Determinism versus randomness in Biology

As mentioned earlier, I bought the current issue of La Recherche for its catchy special report on the impact of randomness on gene expressions and other biological events. The absurd graph was actually a good  warning about the quality of the report: there is nothing there but a succession of evidences, cut by empty generalities on the nature of randomness like “determinism is only a special case of randomness”, “random does not mean unpredictable” and “randomness does not imply an absence of reproducibility”… The type of determinism invoked in the articles seems to be related to the basic Cartesian type of determinism where a given cause always induces the same effect, which would mean that the same regulatory proteins induce the same expression on all genes at all times. Given that these proteins are in small quantities, thus are not available for all genes, it seems to me quite logical that some genes are expressed and others are not! The argument is therefore not even anti-deterministic but gives a finer reason to the source of the expression mechanism. Randomness at the molecular level thus appears as an evidence and the report does not shed any light on why this “theory” has had so much trouble to penetrate the deterministic foundations of Biology. This is a wasted opportunity as I think there are fairly interesting issues in this domain, in particular in trying to explain different reactions of a cell under identical energy  input and environmental conditions. As usual, I find the journal has a terrible tendency to oversimplifiy topics to the point of making them uninteresting.

Ps-A gold medal to the sentence that “throwing a [fair] coin 100,000 times will result in about 50,000 heads”, missing the fact that the standard deviation is then of 500, i.e. that the outcome is 50,000±1000… And a silver one for missing the religious meaning of probabilism.

6 Responses to “Determinism versus randomness in Biology”

  1. […] often, I bought La Recherche in the station newsagent for the wrong reason! The cover of the December issue was about “God […]

  2. […] Recherche on current mathematics In November, La Recherche (also) published a special issue on the power of mathematics. While this issue contains a load of […]

  3. The paper by Darren Wilkinson is what I was looking for, even though it is presumably too advanced forLa Recherche! It starts by arguing against continuous deterministic mathematical models. “it has recently been acknowledged that biochemical kinetics at the single-cell level are intrinsically stochastic [and] that stochastic models are necessary to properly capture the multiple sources of heterogeneity needed for modelling biosystems in a realistic way”. It then describes how a deterministic ODE model fails to explain the highly noisy and heterogeneous observed cellular response to DNA damage in a protein degradation system. Since stochasticity and heterogeneity are aspects of model biological system behaviour that cannot be ignored”, the paper shows how a Markov jump process does much better on the same system, incorporating intrinsic noise and other sources of heterogeneity, thanks to simulations. The paper also stresses the appeal of (Bayesian) statistical methods for both deterministic and stochastic models, especially in network structure estimation. The conclusion is worth reporting:

    “It is therefore likely that we will see a Bayesian ‘revolution’ in computational systems biology, similar to that already experienced in genetics and bioinformatics. The scientific community must recognize the pivotal role of statistics and statisticians in systems biology research. No serious genetics laboratory or clinical trials unit would be considered complete without at least one expert statistical modeller. The contribution that a statistician can make to the success of a systems biology laboratory is every bit as great, but owing to the historical development of this new discipline, this fact has not been widely appreciated.”

  4. This is a topic close to my heart. There is a lot of confusion about randomness and stochasticity in biology. It seems to derive from the fact that most people in the field have a very poor understanding of probability theory generally, and the Bayesian interpretation in particular. I was recently asked to write an article on the topic for Nature Reviews Genetics, which may be of interest to some:

  5. At least, they did not insult you again.

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