Biological Solution to a Fundamental Distributed Computing Problem

• Published in: Science (American Association for the Advancement of Science) Vol. 331; no. 6014; pp. 183 - 185
• Main Authors: Afek, Yehuda, Alon, Noga, Barad, Omer, Hornstein, Eran, Barkai, Naama, Bar-Joseph, Ziv
• Format: Journal Article
• Language: English
• Published: Washington, DC American Association for the Advancement of Science 14-01-2011; The American Association for the Advancement of Science
• Subjects: Algorithms; Anatomy; Animals; Applied sciences; Biological; Broadcasting industry; Cellular biology; Computer Communication Networks; Computer networks; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Distributed computing; Distributed processing; Drosophila - cytology; Drosophila - growth & development; Exact sciences and technology; Imaging; Management information systems; Mathematical Computing; Mathematics; Messages; Models, Biological; Nervous system; Probability; Processors; Pupa - cytology; Pupa - growth & development; Pupae; Sensory Receptor Cells - cytology; Software; Input output; Leader election; Independent set; Nervous system; Distributed system; Fast algorithm; Distributed computing; Precursor; Multicore processor; Local network; Biological system
• ISSN: 0036-8075; 1095-9203
• DOI: 10.1126/science.1193210

Computational and biological systems are often distributed so that processors (cells) jointly solve a task, without any of them receiving all inputs or observing all outputs. Maximal independent set (MIS) selection is a fundamental distributed computing procedure that seeks to elect a set of local leaders in a network. A variant of this problem is solved during the development of the fly's nervous system, when sensory organ precursor (SOP) cells are chosen. By studying SOP selection, we derived a fast algorithm for MIS selection that combines two attractive features. First, processors do not need to know their degree; second, it has an optimal message complexity while only using one-bit messages. Our findings suggest that simple and efficient algorithms can be developed on the basis of biologically derived insights.