Backup and fault tolerance in systems biology: Striking similarity with Cloud computing
2009 June 19
Striking similarity between biological systems and computing paradigms is not new, and in past there have been several attempts to draw an analogy between systems biology and computing systems. For interested readers I will recommend my last post which examine how systems biology of human can be describes as a grid of super-computers. Over the time researchers have developed several bio-inspired fault-tolerance methods to support fault detection and removal in both hardware and softwares systems, such as fault-tolerant hardware inspired by ideas of embryology and immune systems. Fault tolerance is the ability of a system to retain intended functionality even in the presence of faults, and in case of living cells fault-tolerance is due to the intrinsic robustness of their gene regulatory networks which can be easily observed in case of mutation-insensitivity expression of genes with phenotypic feature. In recent issue of journal Molecular Systems Biology, Anthony Gitter and other co-authors suggest that gene regulatory networks also have backup plans very much like cloud computing networks or MapReduce framework where failure of a computing node is managed by by re-executing its task on another node. Fault-tolerant is seen as mechanism to retain the functionality of master gene in very extreme circumstances through a controller mechanism, while backup plan employs another gene with reasonable sequence similarity to master gene in order to perform the tasks which are key for the survival of cell itself. Their finding suggest that
[T]he overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets.
The yellow TF which has sequence similarity as well as shared interactions with green TF can replace the green TF when it is knocked out and is able to recruit the transcription machinery leading to only small overlap between binding and knockout resultsIn order to understand the systems biology of robustness provided by redundant TFs and their role in broader cellular context authors explored dependence of findings on the TFs’ homology relationships and shared protein interaction network. They observed that TFs with the most similar paralogs had no overlap between their binding and knockout data, while protein interaction networks provide physical support for knockout effects.
Further Gitter describes importance of his research as,
It’s extremely rare in nature that a cell would lose both a master gene and its backup, so for the most part cells are very robust machines. We now have reason to think of cells as robust computational devices, employing redundancy in the same way that enables large computing systems, such as Amazon, to keep operating despite the fact that servers routinely fail
A simple backup mechanism in MapReduce frameworkReferences:
Gitter, A., Siegfried, Z., Klutstein, M., Fornes, O., Oliva, B., Simon, I., & Bar-Joseph, Z. (2009). Backup in gene regulatory networks explains differences between binding and knockout results Molecular Systems Biology, 5 DOI: 10.1038/msb.2009.33
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#cloud Backup and fault tolerance in systems biology: Striking similarity … http://ow.ly/eYKD
Backup and fault tolerance in systems biology: Striking similarity with Cloud computing: Striking similarity bet.. http://tinyurl.com/l63rwh
Backup and fault tolerance in systems biology: Striking similarity with Cloud computing: Striking similarity bet.. http://tinyurl.com/l63rwh
Backup and fault tolerance in systems biology: Striking similarity … http://tinyurl.com/n26xkn
Backup and fault tolerance in systems biology: Striking similarity … http://tinyurl.com/n26xkn
Backup and fault tolerance in systems biology: Striking similarity with Cloud computing http://tinyurl.com/l63rwh
AFAIK, most genes in a cell are not redundant, but degenerate (meaning they perform different functions but can compensate each other, at lower fitness). Redundancy as an engineer would design it is not evolutionary stable. Does this article say engineers are starting to design degenerate rather than redundant networks?
I guess not, this is just a case of redundancy, but at same time I don’t find well defined notions of degeneracy and redundancy in biological systems as in case of engineering design where we have well established definitions.
Well, I guess what I was trying to say was that if the authors claim there is redundancy in biological systems, they either use the term incorrectly and mean degeneracy, or they don’t understand evolutionary theory. Redundant genes will accumulate mutations and alter/lose function over time (become degenerate). There are some theoretical conditions under which you can get redundancy in gene networks, but I’m not sure how realistic they are: http://www.ncbi.nlm.nih.gov/pubmed/9217155
Apologies if this is a very newbie type question, but is there a numerical measure of degeneracy? Are certain pathways “more degenerate” than others? Do certain organisms/cell types have greater levels of degeneracy?
@Ben there are few proposed measures, for example use of information theoretical approach which can estimate degeneracy and redundancy of a system in terms mutual information. http://www.pnas.org/content/96/6/3257.full suggests “degeneracy is high for systems in which many different elements can affect the output in a similar way and at the same time can have independent effects”
This is an interesting but not necessarily novel theoretical view about the true complexity of life forms. The most successful plant breeder in Canada in the past century pointed out that most of the traits he was dealing with did not obey mendelian laws. He raised this protest in 1910, when the laws of Mendel were becoming the focus of most research in genetics and heredity. For nearly a century scientists were perhaps too attracted by the simplicity and beauty of the Mendelian theory – and later by the DNA based models – to pay attention to the complexity of the real case. This is why the vast majority could not understand McClintock. We are still far from ready to claim we have a coherent theory about heredity and about the mechanisms of life. The network properties that are under investigation (many articles dealing with this) provide a picture of a system that is more adaptable to environmental stresses and has broader diversity of evolutionary mechanisms than what the Mendelian model could support.