CPHx: Mike Hubank, UCL Genomics, Inst. of Child Health – Genome-wide Transcriptional Modeling

Genome-wide Transcriptional Modeling

Mike Hubank, UCL Genomics, Inst. of Child Health


Challenges in complex transcriptional analysis.

Interpretation beyond the “favorite genes/Top 10” style are less frequent.  Ask better questions:

  • What TF activities control the response?
  • which genes are targets of which TF?
  • How does TF activity affect the expression patter?
  • How do tf activities interact to shape the response?

Data driven mathematical modeling offers solutions.

[ooo.. actual math!  Can’t copy it, tho.]

Goal is to assign values to the parameters, including correct rate of change of gene, concentration, degredation rate, activity of transcription factor, etc.

[more equations..  rearrangements of the one before]

Everything you can measure on one side, what you can’t on the other.  Discretisation by Lagrange interpolation.  Genome wide transcriptional modeling (GWTM) generates production profiles for every transcript.

Example:  DNA damage response model, Radiating T-cell cell-line.  Model based screening of activity profiles.  Identifies jointly regulated transcripts, groups them, with probabilities.

Validation done.  Knock out one gene, see if predictions make sense in taking out other genes as well.  “obviously they went quite well, otherwise I wouldn’t be here talking to you.”

GWTM explains a high proportion of the response.  68% of upregulated genes correctly assigned.  Unexplained transcripts result of co-regulation or measurement error.  Aim to eradicate error with Sequencing based measurement.

There is a problem with NGS data, however, when it gives “zeros”.  When you have a read of zero, it may not actually be completely off, so it’s hard to model around that.  New Correction tool – Dirac-sigma truncated log normal. [did I get that right? I’ve never herd of it.]

Conclusions: Dynamic transcriptional modelling can be used for:

  • Data driven deconstruction of complex responses
  • Ability to conduct complex “experiments” in silico (eg, vary TF activity or parameter models to make predictions.)
  • Generation of biologically meaningful parameter values (eg, correlation with apoptosis regulators.)
  • Linkage of functional modules
  • (Benefit of) Economy

Much of this is already available in R, the rest of it will be shortly.

1 thought on “CPHx: Mike Hubank, UCL Genomics, Inst. of Child Health – Genome-wide Transcriptional Modeling

  1. Pingback: Copenhagenomics » Recap of Day 1 at CPHx

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