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Modelling Transcriptional Regulation with Gaussian Processes
Published on Apr 04, 20075214 Views
Modelling the dynamics of transcriptional processes in the cell requires the knowledge of a number of key biological quantities. While some of them are relatively easy to measure, such as mRNA de
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Chapter list
Modelling Transcriptional Regulation with Gaussian Processes00:01
Outline00:32
Online Resources01:02
Framework01:09
This Talk02:47
Linear Response Solution03:33
Gaussian Processes04:26
Gaussian Processes05:19
Covariance Functions05:54
Covariance Functions06:32
Covariance Functions07:04
Covariance Functions07:08
Different Covariance Functions07:27
Covariance Samples07:30
Covariance Samples07:46
Prior to Posterior07:50
Gaussian Process Regression08:00
Gaussian Process Regression08:05
Gaussian Process Regression08:17
Covariance of Latent Function08:25
Computation of Joint Covariance08:31
Induced Covariance09:16
Covariance for Transcription Model09:26
Joint Sampling of x (t) and f (t)10:08
Joint Sampling of x (t) and f (t)10:40
Joint Sampling of x (t) and f (t)10:42
Noise Corruption10:58
Artificial Data11:57
Artificial Data Results13:16
Results14:28
Linear response analysis14:35
Linear Response Results14:39
Results — Transcription Rates15:25
Results — Transcription Rates15:35
Results — Transcription Rates15:38
Linear Response Discussion15:56
Non-linear Response Model16:25
Formalism16:34
Response Results16:58
Non-linear response analysis17:30
exp (·) Response Results17:47
log (1 + exp (f )) Response Results18:01
3/(1+exp(−f)) Response Results18:18
Discussion18:44
Future Directions19:30