Rita Pizzi
Department of Information Technology
Università degli Studi di Milano
ARTIFICIAL
NEURAL NETWORKS
IDENTIFY THE DYNAMIC
ORGANIZATION OF
MICROTUBULES AND
TUBULIN SUBJECTED
TO ELECTROMAGNETIC
FIELD
TUBULIN
Tubulin is a globular protein
and the fundamental
component of microtubules.

•Microtubules (MTs) constitute
the cytoskeleton of all the
eukaryotic cells and are
supposed to be involved in
many key cellular functions.
MICROTUBULES
•MTs are cylindrical polymers composed by
aligned tubulin dimers, alpha and beta-tubulins,
that polymerize in a helix that creates the
microtubule.
MICROTUBULES
•Their diameter is around 15 nm
and their length can vary from
some nm up to some
centimeters
•Many researchers claim they are
involved in the information
transmission among cells
MICROTUBULES
In past researches we tried to validate the

hypothesis that microtubules possess anomalous
properties due to their tubular structure
To this purpose we tested possible modifications

of their biophysical structures with two physical
measures: resonance and birefringence
CARBON NANOTUBE PROPERTIES
We studied the MT and Tubulin biophysical behavior in
presence of electromagnetic field, to assess if their
tubular structure could make them cavity antennas,
as carbon nanotubes (CNT) recently showed to be.
CARBON NANOTUBES PROPERTIES
• CNT have the same tubular structure and the same
dimensions as MTs
•
It was shown that they behave as antennas for
extremely high frequencies, receiving and
transmitting nanoscale waves
RESONANCE
Our experimental approach verified the existence of a
sharp mechanical resonance in MTs at a frequency
of 1510 MHz.
RESONANCE
The tubulin solution and the control solution did
not show any reaction.
This lack of response in tubulin and control solution can
be considered a hint that MT resonance was caused
by their molecular tubular structure.
BIREFRINGENCE
• Moreover we submitted MTs behavior to a
birefringence test.
• Birefringence is an optical property of materials that
arises from the interaction of light with oriented
molecular components.
BIREFRINGENCE
We submitted MT, tubulin and control solution to
electric and magnetic field
BIREFRINGENCE
• We measured birefringence of polarized light
crossing the MT, tubulin and control samples.
• We observed that MTs react to electromagnetic fields
in a different way than tubulin and control.
• Birefringence effect is always higher in MTs than in
tubulin and control, with statistical significance
• This suggests again that the molecular structure of
MTs could be the cause of their reaction to e-m
fields.
NANOTUBES AND BUCKYBALLS
In this study we considered for comparison carbon
nanotubes (CNT) and buckyballs (BB)
MT structure is very similar to the CNT one
The globular and regular structure of BB can be
compared to the tubulin structure
•In order to assess the
significance of these
findings we performed a
dynamic simulation of the
molecular structures of
tubulin, MT , CNT and BB
subjected to different levels
of e-m fields.
•We adopted the Ascalaph
simulation environment. It
allows simulations of large
molecular structures and
many parameterizations.
ASCALAPH
ASCALAPH
The tertiary structures were obtained by Protein
Data Bank and NANO_D INRIA group.
1° simulation: No electric field
2° simulation: A= 2 V/cm,
F= 90 Hz
3° simulation: A= 90 V/cm, F= 90 Hz
The structures were immersed in water at 298.15 °K
Simulation time: 7000 ps
ARTIFICIAL NEURAL NETWORKS
•The simulation results were submitted to two different
artificial neural networks.
•ANN are known to be effective non-linear classifiers
useful in case of complex patterns
•The xyz values of the molecules after simulation
(energy minimization) were chosen as input values of
the ANNs
•We adopted two different Artificial Neural Networks :
SONNIA and ITSOM
SONNIA
SONNIA is an ANN environment useful in the field
of drug discovery and protein prediction. Performs
both supervised and unsupervised learning.
e
Its output are a set of colored
boxes, one for each
competitive neuron (in case of SOM).
Each box represents :
•Occupancy: number f patterns mapped onto
the same neuron, i.e. similarities in the input
domain
•Conflicts, i.e. neurons that refer to inputs
belonging to different classes.
ITSOM
•ITSOM is an evolution of the Kohonen SOM.
•The sequence of its winning neurons form
series of numbers that repeat “nearly”
periodically, i.e. constitute cahotic attractors.
•Each attractor identifies the input pattern
univocally
•The graphical representation of the cahotic
attarctor gives the idea of the dynamical
organization of the pattern
SONNIA
TUBULIN
A=2V/cm F=90Hz
No field
Occupancy: 55
Conflict: 251
Occupancy: 51
A=90V/cm F=90Hz
Occupancy: 53
Conflict: 1020
Conflict: 384
SONNIA
MICROTUBULE
A=2V/cm F=90Hz
No field
Occupancy: 37
Conflict: 676
Occupancy: 35
A=90V/cm F=90Hz
Occupancy: 38
Conflict: 117
Conflict: 780
SONNIA
BUCKYBALL
NANOTUBE
No field
O: 8
C: 0
O: 7
C: 0
A=2V/cm F=90Hz
O: 4
C: 0
O: 6
C: 0
A=90V/cm F=90Hz
O: 7
C: 0
O: 13
C: 0
SONNIA
•At zero field, tubulin shows a high occupancy and conflicts
value.
•With weak electric field tubulin maintains the same
occupancy and conflicts.
•With higher electric field the numberr of conflicts grows, to
indicate that the structure organization decreases.
•In absence of field the MT show a more restricted occupancy
than tubulin, indicating a compactness in their spatial structure.
• No changes with a weak electric field.
•With a stronger electric field the conflicts decrease.
•BB and NT show low occupancy and conflict values, due to a
low number of components and to their extremely regular
structure.
•Although NT structure is bigger than BB, occupancy is low to
indicate its strong stability.
ITSOM
Grafici in MatLab
A=90V/cm F=90Hz
A=2V/cm F=90Hz
No field
Tubulin
A=90V/cm F=90Hz
A=2V/cm F=90Hz
No field
ITSOM
Microtubule
ITSOM
Nanotube
Buckyball
Senza campo
A=2V/cm F=90Hz
A=90V/cm F=90Hz
CONCLUSIONS
The dynamical attractors generated by ITSOM reach analogous
conclusions. Tubulin creates a steady attractor without field , that tends
to become less structured in presence of field.
MTs show the same strong organization as tubulin in absence of field, but
on the contrary, their attractors tend to become more compact in presence
of electric field, concentrating into a restricted configuration space (after a
transient).
Buckyballs don’t change their regular behavior in presence of
electric field
NTs increase their spatial occupancy, but show an interesting
increase of regularity in presence of field
CONCLUSIONS
•The results obtained by SONNIA and ITSOM highlight
the same behavior observed during the Ascalaph
dynamical simulation.
•In fact BBs and CNTs have an axial movement that
becomes a regular pulse in presence of electric field.
•Tubulin instead seems to have some internal forces that
tend to oppose to a dynamical stabilization, and does not
show any special behavior in presence of electric field.
CONCLUSIONS
•The MT, that without field tends to move off its
starting position, after the application of field tends to
return to its initial position and stabilize.
•The interesting behavior of MT in presence of electric
field confirms our findings about the electrical behavior
of MTs.
•In spite of their structural complexity, MTs show a
strong dynamic stability, that is significantly increased
by the electric field.
•This effect is in part present also in CNTs And
confirms their known electrical properties.
CONCLUSIONS
•These results confirm the experimental
biophysical findings and motivate us to
deepen our research on the structural
properties of MTs.
•This Artificial Intelligence approach can
help explain the experimental evidences at
a microcopic level, allowing a more correct
interpretation of these findings.
TUBULIN
MICROTUBULE
CNT SIMULATION
No field
90 V/cm field