

• High throughput experimental data
• High throughput perturbation
• Data integration