From what I understand, DNA computing had some interesting applications, but I doubt it was useful for most problems, meaning maintaining funding
would be difficult. The machines to process DNA aren't cheap. Biological samples are time consuming to handle. DNA computers were originally well
known for massively parallel processing a single problem.
Regarding a biocomputer that uses ATP, I assume that mimicking cellular respiration to regenerate ADP into ATP is probably difficult. When ATP is
used, it turns into ADP, if you didn't know. Running a Biological computer for an extended period of time would probably be hard without a massive
pre-made source of ATP. Admittedly, I've read very little on the topic.
I did find one negative critique of biocomputers:
Here is an article from a couple years ago explaining newer approaches with DNA computing:
Research is ongoing in these fields, but they're very niche.
The good news is that computer processing is already down to around 7 nanometers. DNA is about 2 nanometers wide. We are naturally getting down to
the size of DNA and molecular computers.
Also, bioinformatics is a growing field where computer scientists, statisticians, mathematicians, biologists, and geneticists get together to learn
how problems are solved by biological systems, such as DNA and proteins. So we are developing better methods for classical computers to use, without
needing to change classical computers.
If I were to guess, we might see the introduction of neural circuits soon. These circuits will allow computers to tackle different types of problems
with greater efficiency; plus they already work. So it is really a matter of bringing down the cost.
Specifically, these help build neural networks for developing A.I. programs. They are like a primitive version of neurons in our brain. Training
A.I. computers is very time consuming, but the need for A.I. programming is on the rise. If this technology became more widespread, it would probably
significantly reduce time to develop A.I. software, which would have huge consumer and business implications. Think of doing complex processing on
video in real-time. It could speed up the advent of Alternate Reality (AR) devices, automate common business processes more easily, and open up new
applications and technologies that we haven't yet thought of. I also assume that companies like Microsoft and Apple would probably automatically
utilize this technology to increase the speed of certain applications, such as recognizing faces.
So, in summary, bio computing, of various types, hasn't really gone dark. They are niche fields with high costs and limited payoff. Most likely, the
research will slowly be integrated into classical computers, giving them a needed boost for specialized tasks. The introduction of new materials,
like carbon nanotubes and fiber optics will continue to enrich classical computers so that they can shrink and perform at higher speeds and with less
power, closer mimicking biological computing. DARPA is involved. SyNAPSE is a DARPA project.
it wants to create novel computing platforms using new concepts like optical engineering, meta-materials and even DNA computing. The goal is to create
a new breed of CPUs that would outperform current tech for specialized problems but still fit into modern computer architectures. If successful, such
a device "may enable revolutionary new simulation capabilities for design, prediction, and discovery," according to DARPA.
edit on 2017-2-25 by Protector because: Tying in to original topic