Cellular computing and information processing in microtubules and cytoskeleton
Many years ago (1990-
Top photo (1991. From left to right) .-
Bottom caption .-
We related the
above model with neural networks in the context of currently recognized
cellular structures within neurons. Neural network models and paradigms
require adaptation of synapses for learning to occur in the network.
Some models of learning paradigms require information to move from axon
to dendrite. This motivated us to examine the possibility of
intracellular signaling to mediate such signals. The cytoskeleton forms
a substrate for intracellular signaling via material transport and
other putative mechanisms. Furthermore, many experimental results
suggest a link between the cytoskeleton and cognitive processing. In
these papers [5, 6, 9] we review research on intracellular signaling in
the context of neural network learning. The work justifies a possible
role of microtubules in the learning rule back-
Also we conducted a theoretical model  for molecular computing in which Boolean logic was implemented in parallel networks of individual MTs interconnected by MAPs. Conformational signals propagate on MTs as in data buses and in the model MAPs are considered as Boolean operators, either as bit-
A possible impact of the Hameroff's theories was published in 1993 in two chapters [6, 7] of the book Rethinking Neural Networks: Quantum Fields and Biological Data (Eds. K.H. Pribram y Sir J. Eccles).
Modeling and simulation of quantum coherent superposition and decoherence in cytoskeletal microtubules
Although experimental evidence suggests the influence of quantum effects in living organisms, one of the most critical problems in quantum biology is the explanation of how those effects that take place in a microscopic level can manifest in the macroscopic world of living beings. At present, quantum decoherence associated with the wave function collapse is one of the most accepted mechanisms explaining how the classical world of living beings emerges from the quantum world. Whatever is the cause of wave function collapses, there exist biological systems where a biological function arises as a result of this collapse (e.g. birds navigation, plants photosynthesis, sense of smell, etc.), as well as the opposite examples (e.g. release of energy from ATP molecules at actomyosin muscle) where a biological function takes place in a quantum coherent environment. In this paper  we report the modelling and simulation of quantum coherent superposition in cytoskeletal microtubules including decoherence, thus the effect of the collapse of the microtubule coherent state wave function. Our model is based on a new class of hybrid cellular automata (QvN), capable of performing as either a quantum cellular automata (QCA) or as a classical von Neumann automata (CA). These automata are able to simulate the transition or reduction from a quantum microscopic level with superposition of several quantum states, to a macroscopic level with a single stable state. Our results illustrate the significance of quantum biology explaining the emergence of some biological functions. We believe that in the future quantum biology will have a deep effect on the design of new devices, e.g. quantum hardware, in electrical engineering.
Even when Nanobiology disappeared long ago, it was a very exciting journal. In 1992 we published a paper  (vol. 1(1): 61-
It was a good time, not only because we were young, but by the lively atmosphere and intellectually exciting environment in Tucson with Stuart Hameroff.
From cytoskeleton to neural networks
The study of neuronal cytoskeleton led us to study how neurons modulate the strength of the synapse . Using the neural network of Aplysia we constructed an artificial neural network  in which the weight of the connections between neurons was obtained from numerous molecular and cellular mechanisms.
One more step: Bacterial computing
to establish adaptive relationships with the environment is an
essential characteristic of living cells. Both bacterial computing  and bacterial intelligence 
are two general traits manifested along adaptive behaviors that
respond to surrounding environmental conditions. These two traits have
generated a variety of theoretical and applied approaches. Since the
different systems of bacterial signaling and the different ways of
genetic change are better known and more carefully explored, the whole
adaptive possibilities of bacteria may be studied under new angles. For
instance, there appear instances of molecular “learning”
along the mechanisms of evolution. More in concrete, and looking
specifically at the time dimension, the bacterial mechanisms of
learning and evolution appear as two different and related mechanisms
for adaptation to the environment; in somatic time the former and in
evolutionary time the latter. In this paper  we reviewed the possible application of both kinds of mechanisms to prokaryotic molecular computing  schemes as well as to the solution of real world problems .