Network Based Prediction of Metabolic Enzyme Sub-cellular Localisation

Shira Mintz-Oron***
Clear and easy to understand talk but the method seems very complex, it is necessary to go to the flux level?

GFP and microscopy are the experimental techniques but they are costly and difficult in higher eukaryotes.

Localisation is by motifs, composition, homology etc.
Here predict localisation based on metabolic networks.
Prior knowledge of the localisation of a subset of the network is needed, want to minimise the number of membrane transport reactions (thermodyamic cost).
Use constraint based modelling to predict the flux rate at a steady state, predict the flux rates under constraints of mass balance, thermodynamic constraints and enzymatic capacity (rate bounds). Maximise biomass production.

1. Divide the data into the localised and the unlocalised (unknowns) enzymes.
2. Put all the enzymes into all compartments - build all the transport reactions limit enzyme activity for known localised reactions to the compartments in which they are known to be active.
3. Give a unit penalty for transport reactions - want a low penalty so that some transport is allowed.

Fuzzy classification so gets scores for more than one compartment and so this means that a distribution across multiple compartments is possible.

Using Side Effects of Medicines to Identify Drug Targets

Michael Kuhn **
Phenotype data - what side effects are caused by a drug - from clinical trials - text mined (1000)
Targets of drugs are also available as a dataset - 500
750 drugs to characterise drugs with similar side effects have similar targets.

• Problem to deal with synonyms in phenotype data - use ontologies (Costart) to cluster the same concepts in the side effects.
• Some side effects are very common - parent terms of more specific side effects in the ontology - these become non-predictive. use a log frequency weight.
• Some side effects are correlated use Gerstein-Sonnhammer-Chothia weights (from HMM).

Use shuffling to normalise the score and get the side effect similarity. Also measure chemical similarity. Low chemical similarity low chance of sharing targets and similar for side effect similarity - need both chemical and side effect similarity for effective description. Side effect much more effective at predicting the same targets than chemical similarity.

http://sideeffects.embl.de


Can create a drug-drug network connected when they share a target which will have the same phenotype. Rabeprazole is an exception as it is not a nervous system drug but a stomach drug.

Side effects also occur with the placebo as well as with the drug.

On targets and off targets are treated equally in the model - so they assume the same off target is affected in the case of having the same side effects.

http://stitch.embl.de

Clustered Alignments of Gene-Expression Time Series Data

Adam Smith ***
Want to align time series to compare treatments so you can find causative effects. Ultimately would be good to search a database for similar effects to find genes that are operating together.

You get warping so you need to align equivalent points. Use splines to create continuous series from discrete data.
Shorting alignments trim series to the same features of maxima and minima.
SCOW - efficient method for aligning time series.

• Most extensive fiting is dynamic programming to minimse Euclidean distances between two time series being aligned (Sakoe and Chiba 1978)
• Parametric time warping (Eilers 2005) approximate warping to a parabolic or linear warp.
• Segment based warping (Smith et al 2008) - alignment score for different segments
• COW correlation optimised warping - points of discontinuity are called knots - where there is a break and a warp.
• SCOW - shorting correlation optimised warping.

Evaluated EDGE toxicology database 216 observations 1600 genes times 6 to 96 hours.
Trying to match query to find the most similar treatment profiles in the database.

For clustering pick an average time series alignment and then the extremes above and below before continuing to add the other time series distances to each of the clusters. By using clusters the alignments are improved.

Modelling Stochasticity and Robustness in Gene Regulatory Networks

Abhishek Garg ****

Takes a Boolean modelling approach. Stochastic Boolean Modelling

• T-helper cell differentiation gene regulatory network Mendoza and Xenarios 2006
• T-cell activation network Stephan Klamt
  

Enviromental input changes the nature of the differentiated cell. Stochastic behaviour is common in biological models.

Robustness maintains functionality over perturbations. In gene networks do they move between steady states (can they change attractor), and give different cell type.

Stochasticity can be applied to the nodes or the functions.
Not Probabalistic Boolean Networks - Datta group.
Use Boolean functions AND, OR etc.

Stochasticity in nodes flip the output using a probability distribution.
Kauffman, Willanda etc lots of literature.
Over-represents noise by placing it at the end and not at the inputs/intermediates.

More stochastic the more interaction are involved so allosteric and protein localisation has high noise.
http://si2.epfl.ch/~garg/

Modelling Ecological and Genetic Diversity in Bacteria

Eric Alm ***
Used adaptML to look at the partitioning of bacteria by habitat and seasonal variation. This is an MC based method.

Ecological preferences are sufficient to produce speciation - what was defined as one species was found to have 17 different habitat locations and seasonal variations. Habitats are defined by net sizes or species in which they are present. Move from fish to squid means adaptation to the mucus membranes and so aquired new features.

Go from zooplankton to small particle associated. Looking for patterns of gene expression, between different lifestyles. Did 20 genomes how do you analyse a population of genomes?

1. Where are the recombination break-points?
• Inconsistency of a phylogentic tree at a point with phylogentic trees from preceding bases.


• McDonald-Kreitman Test

Information and Biology

Pierre-Henri Gouyon ***** (cannot give this enough stars this is fundamental)

Takes Richard Lewontin's view - Triple Helix - Genes, Epigentics and Environment.
http://www2.mnhn.fr/oseb/spip/

Biodiversity - treasure but also a problem - devil is in the detail.
Biology is a young science - anthropomorphism and vitalism are difficult to escape, hard to make it Galilean like physics. Born really in 1859 with Darwin.

• Mandrake is human seed grown from the ground.
• Linaeus is the frist bioinformatician.

"eternal law of reproduction and multiplication within the limits of their proper types" Linnaeus

Diversity is the variety of species - is biodiversity just counting the species and what differences warrant new species - in insects differences are much smaller.

• Cuviers - Extinctions destroy invariability there is no transformation.
• Lamark species transform never become extinct.

Darwin considered individuals and not species. Product of his environment and seeing the struggles around him. All are as evolved as the others.

"no clear line of demarcation has as yet been drawn between species and sub-species" Darwin.

It is a continuum - this is very important. We have become bean counter and not integrative biologists.

Galton developed heritability and Weissman kills of acquired characteristics - amputees do not give birth to amputees. Do not produce what I have "learnt" pass on what was given to me. I am like a tube through which something passes so the only effect an individual can play is over the number of progeny.

• Bateson pupil of Galton - did not like Maths
• Pearson was the maths student.

Systematics and genetics converge with coalescence. Diversity needs to refect distances as well as number of leaves.

Epigenetics - what the reading system add to pass on from one generation to the next.

"Individuals are contingent artefacst invented by genes to be reproduced."

What information produces the individual?
No linear relationships DNA does not go to RNA like carbon + oxygen goes to carbon dioxide. So the flow is not linear or free from weight.

Genes are agents and not limited to the matter genes are not equal to DNA.

The same repsonse can be triggered from many different causes so how do you quantify information without context? You need to be able to read a book for the information to be transformed into action. Recipes do not make dishes. Need recipe, chef (reading system) and the environment - his oven/kitchen.

The Triple Helix

Nature is not moral or immoral it is indifferent. This was the shock of Darwin.

Eugenics Ada Juke case - sex perverts and illegitimacy were hertiable. It was common to all geneticists of the time - they all believed they could do the most for mankind, scientist sense of superiority. Ended at Nuremberg so now we look at the human rights.

• Not determined by our genes but our environment.- left wing
• Innate is more important - right wing.

Exchange horses and riders so each rider rides each horse over the same course. Contingent over the horses and the riders. Results cannot be extrapolated to new populations - only valid for the same sets.

Need a proper definition of biological information beyond Shannon.

BioPathways SIG II - ISMB/ECCB 2009

Using FunCoup to predict disease genes

Eric Sonnhammer *
Build networks combining all biological data PPI, coexpression, phylogenetic profiles (for co-evolution), domain interactions, subcellular co-localisation, TF binding sites, miRNA targetting.

Uses orthology to relate between organisms. Data is compared vs "training sets" in a Bayesian Framework. Using Gold Standard datasets HPRD for PPI, KEGG metabolic and signalling pathways. Uses log odds ratios. Networks cut at a confidence of 0.2 number of interactions grows exponentially with confidence level (why does this worry me?). Lots of support for interations comes from orthologous species.

http://FunCoup.sbc.su.se

Query from a few starting genes. Searches across species show orthologues. Examples for Parkinsons and Alzheimers from his paper.

http://genome.cshlp.org/content/19/6/1107.long

The methods can only predict associations to disease if the genes are part of the Large Central Component - isolated genes are not within the network.