Integrating -Omics

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Integrating -Omics

• Brent D. Foy, Ph.D.
• Associate Professor
• Department of Physics
• Wright State University
• Dayton, OH

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Overview

• Combining Genomic Data with Proteomic Data
• Which gene makes which protein?
• If mRNA level goes up, does the protein level go
  up?
• Biomolecular Network Modeling
• Issues
• State of the Field
• Our work

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Gene to Protein Identification
Partial table from Affymetrix rat gene tox chip
The J02722 is the GenBank nucleotide ID for
this gene.
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Gene to Protein Identification

• A Search for J02722 on GenBank
  (http//www.ncbi.nlm.nih.gov/Genbank/) or EBI
  (http//www.ebi.ac.uk/cgi-bin/emblfetch) brings
  up gene information page.
• Scroll down for protein id. GenBank gives link
  for AA41346.1. EMBL gives links for EPD
  EP31003 and Swiss-Prot P06762. Clicking on
  link takes to information page on protein.
• Match up Affymetrix gene id with protein id
  provided by proteomics experiment.
• Can do reverse, given protein id, find gene id.

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Gene to Protein Identification

• Since we have 150 identified proteins from
  proteomics, and 1000 genes on Affymetrix gene
  chip, we did the reverse approach (given protein,
  find mRNA), and found 21 genes corresponding to
  16 proteins that were present in both.
• Discrepancy?
• AFFY and GenBank M25157 Rat Cu, Zn
  superoxide dismutase, from Sprague Dawley, lung
  cell line, 601 base pairs
• AFFY and GenBank Y00404 - Rat mRNA for
  copper-zinc-containing superoxide dismutase, from
  Sprague Dawley, liver, 650 base pairs
• Errors in public databases, or just incomplete
  knowledge of mRNA or protein varieties

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Change in mRNA Expression vs Change in Protein
Expression
Ratio of expression in absence of galactose to
expression in presence of galactose
Ideker T, et al., Science, 292 929-934, 2001.
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mRNA Expression vs. Protein Level
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Time Course mRNA and Protein Levels
50 mM Hydrazine-exposed Hepatocytes
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Biomolecular Network Modeling
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Metabolic Network Modeling -Tracer studies

• Quantify activities of biochemical pathways
• For example, C-13 NMR analysis of TCA cycle and
  gluconeogenesis in liver

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Genetic Regulation

• Genes expressed in distinct domains, precisely
  delineated by time, state of cell, and level of
  response.
• This control is exerted by regulatory elements in
  the promoter and enhancer regions of genes.
• Field still young, but some quantitative results
  are appearing.

• Feedback with other genes

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Biomolecular Network Modeling Issues

• Compared to standard modeling of kinetic
  processes, challenges include
• Stochastic reaction behavior due to random
  diffusion processes and small numbers of
  molecules
• Multiple protein-protein, protein-mRNA, etc.
  interactions
• computational efficiency, parallelized code for
  operation on multiple CPUs
• Can you separate out the model for a pathway from
  the whole cell?

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Biomolecular Network Modeling Task
gene A mRNA A prot A rxn A1 A2 gene B mRNA
B prot B rxn B1 B2 gene C mRNA C prot
C gene D mRNA D prot D

• Compounds other than genes are mobile
• Some of these mobile compounds affect many
  reactions (e.g. ATP, ions)

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Biomolecular Network Modeling Finding the
Parameters
Use the simulation itself to narrow down on the
possibilities
1. Optimize on stability
Stable regions
Parameter 2
Parameter 1
2. Optimize on something else maximum energy
efficiency rapid cell division
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Biomolecular Network Modeling - State of the
Field

• E-Cell
• Virtual Cell
• Bio-Spice/Arkin
• Specific Laboratories Institute for Systems
  Biology/Leroy Hoods group
• Useful links page http//www.cds.caltech.edu/erat
  o/links.html

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E-Cell

• From Laboratory for Bioinformatics, Keio
  University, Japan
• Attempt to integrate genes, RNA, proteins, and
  metabolites of entire cell in one simulation
• Freely available, http//www.e-cell.org/

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E-Cell

• Used to simulate a minimal cell based on
  Mycoplasma genitalium
• 127 genes
• Integrate with online databases
• Many parameters estimated
• Substances modeled include small molecules,
  macromolecules, multi-protein complexes,
  protein-DNA complexes
• Multiple reaction types

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E-Cell, published results
Remove glucose from culture medium
ATP
Some mRNA levels
Time
Time
Tomita, M., et al. Bioinformatics, Volume 15,
Number 1, 72-84 (1999)
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Virtual Cell

• National Resource for Cell Analysis and Modeling
  (NRCAM), located at University of Connecticut
  Health Center
• Access via internet, http//www.nrcam.uchc.edu/
• Has a graphical, biological users interface
• Compared to E-Cell
• Includes 3-d spatial information within cell
• Has not been applied to gene-gtmRNA-gtprotein-gtmetab
  olites

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Virtual Cell
Define physiology, with reactions among substances
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Virtual Cell
Geometric results
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Bio-Spice

• Initiated at Berkeley National Laboratory,
  http//gobi.lbl.gov/aparkin/index.html
• Development of Bio-Spice is currently the subject
  of a DARPA project
• It will be a Simulation Program for Intra-Cell
  Evaluation, like SPICE for circuit design
• Intended to be a user-friendly simulation tool
  that captures the network of molecular
  interactions including gene-gene, gene-protein,
  and protein-protein interactions.

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Institute for Systems Biology - Galactose in Yeast
Ideker T, et al., Science, 292 929-934, 2001.
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ISB - physical interaction network
Circles are genes, yellow means product affects
another genes transcription, blue means proteins
interact. Grayscale of circles is mRNA change
with galactose in medium.
Ideker T, et al., Science, 292 929-934, 2001.
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Development of Quantitative Tools - Transcription
RNA Polymerase
Activated Nucleotides
TFIII
TF_A
TF_B
DNA
B A TATA
mRNA sequence
Regulatory factors
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Development of Quantitative Tools - Transcription
(cont.)
State of Promoter kon for RNA Polymerase TATA A
B off any any 1e-99 (Mms)-1 on off off 1e-30 on
on off 5e-23 on off on 1e-99 on on on 5e-23
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Development of Quantitative Tools - Transcription
(cont.)
Gene A
B A TATA
product TF_A
Gene B
A TATA
product TF_B
Plus a first-order process for degradation of
TF_A and TF_B
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Development of Quantitative Tools - Transcription
(cont.)
Time course of binding to gene A promoter
Time course of number of TF_A
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Biomolecular Network Modeling - Future Tasks

• Ultimate goal is to provide physiological insight
  on integrated genomic, proteomic, metabolic data
  sets in response to toxicity interventions

• Establish contact with online databases
• Gene-gtprotein-gtmetabolite connections (KEGG,
  others)
• protein-protein interactions (published list,
  Nature Biotech)
• protein-DNA interactions (TRANSFAC, SCPD)
• Evaluate proper scale of modeling effort relevant
  to task. Scale in both the level of biological
  detail, and in terms of man-hours.
• Choose software and gain expertise with it, or
  create software as needed.
• One early goal - explore minimal cell and its
  stability in response to perturbation

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Collaborators
AFIT Dr. Dennis Quinn 2Lt Matt Campbell WSU Dr.
Tatiana Karpinets
AFRL Dr. John Frazier Dr. Charles Wang Dr. Victor
Chan AFOSR Dr. Walt Kozumbo
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Questions?
Integrating -omics