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Geo212
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Is it possible,at least theoretically, to take digitised DNA and produce a computer simulation of the organism that it came from?
Folding at home does exactly that. It takes time and some manual tuning, but it is possible.Ryan_m_b said:For the moment we can't even model the folding of a single protein
mfb said:Folding at home does exactly that. It takes time and some manual tuning, but it is possible.
Geo212 said:Is it possible,at least theoretically, to take digitised DNA and produce a computer simulation of the organism that it came from?
Geo212 said:Is it possible,at least theoretically, to take digitised DNA and produce a computer simulation of the organism that it came from?
Pythagorean said:When you get too reductionist, you get problems like "over fitting".
Torbjorn_L said:No.
The reason is that DNA isn't an atomic description of "what goes here and what goes there". It is a recipe for controling and maintaining an already functioning organism, and it relies on a preexisting cellular machinery (from the ovum) and an environment that directs development from cellular levels and up.
If you already can model the rest of the organism from a subcellular level, sure. Then the DNA (or at least its genome) adds the missing functions (as described above).
I guess I don't understand this. As I understand it you simply can't be "too reductionist".
Rather, due to emergent behavior it becomes practically impossible to pick apart some systems. For an example:
"This paper illustrates the use of the nonparametric Wald-Wolfowitz test to detect stationarity and ergodicity in agent-based models. A nonparametric test is needed due to the practical impossibility to understand how the random component influences the emergent properties of the model in many agent-based models."
But note that the test is "reductionist", i.e. informed of the system state with respect to the studied behavior:
"Knowledge of the basic properties of the artificial time series is essential to reach a correct interpretation of the information which can be extracted from the model. To acquire such knowledge it is important to perform statistical testing of the properties of the artificial data (Leombruni and Richiardi 2005; Richiardi et al. 2006). Supposing that an agent-based model has a statistical equilibrium, defined as a state where some relevant statistics of the system are stationary (Richiardi et al. 2006) the stationarity test can help in detecting it."
[ http://jasss.soc.surrey.ac.uk/15/2/7.html ]
Other problems with systems are stuff like degeneracy. But that is part of the physics studied.
Pythagorean said:When it comes to modeling, you can certainly be too reductionist (and, conversely, too general). It's a matter of practicality, not a slight on reductionism:
http://en.wikipedia.org/wiki/Overfitting
Pythagorean said:When it comes to modeling, you can certainly be too reductionist (and, conversely, too general). It's a matter of practicality, not a slight on reductionism:
http://en.wikipedia.org/wiki/Overfitting
The basic idea is that if you're studying something complex (like fluids) you use abstractions like the Reynold's number, pressure, temperature, and other group descriptions, rather than trying to model each particle in the ensemble. Not that the particle view isn't valid, but that it's not practical in a computer simulation.
Torbjorn_L said:I understand it as that overfitting has nothing to do with practical "reductionism" (a philosophic term) as a system composed of its parts and how to use that to advantage (an actual usage), but is a problem of statistic modeling.
I agree with the rest of course, as it was much of what my comment tried to describe (in a longer format).
Computer simulation of an organism is the process of creating a digital model of an organism and using algorithms to simulate its behavior and interactions with its environment.
The purpose of computer simulation of an organism is to understand the complex biological processes and behaviors of living organisms, and to make predictions about how they may respond to different environments and stimuli.
Computer simulation of an organism is different from other types of computer simulations because it involves modeling the behaviors and interactions of a living, biological system rather than a physical or engineered system.
Computer simulation of an organism can be used to simulate any living organism, from single-celled microorganisms to complex multicellular organisms.
The potential benefits of computer simulation of an organism include gaining a better understanding of biological systems, predicting the effects of environmental changes on organisms, and potentially aiding in the development of new treatments for diseases or disorders.