Gene Network Modeling

Gene Network Modeling Prof. Yasser Kadah Eng. Fadhl Al-Akwaa

OUTLINES • What is the Gene Regulatory Network? GRN • Application of GRN • GRN Construction Methodology • GRN modeling steps • GRN Models • GRS Software • Next work • Reference

From The Last Lecture • DNA sequence {A,T,C,G} ATCGAATCGA • Protein sequence { except B, J, O, U, X, Z} KMLSLLMARTYW

Transcription Translation :The Central DogmaProtein Synthesis Cell Function Transcriptome Proteome Genome

Transcription Translation Bioinformatics Important Challenges • Protein Function • Protein 3D Structure • Gene • Prediction • Gene Function

Transcription Translation Public Data Base • Protein sequence • KMLSLLMARTYW • DNA • sequence • {A,T,C,G} • Microarray Gene Expression Level

Gene Expression 9

Microarray Technology

+ + GENE A + - Translation Rate Protein Level Gene Expression Level Transcription Rate

Translation Rate + + Protein Level Gene Expression Level + Transcription Rate GENE A GENE B - ? ? Translation Rate + + Protein Level Gene Expression Level + Transcription Rate -

Translation Rate + + Protein Level Gene Expression Level + Transcription Rate GENE A GENE B - ? Translation Rate + + Protein Level Gene Expression Level + Transcription Rate - ? ? ? ?

OUTLINES • What is the Gene Regulatory Network? • Application of GRN • GRN Construction Methodology • GRN modeling steps • GRN Models • GRS Software • Future work • Reference

What is Gene Regulatory Network? (GRN) ? ? ? ? ? Gene A Gene C Gene D Gene B

GRN An example:Fission yeast Lackner DH ,2007 http://www.sanger.ac.uk/Info/News-releases/2007/070413.shtml

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

http://www.enm.bris.ac.uk/anm/summerschools/complexity/imagery/191.htmlhttp://www.enm.bris.ac.uk/anm/summerschools/complexity/imagery/191.html

Why build a Gene Network? Functional Genomics • Allow researchers to make predictions about gene function that can then be tested at the bench. • The Focus is gradually shifting to Functional Genomics.

Application of GRN Translational Genomics • we can study the effects of a compound (such as a drug) on the level of expression of many genes. • Translational Genomics The mission of the Translational Genomics is to translate genomic discoveries into advances in human health.

Application of GRN UnderstandingExperimental data • Biologists are expecting powerful computational tools to extract functional • information from the Experimental data.

GRN Model Objective Construct a gene network model that: • Describes known genes interactions well • Predicts interactions not known so far • Allows for Drug effect simulation • Understand the etiology of the Disease

GRN Construction Methodology • Forward Engineering • Inverse Engineering “Traditional methodology”

Forward Engineering Hard

Reverse Engineering Model Gene Network very difficultinverse problem Possible forward problem Microaary Data

Reverse Engineering Boolean networks easy Boolean data easy

assay Data Required: DNA Microarray gene 1 gene 2 gene 3

Data Required: Gene Expression Matrix

Data Required: Gene Expression Matrix Time serious Snap Shot

OUTLINES • What is the Gene Regulatory Network? GRN • Application of GRN • GRN Construction Methodology • GRN Modeling Steps • GRN Models • GRS Software • Next work • Reference

Overview of steps in modeling and control of Probabilistic Boolean networks Ranadip Pal,2007 Microarray Image Data Extraction Discretization A3 A1 A2 C Gene Expression Extraction B Discretization Grid Alignment Segmentation Hypothesis testing Upregulated 99% 1 t1 0 t2 -1 Down regulated t1 t2 Application of Stationary Policy Design of Optimal Control Policy Gene Selection BN generation (I) Penalty Assignment PBN steady state matched Seed Algorithm Y (II) Formulation of Optimal Control Problem Original Steady State Dynamic Programming Optimal Control Policy F E Prior Biological Knowledge D Gene Selection Steady State using Control Network Generation G H Control of Network

GRN modeling steppes: Discretization gene 1 gene 2 gene 3 assume that genes exist in two states: on and off if expression of gene i is above level ti consider it on, otherwise, consider it off

GRN modeling steppes: Discretization t1

GRN modeling steppes: Discretization on on on on on on on t1 off off off off off off off off

GRN modeling steppes: Discretization gene 1 gene 2 gene 3 t1 t2 t3

GRN modeling steppes: Discretization gene 1 gene 2 gene 3 on on on on on on on on on t1 on on on t2 off on on t3 off off off off off off off off off off off off off off off off off off

GRN modeling steppes: Discretization • we obtain the following discretized gene expression data: • the gene expression data is now in the form of bit streams

GRN modeling steppes: Discretization Up-regulated 1 Unchanged 0 Down-regulated -1 assume that genes exist in three states

GRN modeling steppes: Gene SelectionClustring

Clustering Steps: Correlation Choose a similarity metric to compare the transcriptional response or the expression profiles: Pearson Correlation Spearman Correlation Euclidean Distance …

Clustering Steps: Correlation Algorithm • Correlation coefficients are values from –1 to 1, with 1 indicating a similar behavior, –1 indicating an opposite behavior and 0 indicating no direct relation.

Clustering Steps: Clustering Algorithm • Choose a clustering algorithm: • Hierarchical • K-means • …

Hierarchical Clustering g1 g4 • Find largest value in similarity matrix. • Join clusters together. • Recompute matrix and iterate.

Hierarchical Clustering g2 g3 g1 g4 • Find largest value is similarity matrix. • Join clusters together. • Recompute matrix and iterate.

Hierarchical Clustering g5 g2 g3 g1 g4 • Find largest value is similarity matrix. • Join clusters together. • Recompute similarity matrix and iterate.

Clustering Example Eisen et al. (1998), PNAS, 95(25): 14863-14868

_ g1 _ + ? _ g2 g3 + + _ g4 _ Gene network GRN Modeling Steppes: GRN Generation Statistical Signal Processing Technique

Gene Network Modeling

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