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![Page 1: Microarrays to Functional Genomics: Generation of Transcriptional Networks from Microarray experiments Joshua Stender December 3, 2002 Department of Biochemistry.](https://reader036.fdocuments.in/reader036/viewer/2022081519/56649f175503460f94c2dcec/html5/thumbnails/1.jpg)
Microarrays to Functional Genomics:Generation of Transcriptional Networks
from Microarray experiments
Joshua Stender
December 3, 2002
Department of Biochemistry
![Page 2: Microarrays to Functional Genomics: Generation of Transcriptional Networks from Microarray experiments Joshua Stender December 3, 2002 Department of Biochemistry.](https://reader036.fdocuments.in/reader036/viewer/2022081519/56649f175503460f94c2dcec/html5/thumbnails/2.jpg)
What is a genetic network?
Gene networks are usually represented as directed
graphs where the nodes are defined as the genes and the edges represent regulation.
Networks summarized a limited relationship
between a subset of genes in both positive and
negative feedback loops.
Jenssen et al. 2001
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Why interested in Genetic Networks?
• Drug therapies for complex diseases• Gain insights for stimulus-response
interactions• Identify novel pathways• Understand cell physiology • Understand multifactor gene-gene or gene-
protein relationships in normal and disease states
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Modeling Network Framework
• Need to define a map from sequence space to functional space
• Stage of Regulation (RNA, Protein)
• Temporal Regulation
• Spatial Regulation(Nucleus,Cytoplasm, etc)
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Prazhnik et al. Gene networks:how to put the function in genomics. Trends in Biochem 20: 467-72.
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Methods for Developing Gene Networks
• Two types of experiments used for network design: Time series and Steady-State gene knock-out
• Co-expression clustering
• Cis acting elements in promoters(Amy Creekmore)
• Reverse Engineering: use of algorithms to generate new networks
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Time-Series Approach
• Expression level of a certain gene at a time point can be modeled as some function of previous time points.
• Problem exists with dimensionality where more genes then time points. Better results require more time points
• Solution in the literature: Basic Linear Model, Singular Value Decomposition, and Bayesian Networks
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Steady-State Approach
• Takes advantage of gene deletions or over expression
• If gene A goes up after gene B deleted, perhaps gene B is negative modulator of A and so on
• Microarrays offer opportunities to identify gene deletion consequences on entire genomes
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Genetic Network Generation Schematic
Jong Modeling and simulation of genetic regulatory systems: a literature review. J. Comput Biol 2002;9(1):67-103
![Page 10: Microarrays to Functional Genomics: Generation of Transcriptional Networks from Microarray experiments Joshua Stender December 3, 2002 Department of Biochemistry.](https://reader036.fdocuments.in/reader036/viewer/2022081519/56649f175503460f94c2dcec/html5/thumbnails/10.jpg)
Algorithmic Approach to Network Design
• Boolean Binary State along with co expression clustering
• Continuous Steady-State(Non-Linear):Assumes genes can have intermediate states
• Singular Value Decomposition
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Methods for Generating Gene Networks
• D’Haeseleer et al. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16(8): 707-26.
• Fuente et al. Linking the genes: inferring quantitative gene networks from microarray data. Trends in Genetics 18(8): 395-98.
• Toh et al. Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling. Bioinformatics 18(2): 287-297.
![Page 12: Microarrays to Functional Genomics: Generation of Transcriptional Networks from Microarray experiments Joshua Stender December 3, 2002 Department of Biochemistry.](https://reader036.fdocuments.in/reader036/viewer/2022081519/56649f175503460f94c2dcec/html5/thumbnails/12.jpg)
Types of Clustering
• Non-hierarchical- clusters N objects into K Groups until a preset threshold is established. Examples include: K-means, SOM, and Expectation-maximization
• Hierarchical- returns a hierarchy of nested clusters (agglomerative vs. divisive)
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Why use clustering?
• Wealth of data from microarray is overwhelming• Cluster to limit gene list to one that has genes that
change significantly• Inference of functional annotation • Extraction of regulatory motifs• Molecular signature for distinguishing cell or
tissue types• Use of learning machines to characterize unknown
genes
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Determining Distances Between Genes
• Majority of clustering algorithms use matrix of pair wise distances between genes
• Distances can be calculated based on:
1. Similarity according to positive correlations
2. Similarity based on positive and negative correlations
3. Similarity based on mutual information
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Guilt-by Association(GBA)
• Gene selected at random and determine its nearest neighbor
• Genes are clustered based on arbitrary cut-off distances in expression space
• Assumes that genes regulated in the same pattern participate in similar processes
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K-means Clustering
• Partitions N genes into K groups
• Centroids are weighted center of a cluster
• Each gene is assigned to a cluster and the centroid is calculated
• Centroid continuously recalculated and genes reassigned
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Self-Organizing Maps (SOM)
• Very similar to K-means, however cluster centers are placed on a grid
• At each iteration, gene pattern chosen at random and nearest cluster neighbor and cluster center updated
• Requires user to define number cluster and grid size
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Expectation-Maximization
• Clustering similar to K-means, however genes assigned to multiple categories
• Membership to a cluster is based on Gaussian distribution of probabilities
• Continuously update membership and the 3 following parameters are assigned for each cluster: centroid, covariance, and mixture weight
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Determining which clustering analysis to use
• Each combination of distance measure and clustering algorithm will emphasize different types of regularities associated with data
• Best to complement data with more than one clustering analysis due to variety of algorithms and the multiple functions of each gene
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Brazhnik et al.
Construction of a Simple Network
Clustering
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Boolean Networks
• Simplification: each gene represented in the binary ON/Off state
• Each gene is regulated by other genes using Boolean functions
• Most genes are in an intermediate state and therefore are continuous
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Example of a Boolean Network
Jong Modeling and simulation of genetic regulatory systems: a literature review. J. Comput Biol 2002;9(1):67-103
![Page 23: Microarrays to Functional Genomics: Generation of Transcriptional Networks from Microarray experiments Joshua Stender December 3, 2002 Department of Biochemistry.](https://reader036.fdocuments.in/reader036/viewer/2022081519/56649f175503460f94c2dcec/html5/thumbnails/23.jpg)
Limitations of Boolean Networks
• Fail to reveal causality
• Non-Quantitative
• Does not take into account multiple gene states
• In the future Protein-Protein interaction maps need to be included
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Graphical Gaussian Model
• Toh et al. Inference of a Genetic Network by a Combined Approach of Cluster Analysis and Graphical Gaussian Modeling. Bioinformatics 18(2): 287-297.
• Goal: To establish a method to combine Clustering and GGM for genetic network predictions.
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Graphical Gaussian Modeling
• GGM is a multivariate analysis to infer or test a statistical model for the relationship among a plural of variables where a partial correlation is used
• Data: 2467 Saccharomyces cerevisiae genes under 79 different conditions
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Graphical Gaussian Method
• Genes were clustered into 34 distinct clusters
• To reduce dimensionality, each cluster was averaged for each condition
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Step 0: Complete Graph generated with M nodes and every node connected to each other.
Step 1: Calculate partial correlation Matrix P( from correlated Coefficient Matrix C( where indicates iteration.
Step 2: Find element with smallest absolute value in P( and replace it with 0.
Step 3: Reconstruct C(from P(
Step 4: Termination is dependent on deviance Dev1= Nlog ( | C(C(0)|)Dev2= Nlog ( | C(C()|)Calculate dev1 and dev2. If either dev <.05 iteration stopped. Else go to step1
Stepwise iterative algorithm developed by Wermuth and Scheidht(1977)
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Graphical Gaussian Method
Sub graph of the independence graph corresponding to partial correlation coefficient matrix
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Graphical Gaussian Method Results and conclusions
• Algorithm stopped after 189 iterations• SUC2(sucrose hydrolyzing enzyme) was used as
model to evaluate accuracy of method: Among 40 known correlations for other genes, method identified 3 to be of same cluster,8 to have correlation of 0 and 29 to interact.
• Conclude that about 75% accurate. • Could be a highly effective method for gene
network generation if combined with previous knowledge
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Linear Additive Method
• Fuente et al. “Linking the genes: inferring quantitative gene networks from microarray data.” Trends in Genetics 18(8): 395-8.
• Goal: To establish a method for inferring gene networks and the corresponding gene interaction strengths
• Represent gene networks that consider expression levels as continuous variables
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Linear Additive Method
Co-control coefficient
FR=Fluorescence Intensities
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Linear Additive Method Conclusions
• In Silico approach is useful in testing inferred networks
• Can be used with experiments with one gene disruption at a time
• Generated method for developing gene networks that include quantitative interaction strengths
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New and Improved Network Designs
• Continuous-value network inference: uses differential equations and allows genes to be continuous variables
• Gene Duplication: Network nodes are randomly duplicated to help network connections evolve
• Many computer simulations are being developed to help mimic real data to aid in the design of new algorithms
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Conclusion and Outlook
• Integration of large amount of biological data and computational power increasing our knowledge of complex systems
• Increasing need to standardize microarray experiments and create databases
• Gradual improvement of cluster and gene inference algorithms
• Addition of differential proteomics and also incorporation of multiple regulation steps