Embracing the Post-Omics Era with Computational …orca.st.usm.edu/~zhang/seminar/CompuTox2010USMtalk-final...prioritization of data requirements and risk assessment of chemicals"

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One Team, One ERDC . . . Relevant, Ready, Responsive, Reliable

Ping GongPing Gong

Environmental Genomics and GeneticsEnvironmental Genomics and Genetics(EGG) (EGG) Team Team @ Environmental Laboratory@ Environmental Laboratory

Embracing the PostEmbracing the Post--OmicsOmics

Era with Era with Computational Biology/ToxicologyComputational Biology/Toxicology

USM Seminar 1/22/2010

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Outline

• Introduction• Bioinformatics• Knowledgebase• Reverse engineering• Machine learning• Predictive modeling• Summary

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Outline


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Omics: technological evolution

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Adaptation to evolving technologies

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U.S. EPA’s stand

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U.S. Army’s stand

Limited # of chemicals(< 50 parent compounds)

Too many organisms(millions of species)

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Computational biology/toxicologyComputational Biology: The development and application of data-

analytical and theoretical methods, mathematical

modeling and computational

simulation techniques to the study of biological, behavioral, and social systems. (NIH 2000)

Computational biology is an interdisciplinary field that applies the techniques of computer

science, applied mathematics

and statistics

to address biological

problems. (Wikipedia)

Computational Toxicology: "integration of modern computing

and information technology with molecular biology

to improve Agency prioritization of data requirements and risk assessment of chemicals" (U.S. EPA 2003)

Computational toxicology is the application of mathematical

and computer

models to predict adverse effects and to better understand the mechanism(s) through which a given chemical

causes harm. (US National Library of Medicine, NIH)

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Computational biology/toxicology

+ =Computational

Biology/Toxicology

?Nautilussupercomputer

(IBM)

+Supercomputercluster

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Outline


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BioinformaticsBioinformatics (NIH2000):

Research, development, or application of computational

tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize

such data.

Bioinformatics (Oxford Journal)Scope Guidelines:•

Genome analysis•

Sequence analysis•

Phylogenetics•

Structural bioinformatics•

Gene expression•

Genetics and population analysis•

Systems biology•

Data and text mining•

Databases and ontologies(Wikipedia)

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Sanger sequence analysisBase-calling algorithm for

automated DNA sequencingPhred

(Phil Green)

Sanger electrophoresis sequencing

Q = −10 ×

log10

(Pe

)where Pe

: error probability

1: locate predicted peaks2: locate observed peaks3: match observed and

predicted peaks4: find missed peaks

Genome Res. (1998) 8:175-

185. & 186-198.

CodonCode

Q Pe Accuracy

10 10% 90%

20 1% 99%

30 0.1% 99.9%

40 0.01% 99.99%

ABI PRISM® 3130xl DNA Sequencer

One template per reaction

http://en.wikipedia.org/wiki/File:Radioactive_Fluorescent_Seq.jpg

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454 sequence analysisHigh throughput sequencing (454, SOLiD, Solexa/Illumina)

454 pyrosequencing (sequencing by synthesis)

Basecalling

Workflow comparison between 454 and Sanger

454 GS FLX Titanium series:•

1 M Q20 reads (400~600 Mb) per 10-hr run

•

Average read length = 400 bp

(1000 bp

in year 2010)

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Computational Sequence assemblyPrinciple: Align

Overlap

Consensus

MIRA: a hybrid 454/Sanger reads assembler(Streptococcus pneumoniae TIGR4 genome)(www.chevreux.org/mira_ex_454sanger.html)

Sequence assembly

Computational Biology and Chemistry 2009. 33 (2): 121-136http://genome.ku.dk/resources/assembly/methods.html

Low Sanger coverage(one overcall)

High Sanger coverage(4 of 5 trace undercall)

http://www.flybase.org/

http://www.wormbase.org/

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Bioinformatics –

sequence annotationSimilarity/Homology/Motif-based tools: BLAST, InterProScan

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Earthworm transcriptome

project

E. fetida cDNA sequences Sanger 454 GSRaw sequence reads 4032 562327Quality filtered sequence reads 3144 518350Average sequence length (base) 310 104Assembled contigs 448 31114Reads assembled into contigs 1361 236963Unassembled singletons 1783 157070Repeats 121090Outliers and partial sequences 3227Unique sequences 2231 188184

Annotation:BLAST: 10K matches (E≤

10-4); InterProScan: 35K matches;Transcription factor (HMM prediction): 2627 matches

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In-depth

functional annotation

Sanger sequences only

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Outline


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Knowledgebase: a new DOE initiative

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What’s a knowledgebase

Systems Biology Knowledgebase (Knowledgebase) serves as a foundation on which scientists can integrate modeling, simulation, experimentation, and bioinformatics. It will not only give scientists free and broad access to diverse data types but will also provide sophisticated tools for data analysis, visualization, and integration.

Specific objectives of DE-FOA-0000143:(1)

develop methods to integrate multiple data types;

(2)

develop new methods to infer and curate (meta)genomic

functional annotations;(3)

develop methods to couple multiple cellular pathways and processes; and

(4)

develop new methods to model whole cellular processes.

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Omics

databases ≠

Knowledgebase

DECIPHER

DBD

http://www.flybase.org/

http://www.wormbase.org/

http://wfleabase.org/

http://www.genome.ad.jp/kegg/

https://decipher.sanger.ac.uk/

http://www.genedb.org/

http://ipfam.sanger.ac.uk/

http://www.informatics.jax.org/

http://vega.sanger.ac.uk/Danio_rerio/Info/Index

http://www.ncbi.nlm.nih.gov/genome/seq

http://www.yeastgenome.org/index.shtml

http://www.arabidopsis.org/index.jsp

http://www.gramene.org/

http://rgd.mcw.edu/wg/home

http://ecoliwiki.net/

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Knowledgebase if data knowledge

A query-oriented data management system developed jointly by

http://www.biomart.org/biomart/martview/index.html

http://www.ebi.ac.uk/

http://www.oicr.on.ca/

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Earthworm Toxicogenomics

Knowledgebase

ETKB

Proteomic data

Transcription factors

microRNA

& target mRNA

Genome sequence & annotation

QTLSNP

Metabolites

Toolkits(BLOM)

http://www.biomart.org/biomart/martview/index.html

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Outline


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Reverse engineering (RE) is the process

of discovering

the technological principles of a device, object

or system

through analysis

of its structure, function

and operation. (Wikipedia)

Network reconstruction:A fundamental problem in functional genomics is to determine the structure and dynamics of genetic networks based on expression data.

Gene network by Andrey

Rzhetsky

(From: The New Genetics)An embryonic developmental gene network in a fruit fly.

Reverse engineering

http://en.wikipedia.org/wiki/Process_(engineering)

http://en.wikipedia.org/wiki/Discovering

http://en.wikipedia.org/wiki/Device

http://en.wikipedia.org/wiki/Object

http://en.wikipedia.org/wiki/System

http://en.wikipedia.org/wiki/Analysis

http://en.wikipedia.org/wiki/Structure

http://en.wikipedia.org/wiki/Function

http://en.wikipedia.org/wiki/Operation

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GRN in diseased neurons

Changes in gene regulatory networks during disease progression

-- Leroy Hood “Dynamics of a prion perturbed network in mice”

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Changes in gene regulatory networks

Gene expression alteration Gene connectivity alternation

Normal state

Stressed

Diseased

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DREAM project

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RE for network inferenceComparison of different models for GRN inference

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Earthworm Neurotransmission Network reconstruction using BLOM

1t t tx Fx w t t ty Hx v

Inverse Model(State estimate andparameter learning)Observed

measurementsEstimate of

system state

Update GRNsPrior

knowledge

System of Gene Regulation

Measuring devices

Measurementerror source

System state(desired but unknown)

System error source

Controls

Dynamic System(Stochastic)

Forward Model(Deterministic)

Biological system and measurement GRN reconstruction model

Bayesian Learning and Optimization Model (BLOM)

Controlor

RDX(2µg/cm2)

or

Carbaryl(20 ng/cm2)

1) Apply three treatments

to earthworms

0.5 ±

0.1 g

mature

http://images.google.com/imgres?imgurl=http://www.email-marketing-reports.com/images/worms.jpg&imgrefurl=http://www.email-marketing-reports.com/iland/2007_06_01_iland_archive.html&usg=__sqVUjoEVMVhLHkWH25bdTO8QUug=&h=80&w=120&sz=4&hl=en&start=95&um=1&tbnid=1SI3oBiHcvOJ9M:&tbnh=59&tbnw=88&prev=/images%3Fq%3Dearthworms%2Bon%2Bfilter%2Bpaper%26ndsp%3D18%26hl%3Den%26rls%3DSUNA,SUNA:2005-48,SUNA:en%26sa%3DN%26start%3D90%26um%3D1

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RecoveryExposure

Acclimation

Time series toxicity test results

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Genes involved in cholinergic pathway

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Carbaryl_exposure

Carbaryl_recoveryControl

Reconstructed network using BLOM

Preliminary results(1/3 of samples)

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RDX_exposure

ControlRDX_recovery

Reconstructed network using BLOM

Preliminary results(1/3 of samples)

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Outline


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Machine Learning is the study of computer algorithms that improve automatically through experience. Applications range from data mining programs that discover general rules in large data sets, to information filtering systems that automatically learn users' interests.

What’s Machine Learning?

http://www.amazon.com/gp/product/images/1420067184/sr=8-5/qid=1263875605/ref=dp_image_0?ie=UTF8&n=283155&s=books&qid=1263875605&sr=8-5

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• DAC = discriminant

analysis and clustering

• Treatments = control,

RDX‐exposed, TNT‐exposed

• Statistical Analysis

• Tree‐based classification

• Support Vector Machine

(SVM) model

• Hierarchical Clustering

• Numbers in brackets

indicate the amount of

genes remaining after each

step

DAC pipeline for biomarker discovery

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Outline


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Mathematical Modeling Of Biology Is Not A New EndeavourLarge-scale Systems Model Of Cardiovascular Physiology – Guyton 1972

Mathematical modeling in biology

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From simulation to prediction

v-Lung

v-LiverTM

v-CanaryPredictiveToxicology

http://images.google.com/imgres?imgurl=http://www.fws.gov/klamathfallsfwo/about/ecosystem.jpg&imgrefurl=http://www.fws.gov/klamathfallsfwo/about/kffwo.html&usg=___heDZUr9mtJuRhBwn-yGZM9HpxQ=&h=268&w=400&sz=19&hl=en&start=2&um=1&tbnid=COi0rWXBfS2lqM:&tbnh=83&tbnw=124&prev=/images%3Fq%3Decosystem%2Bpictures%26ndsp%3D21%26hl%3Den%26safe%3Dactive%26sa%3DN%26um%3D1

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Math will rock the world

All models are wrong, but some are useful.

-

George Box

•Models as containers of belief or disbelief.

•Belief = Understanding

•Biology = High complexity

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Challenges

•

Incomplete knowledge

•

Wide time and size scale

•

Extrapolation from in vitro in vivo•

Validation often difficult

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iSimBioSys

“A discrete event based stochastic simulation approach for studying the dynamics of biological networks”

iSimBioSys

- Discrete event based biosimulation engine

Stochastic Modeling of biological events and logic modules

In Silico

Results

Integrated platform

HimSim

– Flow based simulation of metabolic network engine

Integrated database of biological networks & pathways

Credit: P. Ghosh

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CompuCell3D

http://www.biocomplexity.indiana.eduhttp://www.compucell3d.org Credit: J. Glazier

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CompuCell3D

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CompuCell3D

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Simulation & visualization

http://www.iac.rm.cnr.it/~filippo/OldWWW/CImmSimWeb/

examples.html

CANCER GROWTH

A 3D plot of the tumor growth in a simulation.

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Summary

http://bioinfo.cs.technion.ac.il/logo.gif

Documents

Embracing the Post-Omics Era with Computational …orca.st.usm.edu/~zhang/seminar/CompuTox2010USMtalk-final...prioritization of data requirements and risk assessment of chemicals"