Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection

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Dynamic network reconstruction from gene
expression dataapplied to immune response during
bacterial infection
Bioinformatics, vol21,no.8 pp1626-1634
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Motivation

• The immune response to bacterial infection
  represents a complex network of dynamic gene and
  protein interactions. The goal is to develop a
  optimized reverse engineering strategy to aimed
  at a reconstruction of this kind of interaction
  networks.

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Transcriptional Regulation (Background)
Each gene has a promoter region where activators
and repressors can attach. While prokaryotic
regulation tends to be more simple and many times
involves just a single activator or repressor,
eukaryotic regulation tends to involve a
combinatorial interactions between several
transcription factors. This allows a more
sophisticated response to multiple conditions in
the environment which is then able to generate
spatial temporal differential expression.
Eukaryotes also make use of enhancers , distant
regions of DNA that can loop back to the promoter
.
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Statistical Problem

• In standard gene expression profiling there are
  many more variables (N genes) than measurements
  (M experiments), so the genetic interaction
  matrix (NxN) of linear model cannot be uniquely
  determined by measurement matrix (NXM)

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Solutions

• fuzzy clustering to reduce the number of
  variables. This can be justified by presence of
  regulatory motifs.
• Methods for finding sparse interaction matrices
  by combinatorial search strategy.
• Singular value decomposition (SVD) based methods
  that calculate a solution to the interaction
  matrix by imposing additional mathematical
  constraints.

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Strategy

• 1). Data pre-processing.
• 2). Optimized fuzzy clustering and cluster
  validation.
• 3). Selection of cluster-representative genes.
• 4). Reconstruction of probable dynamic network
  models ( fitting the simulated kinetics to the
  experimental expression profiles. )

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Dataset

• Stereotyped and specific gene expression
  programs in human innate immune responses to
  bacteria.
• Boldrick JC et al.
• Proc Natl Acad Sci U S A. 2002 Jan 2299(2)972-7
• genome-www.stanford.edu/hostresponse/download.shtm
  l

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Data pre-processing

• Data represents logarithm zed ratios (log-ratios)
  of the expression intensities of 18 432 genes at
  5 time points (t0,0.5,1,2,4h) before and after
  an infection with heat-killed pathogenic E.coli.
• A total of 1336 genes was selected by requiring
  an up regulation or down regulation of at least a
  factor of 8
• For clustering,the time profiles were scaled by
  their respective absolute temporal extreme values
  to focus on qualitative behavior.

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Clustering and validation by fuzzy C-means
(FCM)algorithm
uij membership matrix xi data entry cj center
of the jth cluster norm functions that
measure distance
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Selection of cluster-representative genes

• is assigned to one cluster with a high fuzzy
  membership degree n (MSD)
• annotated with a known immunological function
• is represented by an expression profile with no
  missing values.

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Dynamic modeling
xj(t) expression of gene j1 to C at time t wi,j
gene interaction matrix bi external(infection)
stimulus response vector u(t) heaviside step
function
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Dynamic modeling using a search strategy

• Initialization
• combinatorial search
• First phase forward selection of genetic
  interactions, model complexity expanded
• Second phase backward elimination to simplify
  obtained model
• Third phase refining model by adapting the type
  of dynamic dependency

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L1-fit, n31 mse1.52
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Alternative structure of the dynamic system with
third order time lag elements for CD59 and STAT1
obtained from the proposed Network generation
methods configured by Rmax3 and Emax1
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Online resource

• Validation
• https//www.cs.tcd.ie/Nadia.Bolshakova/validation_
  algorithms.html
• Introduction for clustering algorithm
• http//www.elet.polimi.it/upload/matteucc/Clusteri
  ng/tutorial_html/index.html

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Algorithm of fuzzy C-means clustering