Main menu

**Cellular
computing and information processing in microtubules and cytoskeleton
**

Many years ago (1990-

**Top photo** (right corner, picture
taken in 1991. From left to right) .-

**Bottom caption**

Also we conducted a theoretical model [8] 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-

*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 the cause of wave function collapse,
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
[27] 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 [3] (vol. 1(1): 61-

**From
cytoskeleton to neural networks **

The study of neuronal cytoskeleton led us to study how neurons modulate the strength of
the synapse [10]. Using the neural network of Aplysia we constructed an artificial neural network [11] in which the weight of the connections between
neurons was obtained from numerous molecular and cellular mechanisms.

**One more
step: Bacterial computing**

The capability
to establish adaptive relationships with the environment is an
essential characteristic of living cells. Both bacterial computing [26] and bacterial intelligence [15] 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 [26] we reviewed the possible application of both kinds of
mechanisms to prokaryotic molecular computing [23] schemes as well as to the solution of real world
problems [25].