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

Protein level vs gene expression,ratio 75 mM to 0 mM, different times

gene expression, ratio 75 mM to 0 mM, t = 0

0 1 2

pro

tein

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tio

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to

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M, t

= 3

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Control caseNo hydrazine exposure

gene expression level

1 10 100 1000 10000 100000

pro

tein

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Time Course – mRNA and Protein Levels

50 mM Hydrazine-exposed Hepatocytes

Immunoglobulin Heavy Chain Binding Protein

0

5000

10000

15000

20000

25000

-5 0 5 10 15 20 25 30

Protein

mRNA

N-hydroxy-2-acetylaminofluorene; sulfotransferase

0

500

1000

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-5 0 5 10 15 20 25 30

Protein

mRNA

Heme Oxygenase (HSP32)

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-5 0 5 10 15 20 25 30

Protein

mRNA

Soluble Cytochrome b5

01000200030004000500060007000

-5 0 5 10 15 20 25 30

Protein

mRNA

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Biomolecular Network Modeling

TranscriptomeAnalysis

ProteomeAnalysis

MetabolomeAnalysis

Protein - Pia

Protein - ProteinInteractions - Pia...

Metabolic Pathways

mRNAia

ProteinModifications - Pia1

Cellular Metabolites

Genei*

rRNA

tRNA

GenomeAnalysis

Pre-mRNAi

Genome

mRNAib

snRNA

mRNAij

Sk Mk

Action Pathways

Control Pathways

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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

lactate

pyruvate acetyl-CoA

oxaloacetate

F1 F6

F2

F3

a-ketoglutarate glutamate

fumarate

F4

F5F7

glucose

F8

F9

plasmalactate

ILOL

plasmaglucose

OG

IG

plasmaglutamate

ITOT

lipid + acetate

IAOA

F10

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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.

A B A C D mRNA sequence

DNA

Regulatory factors

• 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

Parameter 1P

aram

eter

2

Stable regions

2. Optimize on something else:maximum energy efficiencyrapid 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 Hood’s group

• Useful links page: http://www.cds.caltech.edu/erato/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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Time Time

ATP Some mRNA levels

Remove glucose from culture medium

Tomita, M., et al.; Bioinformatics, Volume 15, Number 1, 72-84 (1999)

E-Cell, published results

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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->mRNA->protein->metabolites

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Define physiology, with reactionsamong substances

Virtual Cell

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Geometric results

Virtual Cell

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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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Circles are genes, yellow means product affects another gene’s transcription, blue means proteins interact. Grayscale of circles is mRNA change with galactose in medium.

ISB - physical interaction network

Ideker T, et al., Science, 292: 929-934, 2001.

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Development of Quantitative Tools - Transcription

B A TATA mRNA sequence

DNA

Regulatory factors

RNAPolymerase

TFIIITF_ATF_BActivated

Nucleotides

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Development of Quantitative Tools - Transcription (cont.)

State of Promoter kon for RNA Polymerase

TATA A Boff any any 1e-99 (M*s)-1

on off off 1e-30on on off 5e-23on off on 1e-99on on on 5e-23

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Development of Quantitative Tools - Transcription (cont.)

B A TATA product = TF_A

Gene A

A TATA product = TF_B

Gene B

Plus a first-order process for degradation of TF_A and TF_B

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Development of Quantitative Tools - Transcription (cont.)

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

x 104

0

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Time (s)

# A

mR

NA

mo

lec

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Time course of number of TF_A

POLYMERASE1696 events29.53% on

TFIII3967 events99.62% on

TF_A5 events51.45% on

TF_B1852 events45.97% on

0.5 1 1.5 2 2.5 3 3.5 4 4.5

0

0.5

1

1.5

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x 104

Time course of binding to gene A promoter

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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->protein->metabolite 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

AFRL

Dr. John Frazier

Dr. Charles Wang

Dr. Victor Chan

AFOSR

Dr. Walt Kozumbo

AFIT

Dr. Dennis Quinn

2Lt Matt Campbell

WSU

Dr. Tatiana Karpinets

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Integrating -omics

Questions?