Package: digital-organism-simulation-environment
Digital Organism Simulation Environment (DOSE)Life is fascinating and deeply intriguing. Despite so, life forms on Earthor carbon-based life forms as a group is just one form, one possible sampleof possibly a whole magnitude of life. Even then, there are many aspects oflife that cannot be deciphered even by examining current life forms; forexample, how did chemical reactions organize themselves into biochemicalpathways? How did life start? How is intelligence formed?To answer such questions, we will have to restart our evolutionary time tothe very beginning - clearly an impossibly gargantuan task. At the sametime, studying biological/carbon-based life forms is expensive, timeconsuming and destructive. As a molecular biologist, there is no way I canexamine the entire genome of even a bacteria in an inanimate state, thensomehow allow it to continue living as if time had just stopped while Iam examining it.However, if I can simulate a bacteria or any life form in a computer,then I can make a digital copy of the bacterium, pull it apart to study itwhile the original bacterium continues "living" in my virtual world withouteven knowing that it had been duplicated. Many biologists thought of virtuallife forms as a new way to learn about life itself. Studying of virtual lifeforms is known as Artificial Life and I term "virtual life forms" as"digital organisms". There are several advantages in experimenting usingdigital organisms. Firstly, generation time can be much faster compared tomost biological life. Secondly, it is usually cheaper to examine computersimulations than working on actual biological life. Perhaps the mostimportant advantage of looking at life from this perspective is that byrecreating life in a different medium, we are not limited to our own systemof carbon-based life; hence, studying life as what-it-could-be.Digital Organisms Simulation Environment (DOSE) is essentially a virtualworld simulator for studying digital organisms. I will argue that digitalorganisms are considered living organisms (Koh and Ling, 2013). Despite so,being a molecular biologist by training, I have a hard time mappingcomponents of digital organisms into biological life whenever suchcomponents are too abstract.Hence, I decided to design an artificial life / digital organism simulatorthat bears resemblance to biological life and ecology. These are thefoundation papers:1. Lim, JZR, Aw, ZQ, Goh, DJW, How, JA, Low, SXZ, Loo, BZL, Ling, MHT. 2010.A genetic algorithm framework grounded in biology. The Python Papers SourceCodes 2: 6.This manuscript describes the implementation of a GA framework that usesbiological hierarchy - from chromosomes to organisms to population.2. Ling, MHT. 2012. An Artificial Life Simulation Library Based on GeneticAlgorithm, 3-Character Genetic Code and Biological Hierarchy. The PythonPapers 7: 5.Genetic algorithm (GA) is inspired by biological evolution of geneticorganisms by optimizing the genotypic combinations encoded within eachindividual with the help of evolutionary operators, suggesting that GAmay be a suitable model for studying real-life evolutionary processes.This paper describes the design of a Python library for artificial lifesimulation, Digital Organism Simulation Environment (DOSE), based on GAand biological hierarchy starting from genetic sequence to population.A 3-character instruction set that does not take any operand isintroduced as genetic code for digital organism. This mimics the 3-nucleotide codon structure in naturally occurring DNA. In addition,the context of a 3-dimensional world composing of ecological cells isintroduced to simulate a physical ecosystem.- Ling, MHT. 2012. Ragaraja 1.0: The Genome Interpreter of DigitalOrganism Simulation Environment (DOSE). The Python Papers Source Codes4: 2.This manuscript describes the implementation and test of Ragarajainstruction set version 1.0, which is the core genomic interpreter ofDOSE.From this foundation, the complete suite of Digital Organisms SimulationEnvironment (DOSE) can be build.Project website: U{https://github.com/mauriceling/dose}Documentation can be found at http://maurice.vodien.com/project-doseLicense:Unless otherwise specified, dose.coapds package will be licensedunder Python Software Foundation License version 2; all other files willbe licensed GNU General Public License version 3.Copyright 2010-2014, Maurice HT Ling (on behalf of all authors).