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.
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Uploaded videos:
Opening
Opening of the PMNP 2007 in Sheffield
Sep 05, 2007
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2694 Views
Invited talks
Evolution of protein complexes and protein interaction networks
Sep 07, 2007
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6567 Views
Lectures
Gene Regulatory Network Inference: In Silico Hypotheses and Experimental Validat...
Sep 05, 2007
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4806 Views
Least squares estimation of a transcription regulation model
Sep 05, 2007
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2993 Views
Reverse engineering gene and protein regulatory networks using graphical models:...
Sep 05, 2007
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7202 Views
Learning gene regulatory networks in Arabidopsis Thaliana
Sep 07, 2007
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4821 Views
ProBic: identification of overlapping biclusters usinf Probabilistic Relational ...
Sep 07, 2007
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3720 Views
Estimating parameters and hidden states in biological networks with particle fil...
Sep 07, 2007
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6091 Views
Inferring ancestral states of the bZIP transcription factor interaction network
Sep 07, 2007
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2820 Views
Mixture models on graphs
Sep 07, 2007
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4147 Views
Bayesian Inference of transcription factor activity - an application to the fiss...
Sep 07, 2007
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3732 Views
Stochastic estimation of fluxes in metabolic networks
Sep 07, 2007
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4864 Views
Stochastic Parameter Estimation in Biochemical Signalling Pathways
Sep 07, 2007
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3963 Views
A comparison of hypothesis testing methods for ODE models of biochemical systems
Sep 07, 2007
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4805 Views
Debate
Debate
Sep 07, 2007
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2467 Views