Author Archives: u003f

And now for something completely different

{First posted 6 June 2011 at, saved from oblivion by the internet archive}

Answers to Friday’s teaser.

Where on the earth where could you travel one mile south, then one mile east, then one mile north and end up in the same spot you started?

To me, this is a Christmas cracker question to which everyone knows the easy Christmas cracker answer. However there’s another, harder and unexpected answer, and another another even harder answer.

Needless to say, @Xpic found this no trouble at all, tweeting all three answers in order. Which leads me to think that classics graduates may be the cleverest people in the world.

Easy answer and harder answer:

[tweet 76666634667950080 hide_thread=’true’ align=’center’]

Even harder answer:

[tweet 76667104476151808 hide_thread=’true’ align=’center’]


[19 of 222]

And now for something completely different

{First posted 3 June 2011 at, saved from oblivion by the internet archive}

The whole world was up in arms this Monday. Not only was it grossly unfair that those pesky Brits got yet another day off, it also meant that they missed out on U+003F‘s world famous ANFSCD.

To stem the flow of complaints dropping through my letterbox, I’ve decided to run this week’s teaser on a Friday. It definitely isn’t, for example, because I’m desperate to get out in the sunshine and can’t think of anything deep to say.

Here goes:

Where on the earth where could you travel one mile south, then one mile east, then one mile north and end up in the same spot you started?

But everyone knows that, right? Am I just having another HE-ADAC-HE moment?

[18 of 222]

Models and their granularity

{First posted 2 June 2011 at, saved from oblivion by the internet archive}

A wide range of levels of complexity have used to study nutrient consumption and metabolism. For example, one of the constituents of the BrainCirc model [1] is “a basic model of brain metabolic biochemistry”. It is, in fact, anything but basic, describing in detail each of the many reactions taking place during cellular glucose metabolism. This leads to over 100 parameters, most of which are unknown.

At the other end of the complexity scale is an paper [2] that examines the dynamics that lead to tumour cells maintaining their intracellular pH at physiological levels. Acknowledging the difficulties in parameterising their model, the authors adopt a purely qualitative approach, investigating how general functional shapes affect the steady-state pH levels.

The experimental approach in [3] takes the middle ground. They define functional forms for oxygen and glucose consumption based on empirical considerations, rather than biochemistry. Specifically, they were chosen (and the parameters fitted) in such a way as to ensure they satisfy the Crabtree effect (oxygen consumption falls as glucose rises) and the Pasteur effect (glucose consumption falls as oxygen rises).

Q1. So which approach is correct?

  • a: you should use as few parameters as possible and analyse all possible behaviours
  • b: you should use functional forms that capture general behaviour
  • c: you should describe as much detail as possible even without knowledge of parameters

It’s a moot point, and I think you can pigeonhole yourself based on your answer. If you answered a: you are a mathematical biologist; b: you are a theoretical biologist; c: go to question 2.

Q2. Do you like algorithms?

If you answered i: you are a computational biologist; ii: you are a systems biologist.

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  1. Banaji M, Tachtsidis I, Delpy D, & Baigent S (2005). A physiological model of cerebral blood flow control. Mathematical biosciences, 194 (2), 125-73 PMID: 15854674
  2. Webb SD, Sherratt JA, & Fish RG (1999). Mathematical modelling of tumour acidity: regulation of intracellular pH. Journal of theoretical biology, 196 (2), 237-50 PMID: 9990741
  3. Casciari JJ, Sotirchos SV, & Sutherland RM (1992). Mathematical modelling of microenvironment and growth in EMT6/Ro multicellular tumour spheroids. Cell proliferation, 25 (1), 1-22 PMID: 1540680

What is “Systems Biology”?

{First posted 1 June 2011 at, saved from oblivion by the internet archive}

I recently asked for nominations for the next target on my Wikipedia hit-list. I counted and then recounted, and the winner by just a single vote was the Systems Biology article [1].

It’s funny because there was only one reply.

Ντάνκαν tweeted

The take-home message from Wikipedia seems to be that systems biology is nothing more than a collection of (bad)omics. And to an extent that is true: whenever funding bodies take interest in a topic like systems biology, people rebrand their research as within that sphere, to feed on the cash cow. The result is something of a hotch-potch of disparate activities flying under one banner, with those missing out on the funding declaring the topic a fad.

My own view of what systems biology should be is neatly captured by [2] (though the article isn’t about the field in particular). There are over 20000 articles published each year on cancer, and over 200 genes thought to play a role in governing cancer development. Almost all of this knowledge is disconnected. And you can see why – the intuitive, verbal reasoning approaches favoured by oncologists can be a struggle with two interacting elements, let alone 200.

Systems biology can help in two ways: by providing a framework for storing all this data, and by providing the computational and mathematical tools needed to understand the dataset as a whole.

But my opinion is not relevant on Wikipedia, with all articles written from a neutral perspective [3]. This can sometimes be hard when writing about topics you are immersed in. Check back next week to see how I get on with this one.

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  1. Systems Biology. Wikipedia.
  2. Gatenby, R., & Maini, P. (2003). Mathematical oncology: Cancer summed up Nature, 421 (6921), 321-321 DOI: 10.1038/421321a
  3. Five pillars. Wikipedia.


{First posted 25 July 2010 at, saved from oblivion by the internet archive}

Since release 4.0 of our yeast metabolic reconstruction, a number of questions have been put my way:

  • is there a forum for community discussion?
  • who is using YeastNet, and how?
  • where can I get the very latest unreleased version of the network?

to which my answers have typically been somewhat negative: “no”, “who knows?” and “nowhere”.

Our static webpage is clearly somewhat old-fashioned for today’s user-base, so we’re trialling an alternative approach at sourceforge – There’s an inbuilt forum (and the possibility of a blog, if we’re that way inclined). But, most importantly, any updates to the reconstruction are immediately available; this should reduce the time for incorporation of user-submitted updates from about-a-year to about-a-day.

Happy yeasting!

Snake in the grass

{First posted 16 June 2010 at, saved from oblivion by the internet archive}

One aim of this blog was to advocate and advertise the use of open science. Which is somewhat at odds with my advocation and advertisement of Matlab – a popular, powerful, but ultimately pricey piece of software. I’ve spent some time working with Octave – a language that is identical in every way to Matlab, save the subtle difference that it’s completely free. If you throw in the thriving Octave community, I’d go so far as to recommend that the average user make the open-source switch immediately.

Unfortunately, for the heavy number-crunchers amongst you, there are instances (specifically loopy code) where Octave just cannot match its commercial counterpart for speed (as it has no JIT compiler, if you’re that way inclined). So I’ve recently turned to python for all my programming needs.

As a (n old) Matlab user, I’ve found python very easy to learn, particularly when using the SciPy library. The attached code can be used to produce the beautiful Barnsley fern, an iterated function system designed to resemble the Black Spleenwort. Whilst mastering python, you can ponder Barnsley’s hypothesis that

… when a geometrical fractal model is found that has a good match to the geometry of a given plant, then there is a specific relationship between these code trees and the information stored in the genes of the plant.

Barnsley fern


Michael Barnsley, John E. Hutchinson, & Örjan Stenflo (2003). V-variable fractals and superfractals – arXiv: math/0312314v1

Yeast 4.0 released!

{First posted 6 May 2010 at, saved from oblivion by the internet archive}

One goal of integrative systems biology is the accurate representation of metabolite and protein interaction networks. To this end, several groups independently defined the metabolic network of baker’s yeast from available genomic and literature data [1,2]. These models differed considerably, so a “Jamboree” was held in Manchester in April 2007, bringing together experts from various disciplines to resolve discrepancies. The resultant reconstruction [3], known as Yeast 1.0, represents the first consensus, community-driven model of yeast metabolism.

A number of updates have made the model iteratively bigger and better since then. But Yeast 4.0 [4] represents a major advance in that it is the first to allow in silico experiments such as gene knockout analysis to be performed. Just install the COBRA toolbox, get the SBML model and, er, knock yourself out.

yeast mask from Planet Science


  1. Kuepfer L, Sauer U, & Blank LM (2005). Metabolic functions of duplicate genes in Saccharomyces cerevisiae. Genome research, 15 (10), 1421-30 PMID: 16204195
  2. Mo ML, Palsson BO, & Herrgård MJ (2009). Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC systems biology, 3 PMID: 19321003
  3. Herrgård MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, Blüthgen N, Borger S, Costenoble R, Heinemann M, Hucka M, Le Novère N, Li P, Liebermeister W, Mo ML, Oliveira AP, Petranovic D, Pettifer S, Simeonidis E, Smallbone K, Spasić I, Weichart D, Brent R, Broomhead DS, Westerhoff HV, Kirdar B, Penttilä M, Klipp E, Palsson BØ, Sauer U, Oliver SG, Mendes P, Nielsen J, & Kell DB (2008). A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature biotechnology, 26 (10), 1155-60 PMID: 18846089
  4. YeastNet:

A unique approach to FBA

{First posted 20 April 2010 at, saved from oblivion by the internet archive}

Flux balance analysis (FBA) has emerged as a popular tool for analysing genome-scale metabolic networks. Its key benefit lies in the minimal experimental data needed to make quantitative predictions about biological behaviour. However, one of FBA’s primary disadvantages is that it predicts a range of possible flux solutions, rather than a single output.

Last year, Vangelis Simeonidis and I described an algorithm for extracting a unique solution that is representative of the set of possible fluxes. A number of people have asked for an implementation, and their (and so I suppose the FBA community’s) overwhelming software of choice is the COBRA toolbox (COnstraint-Based Reconstruction and Analysis) for MATLAB.

So, following popular opinion, here’s a two-step process to reproducing the results in Table 1 of our paper:

  1. Install COBRA
  2. Extract and run the script “example.m”

Please contact me with any bugs.


Smallbone, K., & Simeonidis, E. (2009). Flux balance analysis: A geometric perspective Journal of Theoretical Biology, 258 (2), 311-315 DOI: 10.1016/j.jtbi.2009.01.027

Modelling the Pentose Phosphate Pathway

{First posted 9 April 2010 at, saved from oblivion by the internet archive}

As anyone who has tried to reproduce a published mathematical model will testify, it’s a long, tedious, and generally futile task. Equations are replaced by ambiguous descriptions, parameter values are left undefined and, worst of all, the main author has given up science to set up a vegan cup-cake business.

And so Natalie Stanford and I set Holly Summersgill and Ed Kent (two talented students from the Systems Biology DTC) this long, tedious and futile task, with four models of the pentose phosphate pathway:

  • Haut, 1974: Simulation of the Pentose Cycle in Lactating Rat Mammary Gland. PMCID:PMC1166237
  • Sabate, 1995: A model of the pentose phosphate pathway in rat liver cells. PMID:7753046
  • Vaseghi, 1999: In vivo dynamics of the pentose phosphate pathway in Saccharomyces cerevisiae. DOI:10.1006/mben.1998.0110
  • Ralser, 2007: Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress. DOI:10.1186/jbiol61

Results were a mixed bag. Two of the models were fully reproducible, a third was close enough. In the Sabate model, however, the equations and results seemed totally inconsistent, differing in all cases by orders of magnitude. Answers on a postcard please.

But the project was about more than just reproducing results; we also aimed to disseminate the models to allow their easily re-simulation by the community. For this two acronyms were required:

  • SBML: a standardised format for representing models of biological processes, supported by many software packages (including Matlab, Mathematica, …)
  • MIRIAM: a standardised format for annotating the entities of those models. By marking-up the molecule “gluc” as CHEBI:17925 allows its unambiguous identification and automatically links to many additional sources of information.

The models are available at the BioModels Database, (Haut, Vaseghi and Ralser).

facebook thinks I’m Maurice Wilkins

{First posted 5 April 2010 at, saved from oblivion by the internet archive}

Doffing a cap to Professor Hull, and I’m on my way.

I suppose it was inevitable that I’d eventually enter the blogosphere; and indeed the twittosphere (shudder). Though in the end it took some na’er-do-well pinching my facebook identity (and facebook central showing a total lack-of-concern for my plight) before I got myself in gear.

The site aims to serve as part blog, part open note­book for my research in Systems Biology. I’ll leave the Big Questions to the policitians, and instead document my day-to-day work at the coal-face. I’ll publish results, ideas and concerns relating to my various research projects; whilst I appreciate that open science doesn’t always work, I hope the site will prove Boring But Beneficial to the community.