1. Distributed Information Processing
in Biological and Computational Systems

2. 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

3. 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.

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

5. 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

6. 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.

7. 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

8. 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

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