Digital Organism Simulation Environment (DOSE)
Project description
Digital Organism Simulation Environment (DOSE)
Life is fascinating and deeply intriguing. Despite so, life forms on Earth
or carbon-based life forms as a group is just one form, one possible sample
of possibly a whole magnitude of life. Even then, there are many aspects of
life that cannot be deciphered even by examining current life forms; for
example, how did chemical reactions organize themselves into biochemical
pathways? How did life start? How is intelligence formed?
To answer such questions, we will have to restart our evolutionary time to
the very beginning - clearly an impossibly gargantuan task. At the same
time, studying biological/carbon-based life forms is expensive, time
consuming and destructive. As a molecular biologist, there is no way I can
examine the entire genome of even a bacteria in an inanimate state, then
somehow allow it to continue living as if time had just stopped while I
am 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 it
while the original bacterium continues "living" in my virtual world without
even knowing that it had been duplicated. Many biologists thought of virtual
life forms as a new way to learn about life itself. Studying of virtual life
forms is known as Artificial Life and I term "virtual life forms" as
"digital organisms". There are several advantages in experimenting using
digital organisms. Firstly, generation time can be much faster compared to
most biological life. Secondly, it is usually cheaper to examine computer
simulations than working on actual biological life. Perhaps the most
important advantage of looking at life from this perspective is that by
recreating life in a different medium, we are not limited to our own system
of carbon-based life; hence, studying life as what-it-could-be.
Digital Organisms Simulation Environment (DOSE) is essentially a virtual
world simulator for studying digital organisms. I will argue that digital
organisms are considered living organisms (Koh and Ling, 2013). Despite so,
being a molecular biologist by training, I have a hard time mapping
components of digital organisms into biological life whenever such
components are too abstract.
Hence, I decided to design an artificial life / digital organism simulator
that bears resemblance to biological life and ecology. These are the
foundation 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 Source
Codes 2: 6.
This manuscript describes the implementation of a GA framework that uses
biological hierarchy - from chromosomes to organisms to population.
2. Ling, MHT. 2012. An Artificial Life Simulation Library Based on Genetic
Algorithm, 3-Character Genetic Code and Biological Hierarchy. The Python
Papers 7: 5.
Genetic algorithm (GA) is inspired by biological evolution of genetic
organisms by optimizing the genotypic combinations encoded within each
individual with the help of evolutionary operators, suggesting that GA
may be a suitable model for studying real-life evolutionary processes.
This paper describes the design of a Python library for artificial life
simulation, Digital Organism Simulation Environment (DOSE), based on GA
and biological hierarchy starting from genetic sequence to population.
A 3-character instruction set that does not take any operand is
introduced 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 is
introduced to simulate a physical ecosystem.
- Ling, MHT. 2012. Ragaraja 1.0: The Genome Interpreter of Digital
Organism Simulation Environment (DOSE). The Python Papers Source Codes
4: 2.
This manuscript describes the implementation and test of Ragaraja
instruction set version 1.0, which is the core genomic interpreter of
DOSE.
From this foundation, the complete suite of Digital Organisms Simulation
Environment (DOSE) can be build.
Project website: U{https://github.com/mauriceling/dose}
Documentation can be found at http://maurice.vodien.com/project-dose
License:
Unless otherwise specified, dose.coapds package will be licensed
under Python Software Foundation License version 2; all other files will
be licensed GNU General Public License version 3.
Copyright 2010-2014, Maurice HT Ling (on behalf of all authors).
Life is fascinating and deeply intriguing. Despite so, life forms on Earth
or carbon-based life forms as a group is just one form, one possible sample
of possibly a whole magnitude of life. Even then, there are many aspects of
life that cannot be deciphered even by examining current life forms; for
example, how did chemical reactions organize themselves into biochemical
pathways? How did life start? How is intelligence formed?
To answer such questions, we will have to restart our evolutionary time to
the very beginning - clearly an impossibly gargantuan task. At the same
time, studying biological/carbon-based life forms is expensive, time
consuming and destructive. As a molecular biologist, there is no way I can
examine the entire genome of even a bacteria in an inanimate state, then
somehow allow it to continue living as if time had just stopped while I
am 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 it
while the original bacterium continues "living" in my virtual world without
even knowing that it had been duplicated. Many biologists thought of virtual
life forms as a new way to learn about life itself. Studying of virtual life
forms is known as Artificial Life and I term "virtual life forms" as
"digital organisms". There are several advantages in experimenting using
digital organisms. Firstly, generation time can be much faster compared to
most biological life. Secondly, it is usually cheaper to examine computer
simulations than working on actual biological life. Perhaps the most
important advantage of looking at life from this perspective is that by
recreating life in a different medium, we are not limited to our own system
of carbon-based life; hence, studying life as what-it-could-be.
Digital Organisms Simulation Environment (DOSE) is essentially a virtual
world simulator for studying digital organisms. I will argue that digital
organisms are considered living organisms (Koh and Ling, 2013). Despite so,
being a molecular biologist by training, I have a hard time mapping
components of digital organisms into biological life whenever such
components are too abstract.
Hence, I decided to design an artificial life / digital organism simulator
that bears resemblance to biological life and ecology. These are the
foundation 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 Source
Codes 2: 6.
This manuscript describes the implementation of a GA framework that uses
biological hierarchy - from chromosomes to organisms to population.
2. Ling, MHT. 2012. An Artificial Life Simulation Library Based on Genetic
Algorithm, 3-Character Genetic Code and Biological Hierarchy. The Python
Papers 7: 5.
Genetic algorithm (GA) is inspired by biological evolution of genetic
organisms by optimizing the genotypic combinations encoded within each
individual with the help of evolutionary operators, suggesting that GA
may be a suitable model for studying real-life evolutionary processes.
This paper describes the design of a Python library for artificial life
simulation, Digital Organism Simulation Environment (DOSE), based on GA
and biological hierarchy starting from genetic sequence to population.
A 3-character instruction set that does not take any operand is
introduced 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 is
introduced to simulate a physical ecosystem.
- Ling, MHT. 2012. Ragaraja 1.0: The Genome Interpreter of Digital
Organism Simulation Environment (DOSE). The Python Papers Source Codes
4: 2.
This manuscript describes the implementation and test of Ragaraja
instruction set version 1.0, which is the core genomic interpreter of
DOSE.
From this foundation, the complete suite of Digital Organisms Simulation
Environment (DOSE) can be build.
Project website: U{https://github.com/mauriceling/dose}
Documentation can be found at http://maurice.vodien.com/project-dose
License:
Unless otherwise specified, dose.coapds package will be licensed
under Python Software Foundation License version 2; all other files will
be licensed GNU General Public License version 3.
Copyright 2010-2014, Maurice HT Ling (on behalf of all authors).
