Pim van Meurs
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Member # 541
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posted 12. November 2003 01:14
26 September 2003 Vol. 301 Science
Networks In Biology
Viewpoints
Biological Networks: The Tinkerer as an Engineer
by U. Alon
Francois Jacob pictured evolution as a tinkerer, not an engineer. Engineers and tinkerers arrive at their solutions by very different routes. Rather than planning structures in advance and drawing up blueprints (as an engineer would), evolution as a tinkerer works with odds and ends, assembling interactions until they are good enough to work. It is therefore wondrous that the solutions found by evolution have much in common with good engineering design. This Viewpoint comments on recent advances in understanding biological networks using concepts from engineering
For a copy see here
I'd like to explore these issues of good engineering design, especially the findings that nature seems to exhibit degeneracy rather than the more commonly used engineering approach of redundancy.
Modularity, robustness and other such concepts have been shown to be explainable by natural principles. So an ID inference may not be possible based on purely eliminative approaches but can we perhaps use degeneracy in nature as a reliable detector of design which does not require intelligent designers? What about the reuse of motifs, from an evolutionary standpoint this is quite understandable but knowing engineers, they'd rather design from scratch especially in areas not directly related to the original motif rather than tinker with the existing motif.
quote:
These concepts, together with the current technological revolution in biology, may eventually allow characterization and understanding of cell-wide networks, with great benefit to medicine. The similarity between the creations of tinkerer and engineer also raises a fundamental scientific challenge: understanding the laws of nature that unite evolved and designed systems.
Some relevant papers on the importance of scale free networks, neutral evolution can be found
Peter Schuster
Rock presented another interesting paper
quote:
What, then, is the role of theoretical modeling efforts in relation to the -omic scale data sets being generated? At present, there is already a wealth of data deposited in public databases that might yield insight into currently unresolved biological problems. Although efforts to model the systems-level ‘behavior’ of cellular interaction networks based solely on these data may not even be feasible, we certainly expect more from our investment than just individual facts deposited in a database. New algorithmic approaches to parsing networks into modules and motifs represent exciting first steps toward adding more value and biological relevance to these data. Furthermore, engineering-based approaches, including the de novo design of simple networks, coupled with modeling bring us closer to the ultimate goal of building realistic large-scale models of biological systems. At the beginning of this review, we presented the question: what can we learn about biology by studying networks? Though we are still a long way from a complete answer to this important question, we can offer two partial answers. First, network-based approaches to uncovering patterns will help to organize this vast collection of data in a way that makes it more accessible and valuable to traditional biologists. Second, reformulating existing biological questions from a network perspective (as discussed in a previous section) has the potential to take full advantage of the wealth of available data and answer questions that could not be addressed otherwise. Although these complex biological networks are proving to be more difficult to model the more we learn about them, we fully expect that our efforts will be rewarded with a detailed picture of the process by which living systems derive phenotype from genotype.”
Link
Similarly Grape Ape provided for the following links
Preferential attachment in the protein network evolution.
Connectivity distribution of spatial networks.
Expanding protein universe and its origin from the biological Big Bang.
Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome.
There is also the very interesting discussion between Dembski and Deanne Taylor at ISCID
And finally the following paper by Barabasi on emergence of scaling in complex networks.
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