Distributed Information Processing in Biological and Computational Systems

Why this article Studies the similarities between computation and biological systems Explores algorithms used in both systems Highlights strengths and weaknesses in both systems Interest in Machine Learning which is required to solve the distributed problem

The Problem “Biological Systems… are distributed and in most cases operate without central control. Such systems must solve information processing problems that are often very similar to problems faced by computational systems, including coordinated decision making, leader election, routing, navigation, and more.” This article explores problems and how each system approaches a solution.

Paper Outline Communication constraints Runtime vs robustness and adaptability Strategies employed by both systems

Communication Models Most distributed models are based on message passing Beeping Model Maximal Independent Set (MIS) problem in computing Sensory Organ Precursor (SOP) selection in biological systems Population Protocols TCP in networking Ant foraging Beeping Model: a unary signal can be sent (beeping or not, where only communication happening with a beep) solves interval coloring, neighboring nodes have disjoint resources, O(logn) solves Maximal Independent Set (MIS) 1. Every node is either an MIS node or connected to one. 2. no two MIS nodes are connected to each other Fruit Flies brain develops a subset of cell which become sensory bristles in which every cell must be a sensory bristle or connected to one and no to SOP can be next to eachother Population Protocols TCP - determines available bandwidth while routing packets Ants - determine amount of food in environment and location based on speed of interactions

Speed vs Robustness Biological systems are designed to be adaptive and robust Tolerate faults Recover from failures or adapt to another solution Mutation, protein misfolding, targeted virus attacks Computational Systems are designed to optimize runtime Complete an algorithm quickly Employ failure detection, not correction Cryptography, checksums, etc.

Distributed Algorithm Strategies Stochastic decision making breaks symmetry overcome noise ensure survival in changing environments Positive and negative feedback Unique Identifiers biological systems do not use identifiers to label sender/receiver Major/weighted voting

Critique Completeness Discussion of what is to come Cautionary note Defined each algorithm Presented examples in both computational and biological systems Discussion of what is to come Expressed a need for further research in the field New models are needed for dynamic communication Cautionary note Some biological systems do not guarantee optimality Completeness - The authors did an impressive job of defining each algorithm and presenting examples in both computational and biological systems

Questions Literature: Distributed Information Processing in Biological and Computational Systems by Saket Navlakha and Ziv Bar-Joseph (Jan 2015)