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

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