Distributed Information Processing in Biological and Computational Systems
Monday, January 26th, 2015Distributed Info. Processing in Biological & Computational #Systems http://cacm.acm.org/magazines/2015/1/181614-distributed-information-processing-in-biological-and-computational-systems/fulltext Contrasts in strategies to handle node failures
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While both computational and biological systems need to address these similar types of failures, the methods they use to do so differs. In distributed computing, failures have primarily been handled by majority voting methods,37 by using dedicated failure detectors, or via cryptography. In contrast, most biological systems rely on various network topological features to handle failures. Consider for example the use of failure detectors. In distributed computing, these are either implemented in hardware or in dedicated additional software. In contrast, biology implements implicit failure detector mechanisms by relying on backup nodes or alternative pathways. Several proteins have paralogs, that is, structurally similar proteins that in most cases originated from the same ancestral protein (roughly 40% of yeast and human proteins have at least one paralog). In several cases, when one protein fails or is altered, its paralog can automatically take its place24 or protect the cell against the mutation.26 Thus, by preserving backup functionality in the protein interaction.
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While we discussed some reoccurring algorithmic strategies used within both types of systems (for example, stochasticity and feedback), there is much more to learn in this regard. From the distributed computing side, new models are needed to address the dynamic aspects of communication (for example, nodes joining and leaving the network, and edges added and being subtracted), which are also relevant in mobile computing scenarios. Further, while the biological systems we discussed all operate without a single centralized controller, there is in fact a continuum in the term “distributed.” For example, hierarchical distributed models, where higher layers “control” lower layers with possible feedback, represent a more structured type of control system than traditional distributed systems without such a hierarchy. Gene regulatory networks and neuronal networks (layered columns) both share such a hierarchical structure, and this structure has been well-conserved across many different species, suggesting their importance to computation. Such models, however, have received less attention in the distributed computing literature.
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