Transcript to flux methods

Last year, Daniel Machado and Marcus Herrgård published a great paper [1] evaluating different methods for mapping from transcript data to flux patterns. Flux predictions for eighteen methods were compared to three E.coli and yeast datasets.

The methods varied widely their requirements: continuous vs. discrete levels (10⁵/cell vs. high/low) and absolute vs. relative expression (10⁵/cell vs. twice as much as a reference condition). I wouldn’t say that Daaaaave (rather boringly called “Lee-12” above) is the best algorithm out there — indeed the paper finds that all the methods perform equally well (badly). But I do think that it lies in the right part of the graph, using continuous, absolute data.

One of the successes of Machado’s paper is making their code and datasets available via github. I’ve now forked it into my own repository, allowing us to test new methods using their suite.

My first test was to attempt to address the problem that

Methods that do not make any assumptions regarding a biological objective (iMAT, Lee–12 and RELATCH*) … incorrectly predicted a zero growth rate in all cases

by enriching the reconstructions: associating the genes encoding the main DNA polymerases with growth. It didn’t make any difference, and I shouldn’t have been surprised. This gene association is one data point amongst thousands, and the cell’s growing would require a major rerouting of flux, thereby moving other many data points away from their best fit.

Back to the drawing-board.

References

1. Daniel Machado and Marcus Herrgård (2014) “Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism” PLoS Computational Biology 10:e1003580.
   doi:10.1371/journal.pcbi.1003580

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Diabetic neuropathy

For the next couple of months I’ll be working on a project alongside Natalie Gardiner from Life Sciences, with co-conspirators @neilswainston and @porld. Natalie studies diabetic neuropathy: this debilitating nerve damage affects around half of diabetes patients, and can lead to pain or loss of sensation. The causes of the condition are not well-understood, though elevated blood glucose is known to be a key factor.

Natalie’s lab has amassed comprehensive proteomic and metabolomic data sets from nerve cells in diabetic rats. This is a great opportunity for us to put to work some of the tools we’ve developed in the MCISB. ‘Omics measurements can tell us what changes occur in a disease, but not which of these changes are important to its development; for this we require knowledge of the underlying network organisation. We shall apply our Daaaaave algorithm [1, see also “From genes to fluxes”] to rat-nerve specific derivations of Recon 2 [2, “Hambo” see also “Striking a balance with Recon 2.1”], to derive cell–level behaviour from Natalie’s gene-level data.

Watch this space for news on “Super-Daaaaave” and “Rambo”.

References

1. Lee D, Smallbone K, Dunn WB, Murabito E, Winder CL, Kell DB, Mendes P, Swainston N (2012) “Improving metabolic flux predictions using absolute gene expression data” BMC Systems Biology 6:73.
   doi:10.1186/1752-0509-6-73
2. Thiele I, Swainston N, Fleming RM, Hoppe A, Sahoo S, Aurich MK, Haraldsdottir H, Mo ML, Rolfsson O, Stobbe MD, Thorleifsson SG, Agren R, Bölling C, Bordel S, Chavali AK, Dobson P, Dunn WB, Endler L, Hala D, Hucka M, Hull D, Jameson D, Jamshidi N, Jonsson JJ, Juty N, Keating S, Nookaew I, Le Novère N, Malys N, Mazein A, Papin JA, Price ND, Selkov E Sr, Sigurdsson MI, Simeonidis E, Sonnenschein N, Smallbone K, Sorokin A, van Beek JH, Weichart D, Goryanin I, Nielsen J, Westerhoff HV, Kell DB, Mendes P, Every Man, His Dog, Palsson BØ (2013) “A community-driven global reconstruction of human metabolism” Nature Biotechnology 31:419-425.
   doi:10.1038/nbt.2488