Sometimes scientists move from the snicky use of referee reports to kill others ideas into open discussions on the origin or complexity of life.
This is one of these weird situations, and a worth participating poll. You may easily infer my opinion…
Dynamics and enzyme catalysis?
CBBL
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Sep 272010
Sep 082010
Coming back from vacations is never easy, and it is even worst in science. A lot of project proposals and articles to be written and conferences to be improved piled up in your laptop and you did not have the time to finnish even half of them. What is worst, that turn in your research you wanted time to think about didn’t come out as expected. Indeed, taking strong and deeply thought decisions is not an easy task while supervising your 6 to 11 years old kids in a rocky beach in the Costa Brava, climbing a Pyrenees mountain or enjoying a foam party in Vilamaniscle.
So fresh air is always welcome and I found it (as most good science) by chance at Uri Alon’s site. Afterwards (as happens also with most good science, unfortunately…) I realized his paper on “How to Choose a Good Scientific Problem” was extremely highly read. His “Materials for Nurturing Science” have started producing in me a similar effect as the reading of some Feynman’s lectures produced years ago, when a PhD student in Lluch’s and González-Lafont lab. Pessimistic views were (and periodically are) collapsing into what Alon refers as “the cloud”. But some readings and talks (veeeery few) lead us again to the right track, the one that was not initially drawn into our particular history. The wall, that I call sometimes (sorry, link in catalan). At any rate, spending some time having a look at Uri’s “Materials…” is an excellent therapy for the type of people that always reject any type of therapy: scientists…
Anyway, I think reading Uri’s paper and having some time to see his videos is an excellent start for any new PhD student or postdoctoral researcher in my lab, so here are the links for you, if you are thinking on spending the best years of your scientific career (and who knows if also of your life) at the CBBL.
Comment on “Statistical Analysis of Global Connectivity and Activity Distributions in Cellular Networks”
CBBL
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Sep 022010
Aaron Clauset comments on the article in his blog. I am glad to see he shares our view but what he does not know (because the history of a paper is never clearly realized in its final published form) is that the article was send for the first time during the spring 2008. It’s been a looong way until somebody else realized the paper gave a comprehensive view of the (at that time!) current data and accepted the paper for publication. Are we facing some sectarism in science? I (naively) used to think NO, but sometimes one does not know what to believe…
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[2010,article] bibtex
A. L. García–Lomana, Q. Beg, G. de Fabritiis, and J. Villà–Freixa, "Statistical Analysis of Global Connectivity and Activity Distributions in Cellular Networks," J. Comput. Biol., vol. 17(7), pp. 869-878, 2010.@ARTICLE{Garcia2010,
author = {Adri\'an L. Garc\'ia--Lomana and Qasim Beg and Gianni de Fabritiis and Jordi Vill\`a--Freixa},
title = {Statistical Analysis of Global Connectivity and Activity Distributions in Cellular Networks},
journal = {J. Comput. Biol.},
year = {2010},
volume = {17(7)},
pages = {869--878},
abstract = {Various molecular interaction networks have been claimed to follow power-law decay for their global connectivity distribution. It has been proposed that there may be underlying generative models that explain this heavy-tailed behavior by self-reinforcement processes such as classical or hierarchical scale-free network models. Here we analyze a comprehensive data set of protein-protein and transcriptional regulatory interaction networks in yeast, an \textit{E. coli} metabolic network, and gene activity profiles for different metabolic states in both organisms. We show that in all cases the networks have a heavy-tailed distribution, but most of them present significant differences from a power-law model according to a stringent statistical test. Those few data sets that have a statistically significant fit with a power-law model follow other distributions equally well. Thus, while our analysis supports that both global connectivity interaction networks and activity distributions are heavy-tailed, they are not generally described by any specific distribution model, leaving space for further inferences on generative models.},
url = {http://arxiv.org/abs/1004.3138}
}
