Backup and fault tolerance in systems biology: Striking similarity with Cloud computing

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 asa 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 results

In 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