Analysis of the yeast transcriptional regulatory network

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Analysis of the yeast transcriptional regulatory
network
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Transcription Factor (TF)

• A TF is a protein that binds to DNA sequences and
  regulates the transcriptions of corresponding
  genes.
• Usually the binding site of a TF is one small
  segment of specific promoter sequence.
• The activity of a TF is regulated according to
  the cells need, largely through signal
  transduction. It may not be directly observed,
  but can be reflected by the genes it regulates.

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Expression regulatory network

• Identifying the expression regulatory network is
  a crucial step towards understanding the cellular
  regulation system.
• Inferring network from microarray data alone
• Inferring network from microarray data and TF-TG
  (Target Gene) Information

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Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv
Y, Barkai N. Revealing modular organization in
the yeast transcriptional network. Nat Genet.
2002 Aug31(4)370-7.
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Segal E et al. Module networks identifying
regulatory modules and their condition-specific
regulators from gene expression data. Nat Genet.
2003 Jun34(2)166-76.
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TF Activity

• Use TF-TG relation benefit the regulatory network
  identification
• TF expression level is not a good measure of the
  TF activity. The activated protein level of a TF,
  rather than its expression level, is what
  controls gene expression.
• The activity of a transcription factor is
  regulated according to the cells need, largely
  through signal transduction. It may not be
  directly observed, but can be reflected by the
  genes it regulates.

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Identify TF Activity by NCA

• Network Component Analysis
• Liao JC et al. Network component analysis
  reconstruction
• of regulatory signals in biological systems.
• Proc Natl Acad Sci U S A. 2003 Dec
  23100(26)15522-7.

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NCA compared with PCA, ICA
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NCA Model
Without further constraints, E cannot be
uniquely decomposed to A and P.
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Criteria for Unique NCA E AP

1. The connectivity matrix A must have full-column
   rank.
2. When a node in the regulatory layer is removed
   along with all of the output nodes connected to
   it, the resulting network must be characterized
   by a connectivity matrix that still has
   full-column rank. This condition implies that
   each column of A must have at least L-1 zeros.
3. P must have full row rank. In other words, each
   regulatory signal cannot be expressed as a linear
   combination of the other regulatory signals.

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Criteria 2
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Estimation of EAP
Iteratively estimate A and P A0 ? P1 ? A1 ?
P2 until convergence Convergence criterion
decrease of least square error lt cutoff
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NCA, infer TF activity in Yeast
E A P
How to define the restrictions to CS? i.e. which
CSi,j0?
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Identify the TF-TG relation by ChIP-chip
experiment
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Yeast cell cycle regulation
441 genes vs. 33 transcription factors
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Inference of regulatory network by Two-stage
constrained factor analysis
Yu T, Li KC. Inference of transcriptional
regulatory network by two-stage constrained
space factor analysis. Bioinformatics. 2005 Nov
121(21)4033-8.
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Inference of regulatory network by Two-stage
constrained factor analysis
Shortcoming of Liao et. al.s approach E
AP Let Cij IEij, the constraint of where the
loading matrix A can be non-zero C comes from
very noisy source. Estimate C, A, P
simultaneously.
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Model setting
Up to here, it is the NCA model by Liao et al.
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Model Fitting
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Model Fitting
Difficulties Simultaneous estimation of both
the structure and coefficients amounts to finding
optimum in a very complex function. The
number of parameters to be estimated is
overwhelming.
Solution Find a reasonable local optimum.
Use the high-confidence set to find a starting
point as close to the global optimum as possible.

Implementation Stepwise model fitting.
Start with a network backbone with only the
high-confidence set, and grow the network
gradually, drawing new connections from the
low-confidence set.
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Set CCMIN, estimate each activity profile tk by
the consensus of the expression of the regulated
genes.
Fix estimate of T, regress each gene expression
profile on the activity profiles of TFs that are
associated with it in CMAX. Use BIC and p-value
to select TFs.
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Result
Data Regular growth ChIP data cell-cycle
microarray data
99 TFs enter our study. Start with 891
evidenced relationships and 29154
lower-confidence relationships.
Final network has 3846 TF-gene connections.
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TFs that exhibit correlated expression and
activity
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Time-shifting between a TFs activity profile and
its expression profile

1. Fit the activity profile using cubic spline
2. interpolate the spline to get shifted profile
3. obtain correlation between the expression
   profile and shifted activity profile
4. maximize absolute correlation with regard to
   minute shift.

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TFs that have activity lagging behind expression
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TFs that have activity lagging behind expression
SWI4
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Between-TF regulations