Enthought and SBML

January 3rd, 2012

A long time ago, I resolved to replace Matlab with python as my programming language of choice. I was recommended the Enthought python distribution (EPD), which has various packages installed around pylab and is free to academics.

I had some issues getting EPD to talk to SBML, until a young man with a weird surname helped me out. The three steps to overcoming your ophidiophobia are:

1. Link EPD’s unusual directory to the normal place …
sudo ln -s /Library/Frameworks/EPD64.framework /Library/Frameworks/Python.framework/

2. … configure libSBML 5.3.0 there …
./configure --with-python=/Library/Frameworks/Python.framework/Versions/Current

3. … and change ~/.bash_profile to
export PATH="/Library/Frameworks/Python.framework/Versions/Current/bin:${PATH}"
export PYTHONPATH=/usr/local/lib/python2.7/site-packages



A python

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Wikipubdate

June 13th, 2011

Some time ago I promised to rewrite the Wikipedia Systems Biology article and I thought I should update you as to how far I’ve got.

Nowhere.

But I’m pleased to announce that other projects are going very well indeed. You see the small, insignificant green plus sign at the top right of the History of Tranmere Rovers F.C. page? I did that. It means Good Article. It’s like getting a B at GCSE Wikipedia authoring. Go me!

The Computational biology wikiproject has really kicked into gear too; a “bunch of geeks” from Hinxton are driving activity, with the backing of the ISCB. Come and get involved!

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Posted in postaday2011, wikipedia | No Comments »

MCA revisited [4]

June 10th, 2011

Of course we can repeat yesterday’s perturbation to reaction 1 over all the reactions. Displaying the results in matrix form, we find that the scaled flux control coefficients are given by

(C_{v_j}^{J_i}) = \left(\begin{array}{rrrrrrrr} 0.12 & 0.25 & 0.26 & 0.03 & 0.02 & 0.28 & 0.03 & 0.01\\ 0.12 & 0.25 & 0.26 & 0.03 & 0.02 & 0.28 & 0.03 & 0.01\\ 0.12 & 0.26 & 0.68 & 0.08 & 0.05 & -0.17 & -0.02 & -0.01\\ 0.12 & 0.26 & 0.68 & 0.08 & 0.05 & -0.17 & -0.02 & -0.01\\ 0.12 & 0.26 & 0.68 & 0.08 & 0.05 & -0.17 & -0.02 & -0.01\\ 0.11 & 0.24 & -0.15 & -0.02 & -0.01 & 0.71 & 0.07 & 0.03\\ 0.11 & 0.24 & -0.15 & -0.02 & -0.01 & 0.71 & 0.07 & 0.03\\ 0.11 & 0.24 & -0.15 & -0.02 & -0.01 & 0.71 & 0.07 & 0.03 \end{array}\right)

whilst the scaled concentration control coefficients are given by

(C_{v_j}^{S_i}) = \left(\begin{array}{rrrrrrrr} 0.77 & -1.47 & 0.29 & 0.03 & 0.02 & 0.31 & 0.03 & 0.01 \\ 0.45 & 0.97 & -0.58 & -0.07 & -0.04 & -0.63 & -0.07 & - 0.03 \\ 0.14 & 0.31 & 0.79 & -0.77 & -0.24 & -0.20 & -0.02 & -0.01 \\ 0.07 & 0.15 & 0.40 & 0.05 & - 0.56 & -0.10 & - 0.01 & -0.00 \\ 0.14 & 0.29 & -0.18 & -0.02 & -0.01 & 0.86 & -0.86 & -0.22 \\ 0.08 & 0.16 & -0.10 & -0.01 & -0.01 & 0.48 & 0.05 & -0.65 \end{array}\right)

So for example, the second row, first column of C_v^S shows how the second metabolite (B) varies with the activity of the first reaction.

The astute reader may notice that the rows of C_v^J sum to 1, and those of C_v^S sum to zero. These relationships, known as the summation theorems, show that control of the fluxes and concentrations in a system is shared amongst all the steps in the system.

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References

  • Hofmeyr JH, & van der Merwe KJ (1986). METAMOD: software for steady-state modelling and control analysis of metabolic pathways on the BBC microcomputer. Computer applications in the biosciences: CABIOS, 2 (4), 243-9 PMID: 3450367
  • Model v.1 SBML

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MCA revisited [3]

June 9th, 2011

MCA is about response to perturbations.

In the METAMOD paper [1] they perturb yesterday’s system, increasing the activity of reaction 1 by 1%. Looking at the response to this change gives an indication of the control the reaction has over the system. Here reaction rate does not increase by 1% – as you would expect if the reaction existed independently from the complex pathway – but only 0.12%. The set of all reaction rate changes are

rxn change
1 0.118%
2 0.118%
3 0.123%
4 0.123%
5 0.123%
6 0.113%
7 0.113%
8 0.113%

These are known as flux control coefficients and are equivalent to the derivative of flux with respect to change in reaction 1

C_{v_1}^{J_i} = \frac{d \ln J_i}{d \ln v_1}.

We can similarly look at changes in concentration

met change
A 0.767%
B 0.450%
C 0.143%
D 0.072%
E 0.136%
F 0.076%

known as concentration control coefficients

C_{v_1}^{S_i} = \frac{d \ln S_i}{d \ln v_1}.

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References

  • Hofmeyr JH, & van der Merwe KJ (1986). METAMOD: software for steady-state modelling and control analysis of metabolic pathways on the BBC microcomputer. Computer applications in the biosciences: CABIOS, 2 (4), 243-9 PMID: 3450367
  • Model v.1 SBML

Posted in MCA, postaday2011 | 2 Comments »

MCA revisited [2]

June 8th, 2011

As a base example, we’ll use the model presented in [1], a 1986 paper describing METAMOD – modelling software for your BBC micro. How I’d love to have a play with METAMOD now.

The model is called SEQFB, a branched pathway with sequential feedbacks. The program is used to find the model’s steady state for metabolite concentrations

Met Conc Copasi
A 2.2099E1 22.0992
B 4.6395E0 4.63950
C 3.3577E-1 0.335772
D 4.3999E-1 0.439992
E 2.7777E-1 0.277773
F 2.6500E-1 0.264999

and reaction rates.

Enz Rate Copasi
1 1.4261E0 1.42613
2 1.4261E0 1.42613
3 6.9765E-1 0.69753
4 6.9765E-1 0.69753
5 6.9765E-1 0.69753
6 7.2847E-1 0.728472
7 7.2847E-1 0.728472
8 7.2847E-1 0.728472

It’s good to see that loading the model [2] into fancy new software [3] gives identical results. Who needs technology eh? OK, so maybe it runs slightly faster today – half a blink of an eye, rather than just under two minutes.

More tomorrow.

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References

  • Hofmeyr JH, & van der Merwe KJ (1986). METAMOD: software for steady-state modelling and control analysis of metabolic pathways on the BBC microcomputer. Computer applications in the biosciences: CABIOS, 2 (4), 243-9 PMID: 3450367
  • Model v.1 SBML
  • Copasi http://www.copasi.org/

Posted in MCA, postaday2011 | No Comments »

MCA revisited [1]

June 7th, 2011

Quite some time ago I promised to spend some time going over Metabolic Control Analysis (MCA). It’s one of the main reasons I decided to do this silly #postaday business, but have been rather hesitant as I just don’t know if what I hope to do is possible.

He who dares, Rodders.

The landmark paper “The control of flux” was written in 1973 by Henrik Kacser and Jim Burns [1], describing how the rates of metabolic pathways were affected by changes in the amounts or activities of pathway enzymes. Their work was given a sound mathematical basis by Christine Reder in 1988 [2].

Given there are excellent webpages devoted to the topic [3,4] along with countless review articles, you might reasonably ask why on earth I’m rerevisiting old ground.

In the paper, Reder sets out criteria under which her analysis holds. Which means of course that there are situations where it doesn’t hold. These are generally ignored – probably because the paper is so “mathsy” – with software tools typically blindly applying the algorithm. I shall show, by example, where the theory breaks down, and (this is where I’m on shaky ground) how it can be tweaked to encompass these cases.

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References

  1. Kacser H, & Burns JA (1973). The control of flux. Symposia of the Society for Experimental Biology, 27, 65-104 PMID: 4148886
  2. Reder C (1988). Metabolic control theory: a structural approach. Journal of theoretical biology, 135 (2), 175-201 PMID: 3267767
  3. Athel Cornish-Bowden. Metabolic Control Analysis
  4. Pedro Mendes. MCA web

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And now for something completely different

June 6th, 2011

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:

@u003f North pole

Harder answer:

@u003f OR a place one mile North of the place near the South pole where the Earth has a circumference of 1 mile.

Even harder answer:

@u003f Or, in fact, a circumference of half a mile, or a third, or a quarter, or any inverse integer.

Genius.

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Posted in ANFSCD, postaday2011 | No Comments »

And now for something completely different

June 3rd, 2011

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?

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Posted in ANFSCD, postaday2011 | 1 Comment »

Models and their granularity

June 2nd, 2011

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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References

  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

Posted in postaday2011 | 3 Comments »

What is “Systems Biology”?

June 1st, 2011

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

@u003f re: http://bit.ly/11-of-222 #postaday
how about rewriting http://bit.ly/systems-biology-wtf so it makes sense?

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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References

  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.

Posted in cancer, postaday2011, wikipedia | 2 Comments »

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