About
Experimental advances in molecular biology are providing deeper understanding in the workings of living cells. High throughput functional genomic techniques are providing researchers with a reliable map of the complex networks underpinning the functioning of cells. Cellular processes often involve complex networking of several genes and transcription factors, and their temporal structure can often be accurately described in terms of pathways. A key problem in obtaining a computational understanding of these systems is the incomplete and noisy nature of most data: while certain relevant quantities, such as mRNA concentrations, can be measured accurately in a high throughput fashion, others, such as transcription factor concentrations, are difficult to measure quantitatively.
Probabilistic machine learning techniques such as Bayesian Networks have emerged in recent years as one of the main computational tools. Starting from the pioneering work of Friedman et al. (J. of Comp. Biol., 2000), probabilistic models of gene networks have received considerable attention (for some more recent works, see e.g. Nachman et al 2004, Beal et al 2005, Sanguinetti et al 2006, Sabatti and James 2006, etc). Despite the success of this approach, outstanding tasks remain to be addressed. For example, it is very hard to formulate tractable models that take into account the combinatorial nature of gene regulation, and generalising genome-wide models to incorporate dynamical effects such as pathways presents formidable computational challenges.
The main aim of this workshop is to bring together researchers working on the many facets of these problem, providing a forum for discussion and giving focus to the future directions of research. We aim to involve some experimental biologists in order to foster collaborations between computational and experimental researchers.
Find out more here.
Videos
Opening

Opening of the PMNP 2007 in Sheffield
Sep 5, 2007
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2702 views
Invited talks

Evolution of protein complexes and protein interaction networks
Sep 7, 2007
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6576 views
Lectures

A comparison of hypothesis testing methods for ODE models of biochemical systems
Sep 7, 2007
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4811 views

Gene Regulatory Network Inference: In Silico Hypotheses and Experimental Validat...
Sep 5, 2007
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4810 views

Reverse engineering gene and protein regulatory networks using graphical models:...
Sep 5, 2007
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7211 views

Stochastic estimation of fluxes in metabolic networks
Sep 7, 2007
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4872 views

Learning gene regulatory networks in Arabidopsis Thaliana
Sep 7, 2007
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4827 views

Estimating parameters and hidden states in biological networks with particle fil...
Sep 7, 2007
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6097 views

Stochastic Parameter Estimation in Biochemical Signalling Pathways
Sep 7, 2007
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3973 views

ProBic: identification of overlapping biclusters usinf Probabilistic Relational ...
Sep 7, 2007
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3731 views

Bayesian Inference of transcription factor activity - an application to the fiss...
Sep 7, 2007
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3738 views
Mixture models on graphs
Sep 7, 2007
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4147 views

Inferring ancestral states of the bZIP transcription factor interaction network
Sep 7, 2007
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2827 views

Least squares estimation of a transcription regulation model
Sep 5, 2007
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3001 views
Debate

Debate
Sep 7, 2007
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2474 views