Department of Computer Science
Universitas Studiorum Mediolanensis
Milan, Italy
Rita Pizzi
Exploring structural and dynami...
Aim of the project
In our previous experiments we found evidence of a
sensitivity of neurons to extremely weak magnetic fi...
Aim of the project
• Comparison between Microtubules and tubulin,
and between these structures and
nanotubes/buckyballs, t...
A particular biophysical behavior is functional to
some specific property of the studied material.
Observed differences be...
Tubulin
Tubulin is a globular
protein and the
fundamental component
of microtubules.
Microtubules (MTs)
constitute the
cyt...
Microtubules are cylindrical polymers composed by
aligned tubulin dimers, alpha and beta-tubulins, that
polymerize in a he...
MTs could have optical,
electrical and quantum
properties that might explain
long-distance intracellular
communication pro...
Carbon nanotubes (CNT) have the same tubular
structure and the same dimensions as MTs
Buckyballs (BB) have a globular stru...
Carbon Nanotubes (CNT) and Buckyballs (BB)
• A fullerene is any molecule composed entirely of carbon, in
the form of a hol...
Carbon Nanotubes (CNT) and Buckyballs (BB)
• Nanotubes (CNTs) are cylindrical fullerenes. These tubes of carbon
are usuall...
Carbon Nanotubes (CNT) and Buckyballs (BB)
Electromagnetic Wave absorption
•One of the more recently researched properties...
Antennas and Resonance
•Antennas transform an electromagnetic field into an electric signal or
viceversa.
•When fed by an ...
Antennas and Resonance
Our hypothesis is that MTs can behave as oscillators as well as CNTs do,
becoming superreactive rec...
Antennas and Resonance
• The first antenna is connected to a Microwave Signal Generator (Polarad
mod. 1105) generating fre...
Antennas and Resonance
The experimental results are the following:
With tubulin and control samples no changes were detect...
The fact that the control buffer did not affect the
reference signal peak means that the observed effects
depend exclusive...
Birefringence
A polarimeter is a scientific instrument used to measure the angle of
rotation caused by passing polarized l...
Birefringence
A Nicol prism consists of a rhombohedral crystal of Iceland spar (a
variety of calcite) that has been cut at...
Birefringence
•The experiment was carried out at the Department of Physics of our
University.
•A polarimeter was prepared ...
A : He-Neon Laser (Hughes 3222H-P, 633 nm; np 5 nW max); Nicol polarizer;
beam splitter per
B : Cuvette and coil, 610.1 Hz...
Birefringence
We applied magnetic and electric field to
evaluate the sensitivity of the test solutions to
the fields measu...
Birefringence
We executed four different test sessions, preparing 4 different cuvettes
containing:
• Tubulin in tubulin bu...
Birefringence
•For each test the polarimeter measures the current coming to the
photodiode: in presence of scattering due ...
EF TMF LMF NF
610Hz 632Hz 610Hz 632Hz 610Hz 632Hz 610Hz 632Hz
Mt in MT buffer 927.41 24.75 1257.95 101.44 1232.30 2253.10 ...
EF TMF LMF NF
610Hz 632Hz 610Hz 632Hz 610Hz 632Hz 610Hz 632Hz
Mt in MT buffer 1141.8 27.2 1547.0 185.0 1517.5 2628.3 1412....
The tabled values have been normalized with respect to
the reference value (632 value/610 value).
Tab. A EF Tab. B EF Tab....
Tab. A TMF Tab. B TMF Tab. C TMF Tab. D TMF
MT in MT
buffer
0.0810 0.0837 0.0766 0.0781
Tb in MT
buffer
0.0996 0.1018 0.09...
LONGITUDINAL MAGNETIC FIELD
With longitudinal magnetic field the solution with MTs has
always a value that is minor than b...
Tab. X NF Tab. Y NF Tab. Z NF Tab. K NF
Mt in MT buffer 0.00860 No peak in 632 0.00389 0.01069
Tb in MT buffer 0.00285 No ...
Statistical Analysis
• Given the substantial equivalence between parameterizations, the
statistical analysis was performed...
Statistical Analysis
Results
MTs react to electromagnetic fields in a different way than tubulin
and control sample: birefringence effect is al...
Conclusions
The experimental results confirm the working hypothesis
that Microtubules could be the structure inside neuron...
Computational Analysis
C o m p u t a t i o n a l A n a l y s i s
Synergetic use of different computational methods to vali...
Molecular Dynamics software
Ascalaph
- Very flexible tool with many possible
parameterizations for the force fields
- Vari...
Tertiary structures of MTs and tubulin obtained from Protein Data
Bank (PDB) and NANO_D INRIA group
Tertiary structures of...
TUBULIN MICROTUBULES
Tubulin and Microtubules Simulation
CNT Simulation
No Field 90 V/cm field
CNT Simulation
BBs show to be insensible the to electric field.
CNTs tend to move with a dynamic axial motion, which
becom...
Artificial Neural Networks
C o m p u t a t i o n a l A n a l y s i s
Simulation results were submitted to two different se...
C o m p u t a t i o n a l A n a l y s i s
Artificial Neural Networks
A Self-Organizing Map is an Artificial Neural Network...
C o m p u t a t i o n a l A n a l y s i s
Artificial Neural Networks
Any Artificial Neural Network can be considered as a ...
SONNIA
C o m p u t a t i o n a l A n a l y s i s
SONNIA is a computational environment for the development and analysis of...
C o m p u t a t i o n a l A n a l y s i s
SONNIA
Two parameters were represented:
Occupancy
number of patterns mapped onto...
Occupancy:55 Conflict:251
No Field
Occupancy: 51 Conflict: 384
EF=2V/cm f=90Hz
EF=90V/cm f=90Hz
Occupancy:37 Conflict:676
...
BUCKYBALL NANOTUBES
O: 8 C: 0
C: 0O: 7 O: 13 C: 0
O: 6 C: 0
O: 7 C: 0
No Field
EF=2V/cm f=90Hz
EF=90V/cm f=90Hz
C: 0O: 4
B...
ITSOM network
C o m p u t a t i o n a l A n a l y s i s
ITSOM is an evolution of Kohonen SOM, that highlight the chaotic
d...
ITSOM network
C o m p u t a t i o n a l A n a l y s i s
The sequence of winning neurons forms a series of numbers that are...
BUCKYBALL NANOTUBES
No Field
EF=2V/cm f=90Hz
EF=90V/cm f=90Hz
Buckyballs:
Behavior not
modified by the
electric field
Nano...
No field
EF=2V/cm f=90Hz
EF=90V/cm f=90Hz
Tubulin
Generates a stable
attractor in absence of
field, that tends to become
l...
The MD simulation shows that BBs are insensible the to electric field,
as confirmed also by the Artificial neural Networks...
Tubulin, despite its symmetric structure, seems to have
internal forces that tend to resist a dynamic stabilization,
and i...
Conclusions and Future Plans
C o n c l u s i o n s
The computational methods showed to be valuable for the analysis
of com...
Conclusions and Future Plans
C o n c l u s i o n s
•The evidence of a specific behavior of MTs in presence of
electromagne...
R. Pizzi, S. Fiorentini, G. Strini, and M. Pregnolato. “Exploring Structural
and Dynamical Properties of Microtubules by M...
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Exploring structural and dynamical properties of microtubules by means of Artificial Neural Networks

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  • Tubulin is a globular protein and the fundamental component of microtubules. Microtubules constitute the cytoskleton of all the eukaryotic cells and are supposed to be involved in many key cellular functions. In particular, many researchers claim they are involved in the information transmission among cells
  • MT are cylindrical polymers composed by aligned tubulin dimers, alpha and beta-tubulins, that polymerize in a helix that creates the microtubule., that is empty inside. Their diameter is around 15 nm and their length can vary from some nm up to some centimeters.
  • Tubulin is a globular protein and the fundamental component of microtubules. Microtubules constitute the cytoskleton of all the eukaryotic cells and are supposed to be involved in many key cellular functions. In particular, many researchers claim they are involved in the information transmission among cells
  • In past researches we studied their biophysical behavior in presence of electromagnetic field, in order to assess if their tubular structure could make them cavity antennas, as carbon nanotubes recently showed to be. CNT have the same tubular structure and the same dimensions as MTs, and it was shown that they behave as antennas for extremely high frequencies, receiving and transmitting nanoscale (550 nm, 500 GHz, ) waves.
  • Stabilized microtubules (#MT001-A), tubulin (#TL238), taxol (# TXD01), GTP (#BST06) and General Tubulin Buffer (# BST01) are supplied by Cytoskeleton Inc. Denver, CO. USA.  Preparation of buffer for microtubule: MTs resuspension buffer is obtained by adding 100 μl of 2mM taxol stock in dry DMSO to 10 ml of room temperature PM buffer (15 mM PIPES pH 7.0, 1 mM MgCl2). It is important to make sure that PM buffer is at room temperature as taxol will precipitate out of solution if added to cold buffer. Resuspended taxol should be stored at -20 °C.  Preparation of tubulin buffer: GTP stock solution (100mM) is added to General Tubulin Buffer (80 mM PIPES pH 6.9, 2 mM MgCl2, 0.5 mM EGTA) at a final concentration of 1mM GTP. The tubulin buffer will be stable for 2-4 hours on ice.  Microtubules Reconstitution. 1 ml of buffer MT is added to 1 mg of lyophilized MTs and mixed gently. Resuspended MTs are left at room temperature for 10–15 minutes with occasional gentle mixing. The MTs are now ready to use. They are at a mean length of 2 μm and the tubulin concentration is 1mg/ml. MTs will be stable for 2-3 days at room temperature, although it should be noted that the mean length distribution will increase over time. MTs can be snap frozen in liquid nitrogen and stored at -70 °C.  Tubulin Reconstitution. 1 mg of lyophilized tubulin is resuspended in 1 ml of buffer T at 0-4 °C (final tubulin concentration is 1 mg/ml). The reconstituted tubulin solution is not stable and needs to be used soon after its preparation.
  • Grafin calsait
  • Our experimental approach verified the existence of mechanical resonance in MTs at a frequency of 1510 MHz, whereas 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. Moreover we analyzed the MTs behavior in birefringence conditions. Birefringence is an optical property of materials that arises from the interaction of light with oriented molecular components. We submitted MT, tubulin and control solution to either tranverse electric field and longitudinal and transverse magnetic field, and measured birefringence of polarized light under controlled conditions. We observed that MTs react to electromagnetic fields in a different way than tubulin and control. In particular, electric field and longitudinal magnetic field show opposite effects in MTs and tubulin. Birefringence effect is always higher in MTs than in tubulin and control, with statistical significance, and this suggests again that the molecular structure of MTs could be the cause of their reaction to e-m fields.
  • In order to assess the significance of these findings we performed a dynamic simulation of the molecular structures of tubulin and MT subjected to different levels of e-m fields. We also compared them with CNT and BB structures. We adopted the Ascalaph simulation environment. It allows simulations of large molecular structures and many parameterizations.
  • In order to assess the significance of these findings we performed a dynamic simulation of the molecular structures of tubulin and MT subjected to different levels of e-m fields. We also compared them with CNT and BB structures. We adopted the Ascalaph simulation environment. It allows simulations of large molecular structures and many parameterizations.
  • MT show instead a peculiar behavior.Their spatial organization is stronger than tubulin alone even in absence of field. The presence of electric field causes a decrease of conflicts, indicating a better structural organization, confirmed both by SONNIA and by the ITSOM attractors, that show a high regularity and compactness. The same regularity is shown in the NT attractors in presence of field. In spite of their structural complexity, MTs show in summary a strong dunamic stability, that is significantly increased by the electric field. These results confirm the experimental biophysical findings and motivate us to deepen our reasearch on the structural properties of MTs.. This computational apporsch can help to explain the experimental evidences at a microcopic level, allowing a more correct interpretation of these findings.
  • SONNIA is an ANN environment useful in the field of drug discivery and protein prediction. Performs both supervised and unsupervised learning. Its output are a set of colored bowes, 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 inpout domain) Conflict, ie neurons that refer to inputs belonging to different classes. ITSOM is the other adopted ANN . It is an evolution of the SOM developed by our group, that allows to recod the series of winning neurons. Each series represents a chaotic attractor that repat “ nearly exactly, and whose values identify univocally the input pattern. With MATLAB simulink we represented the attractors in the state space, in order to evaluate their shape and size. An attractor is a generalization of the steady state, and represents the trajectory of the system in a portion of state space where it is attracted.
  • A neural network can be considered as a dynamic system of n-dimensional differential equations describing the dynamics of n neurons. Each neuron is mathematically defined by its state x (i) and by its gain function gi=gi(xi) differentiable everywhere and not decreasing. A typical gain function is for example the logistic function g(x) = (1 + e -x) -1
  • A neural network can be considered as a dynamic system of n-dimensional differential equations describing the dynamics of n neurons. Each neuron is mathematically defined by its state x (i) and by its gain function gi=gi(xi) differentiable everywhere and not decreasing. A typical gain function is for example the logistic function g(x) = (1 + e -x) -1
  • SONNIA is an ANN environment useful in the field of drug discivery and protein prediction. Performs both supervised and unsupervised learning. Its output are a set of colored bowes, 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 inpout domain) Conflict, ie neurons that refer to inputs belonging to different classes. ITSOM is the other adopted ANN . It is an evolution of the SOM developed by our group, that allows to recod the series of winning neurons. Each series represents a chaotic attractor that repat “ nearly exactly, and whose values identify univocally the input pattern. With MATLAB simulink we represented the attractors in the state space, in order to evaluate their shape and size. An attractor is a generalization of the steady state, and represents the trajectory of the system in a portion of state space where it is attracted.
  • In zero field condition tubuin shows a high occupancy and conflicts value. Stabilization was reached with most difficulty , and this means a lack of native organization. By applying a weak electric field tubulin maintains the same occupancy and conflicts rate. But with a higher electric field the numebr of conflict increases its value, to indicate that the structure organization decreases.
  • BB and NT show low occupancy and conflict values, due to a low number of compnents with respect to the network size and to their extremely regular structure. Although NT structure is bigger than BB, occupancy is low to indicate its strong stability, that does not change with electric field.
  • SONNIA is an ANN environment useful in the field of drug discivery and protein prediction. Performs both supervised and unsupervised learning. Its output are a set of colored bowes, 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 inpout domain) Conflict, ie neurons that refer to inputs belonging to different classes. ITSOM is the other adopted ANN . It is an evolution of the SOM developed by our group, that allows to recod the series of winning neurons. Each series represents a chaotic attractor that repat “ nearly exactly, and whose values identify univocally the input pattern. With MATLAB simulink we represented the attractors in the state space, in order to evaluate their shape and size. An attractor is a generalization of the steady state, and represents the trajectory of the system in a portion of state space where it is attracted.
  • SONNIA is an ANN environment useful in the field of drug discivery and protein prediction. Performs both supervised and unsupervised learning. Its output are a set of colored bowes, 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 inpout domain) Conflict, ie neurons that refer to inputs belonging to different classes. ITSOM is the other adopted ANN . It is an evolution of the SOM developed by our group, that allows to recod the series of winning neurons. Each series represents a chaotic attractor that repat “ nearly exactly, and whose values identify univocally the input pattern. With MATLAB simulink we represented the attractors in the state space, in order to evaluate their shape and size. An attractor is a generalization of the steady state, and represents the trajectory of the system in a portion of state space where it is attracted.
  • Buckyballs don’ t change their regular behavior in presence of electric field, whereas NTs increase their spatial occupancy, but show an interesting increase of regularity in presence of field.
  • 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.
  • MT show instead a peculiar behavior.Their spatial organization is stronger than tubulin alone even in absence of field. The presence of electric field causes a decrease of conflicts, indicating a better structural organization, confirmed both by SONNIA and by the ITSOM attractors, that show a high regularity and compactness. The same regularity is shown in the NT attractors in presence of field. In spite of their structural complexity, MTs show in summary a strong dunamic stability, that is significantly increased by the electric field. These results confirm the experimental biophysical findings and motivate us to deepen our reasearch on the structural properties of MTs.. This computational apporsch can help to explain the experimental evidences at a microcopic level, allowing a more correct interpretation of these findings.
  • MT show instead a peculiar behavior.Their spatial organization is stronger than tubulin alone even in absence of field. The presence of electric field causes a decrease of conflicts, indicating a better structural organization, confirmed both by SONNIA and by the ITSOM attractors, that show a high regularity and compactness. The same regularity is shown in the NT attractors in presence of field. In spite of their structural complexity, MTs show in summary a strong dunamic stability, that is significantly increased by the electric field. These results confirm the experimental biophysical findings and motivate us to deepen our reasearch on the structural properties of MTs.. This computational apporsch can help to explain the experimental evidences at a microcopic level, allowing a more correct interpretation of these findings.
  • Exploring structural and dynamical properties of microtubules by means of Artificial Neural Networks

    1. 1. Department of Computer Science Universitas Studiorum Mediolanensis Milan, Italy Rita Pizzi Exploring structural and dynamical properties of microtubules by means of Artificial Neural Networks
    2. 2. Aim of the project In our previous experiments we found evidence of a sensitivity of neurons to extremely weak magnetic fields. We aimed to verify if this sensitivity could be due to Microtubules. We prepared ad hoc experimental procedures to test the reaction of Microtubules and tubulin to electromagnetic fields: •Resonance •Birefringence
    3. 3. Aim of the project • Comparison between Microtubules and tubulin, and between these structures and nanotubes/buckyballs, that have similar structure and dimension and interesting optical, electrical and quantum properties. • Synergetic use of computational methods for the analysis of data from the biophysical experiments, aiming at the understanding of the anomalous properties of microtubules.
    4. 4. A particular biophysical behavior is functional to some specific property of the studied material. Observed differences between samples of tubulin and MTs under controlled biophysical conditions suggests that the structural configuration of MTs could be the reason of such differences and could be suitable for specific cellular functionalities. Working Hypothesis
    5. 5. 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 M a t e r i a l s
    6. 6. Microtubules are cylindrical polymers composed by aligned tubulin dimers, alpha and beta-tubulins, that polymerize in a helix that creates the microtubule Microtubules (MT) M a t e r i a l s
    7. 7. MTs could have optical, electrical and quantum properties that might explain long-distance intracellular communication processes MTs diameter is around 15 nm and their length can vary from a few nm up to some centimeters Microtubules (MT)
    8. 8. Carbon nanotubes (CNT) have the same tubular structure and the same dimensions as MTs Buckyballs (BB) have a globular structure that can be compared to the tubulin structure Carbon Nanotubes (CNT) and Buckyballs (BB)
    9. 9. Carbon Nanotubes (CNT) and Buckyballs (BB) • A fullerene is any molecule composed entirely of carbon, in the form of a hollow sphere, ellipsoid or tube. Spherical fullerenes are also called buckyballs (C60). • The structure of C60 is a truncated icosahedron, which resembles an association football ball of the type made of twenty hexagons and twelve pentagons, with a carbon atom at the vertices of each polygon and a bond along each polygon edge. • Buckyballs have been used by the Zeilinger’s group as the biggest structures that show a quantum wave behavior in a double-slit experiment with a source of single buckyballs. • The van der Waals diameter of a C60 molecule is about 1.1 nanometers (nm).
    10. 10. Carbon Nanotubes (CNT) and Buckyballs (BB) • Nanotubes (CNTs) are cylindrical fullerenes. These tubes of carbon are usually only a few nanometres wide, but they can range from less than a micrometer to several millimeters in length, and are 0.4-1 nm in diameter. • Because of the symmetry and unique electronic structure of graphene, the structure of a nanotube strongly affects its electrical properties. For a given (n,m) nanotube, if n = m, the CNTs behave as a conductor; if n>m they are semiconductors. • Because of their nanoscale cross-section, electrons propagate only along the tube's axis and electron transport involves quantum effects. CNTs are considered one-dimensional conductors that carry single electrons, so that their quantum conductance is easily measurable thanks to the Heisenberg principle. • Other researches report intrinsic superconductivity in CNTs
    11. 11. Carbon Nanotubes (CNT) and Buckyballs (BB) Electromagnetic Wave absorption •One of the more recently researched properties of carbon nanotubes is their wave absorption characteristics, specifically microwave absorption. •The narrow selectivity in the wavelength makes nanotubes properties extremely useful in photonics technologies. •It has been shown that CNT behave as antennas for extremely high frequencies, receiving and transmitting nanoscale waves
    12. 12. Antennas and Resonance •Antennas transform an electromagnetic field into an electric signal or viceversa. •When fed by an electrical signal they absorb it and return it in the shape of electromagnetic waves (transmitting antennas), or absorb energy from an electromagnetic wave and generate a voltage to their ends (receiving antennas). •Any conductive object behave as an antenna, and if it is tubular and the frequency corresponds to the resonance frequency, it resonates mechanically (cavity antenna) amplifying the signal. •Oscillations increase their extent and this corresponds to an increase of energy within the oscillator. •Resonance is a physical condition that occurs when a damped oscillating system is subjected to a periodic solicitation with a frequency equal to the system oscillation.
    13. 13. Antennas and Resonance Our hypothesis is that MTs can behave as oscillators as well as CNTs do, becoming superreactive receivers and trasmitters able to amplify the signals. •After preparing MT and tubulin in suitable buffers (Taxol for MTs and General Tubulin Buffer for tubulin), and control solutions for both MT and tubulin, we started the resonance experiment. •Two dipole antennas (1/4 wave) are spaced 1.6 in., and the test tube with the MT, tubulin and control solutions are in turn put in a mu-metal box between the antennas.
    14. 14. Antennas and Resonance • The first antenna is connected to a Microwave Signal Generator (Polarad mod. 1105) generating frequencies between 0.8 and 2.5 GHz. The second antenna is connected with a Spectrum Analyzer (Avantest mod. TR4131). • If the peak of the tested material results lower in amplitude than the resonance reference peak , the sample is absorbing, if it is higher the sample is emitting electromagnetic energy.
    15. 15. Antennas and Resonance The experimental results are the following: With tubulin and control samples no changes were detected in the signal amplitude. In the MT sample analysis we observed at 1510 MHz a sharp (0.3 dB) lowering of the reference peak (absorption), and another lowering between 2060 and 2100 MHz. It is possible that the observed peak is related to a harmonic frequency of the main higher resonance characteristic frequency, that depends on the MT dimensions.
    16. 16. The fact that the control buffer did not affect the reference signal peak means that the observed effects depend exclusively on the molecular structure contained in the sample The MT tubular structure can be responsible for the observed variation of the signal Antennas and Resonance
    17. 17. Birefringence A polarimeter is a scientific instrument used to measure the angle of rotation caused by passing polarized light through an optically active substance. Some chemical substances are optically active, and polarized (unidirectional) light will rotate either to the left (counter-clockwise) or right (clockwise) when passed through these substances. The amount by which the light is rotated is known as the angle of rotation. There are different kinds of polarimeter. The most classical is the Nicol prism-based polarimeter, based on the birefringence properties of the Nicol prism. Birefringence is an optical property of materials that arises from the interaction of light with oriented molecular and structural components.
    18. 18. Birefringence A Nicol prism consists of a rhombohedral crystal of Iceland spar (a variety of calcite) that has been cut at an angle of 68° with respect to the crystal axis, cut again diagonally, and then rejoined as shown using, as a glue, a layer of transparent Canada balsam. Unpolarized light enters through the left face of the crystal, as shown in the diagram, and is split into two orthogonally polarized, differently directed, rays due to the birefringence property of the calcite.
    19. 19. Birefringence •The experiment was carried out at the Department of Physics of our University. •A polarimeter was prepared with a monochromatic source of light (633 nm) sent to two Nicol prisms that, for they birefringence properties, polarize it on two different planes. •The beam then crosses the cuvettes containing the control solution and the test solution which, if optically active, rotates the polarization planes of light. Then the light passes a rotable polarizing filter that by comparison detects the rotation angle. •Finally the beam is directed to a photodiode and sampled for a suitable signal analysis software.
    20. 20. A : He-Neon Laser (Hughes 3222H-P, 633 nm; np 5 nW max); Nicol polarizer; beam splitter per B : Cuvette and coil, 610.1 Hz , for the reference sample C : Cuvette and coil, 632 Hz, for the solution sample D : electric field cell E : polarizing filter F : focusing lens focusing to the photodiode G : photodiode with amplifier HP : spectrum analyzer (HP 3582°) COMP : signal acquisition system for off-line processing
    21. 21. Birefringence We applied magnetic and electric field to evaluate the sensitivity of the test solutions to the fields measuring the Faraday and Pockels effects. The Faraday effect ( or Faraday rotation ) is a magneto-optical phenomenon, ie an interaction between light and a magnetic field in a (dielectric liquid) medium. The Faraday effect causes a rotation of the plane of polarization, which is linearly proportional to the component of the magnetic field in the direction of propagation. The Pockels effect, or Pockels electro-optic effect, produces birefringence in an optical medium induced by a constant or varying electric field
    22. 22. Birefringence We executed four different test sessions, preparing 4 different cuvettes containing: • Tubulin in tubulin buffer; • MTs in MT buffer; • tubulin in MT buffer; • MT buffer without MTs. And applying to each cuvette • a transverse electric field (1 V/cm) • a transverse magnetic field • a longitudinal magnetic field • no field.
    23. 23. Birefringence •For each test the polarimeter measures the current coming to the photodiode: in presence of scattering due to the Faraday effect, the signal intensity decreases. •We use simultaneously also a distilled water cuvette to have a reference signal, knowing that a Faraday effect due to the water was to be expected and evaluated. •After normalizing by the value of the distilled water sample, the signals (sampled at 8000 Hz) were submitted to FFT procedures with Hamming and Hann windowing systems, with and without smoothing.
    24. 24. EF TMF LMF NF 610Hz 632Hz 610Hz 632Hz 610Hz 632Hz 610Hz 632Hz Mt in MT buffer 927.41 24.75 1257.95 101.44 1232.30 2253.10 1148.0 9.87 Tb in MT buffer 2229.97 39.46 2013.46 200.63 2047.91 4827.94 2146.92 6.13 MT buffer 2996.69 29.72 2842.10 262.97 2893.39 6758.69 2878.20 16.83 Tb in Tb buffer 3445.27 8.65 884.68 79.21 834.54 1928.90 940.53 3.32 EF TMF LMF NF 610Hz 632Hz 610Hz 632Hz 610Hz 632Hz 610Hz 632Hz Mt in MT buffer 286.7 8.1 391.7 32.8 385.5 712.4 356.5 n/d Tb in MT buffer 694.9 13.7 627.8 63.9 646.7 1525.1 669.8 n/d MT buffer 934.3 11.5 885.6 84.4 902.1 2133.8 897.6 n/d A- Hamming windowing (home made sw) B - Hann windowing – Hann smoothing (SigView)
    25. 25. EF TMF LMF NF 610Hz 632Hz 610Hz 632Hz 610Hz 632Hz 610Hz 632Hz Mt in MT buffer 1141.8 27.2 1547.0 185.0 1517.5 2628.3 1412.1 5.5 Tb in MT buffer 2750.1 4.7 2477.4 234.3 2555.9 5629.3 2610.9 2.3 MT buffer 3690.6 30.8 3498.0 305.1 3564.8 7883.4 3547.3 8.7 Tb in Tb buffer 4247.7 7.7 1089.7 92.5 1028.5 2250.7 1158.5 1.3 EF TMF LMF NF 610Hz 632Hz 610Hz 632Hz 610Hz 632Hz 610Hz 632Hz Mt in MT buffer 748.7 18.68 1015.4 79.3 994.8 1762.5 926.4 9.91 Tb in MT buffer 1800.1 31.58 1625.5 157.1 1674.8 3775.6 1733.0 2.34 MT buffer 2418.6 21.64 2294.1 204.8 2335.2 5284.8 2323.1 8.19 C - Hann windowing without smoothing D - Hamming windowing - Hamming smoothing
    26. 26. The tabled values have been normalized with respect to the reference value (632 value/610 value). Tab. A EF Tab. B EF Tab. D EF Tab. E EF Mt in MT buffer 0.0267 0.0283 0.0238 0.0249 Tb in MT buffer 0.0177 0.0197 0.0169 0.0175 MT buffer 0.0099 0.0123 0.0083 0.0089 ELECTRICAL FIELD Under electrical field the solution with MTs has always bigger values both than the solution with tubulin, and than the buffer alone
    27. 27. Tab. A TMF Tab. B TMF Tab. C TMF Tab. D TMF MT in MT buffer 0.0810 0.0837 0.0766 0.0781 Tb in MT buffer 0.0996 0.1018 0.0946 0.0966 MT buffer 0.0925 0.0953 0.0872 0.0893 TRANSVERSE MAGNETIC FIELD The magnetic transverse field affects the various solutions virtually in the same way. .
    28. 28. LONGITUDINAL MAGNETIC FIELD With longitudinal magnetic field the solution with MTs has always a value that is minor than both the solution with tubulin and the solution alone. Tab. X LMF Tab. Y LMF Tab. Z LMF Tab. K LMF Mt in MT buffer 1.828 1.8480 1.7320 1.7717 Tb in MT buffer 2.327 2.3567 2.2025 2.2544 MT buffer 2.336 2.3654 2.2115 2.2628
    29. 29. Tab. X NF Tab. Y NF Tab. Z NF Tab. K NF Mt in MT buffer 0.00860 No peak in 632 0.00389 0.01069 Tb in MT buffer 0.00285 No peak in 632 0.00088 0.00135 MT buffer 0.00585 No peak in 632 0.00245 0.00353 NO FIELD •Without electromagnetic field the solution with MTs has always a value bigger than the value of the solution with tubulin. •In this case the value of the solution with tubulin is minor than the value of the solution alone.
    30. 30. Statistical Analysis • Given the substantial equivalence between parameterizations, the statistical analysis was performed checking the significance of data processed with Hamming windowing and Hamming smoothing (5 pts). • The chosen procedure was a Paired T-test. • Among all the tests, just the Paired T-test which compares tubulin in microtubules buffer and buffer alone subjected to electric field, shows a value above the 5% threshold. • All the other comparisons show an extremely high statistical significance, with p-Value always <0.0005.
    31. 31. Statistical Analysis
    32. 32. Results MTs react to electromagnetic fields in a different way than tubulin and control sample: birefringence effect is always sharply different in MTs with respect to tubulin and control, with very high statistical significance (p<<0.001). This suggests again that the molecular structure of MTs could be the cause of their reaction to electromagnetic fields The uniformity of the results through the different parameterizations after normalization suggests that the measured effects are not due to noise or chance.
    33. 33. Conclusions The experimental results confirm the working hypothesis that Microtubules could be the structure inside neurons responsible for their sensitivity to extremely weak electromagnetic field and that this behavior could be due to their peculiar tubular structure, that allow them to behave like cavity antennas.
    34. 34. Computational Analysis C o m p u t a t i o n a l A n a l y s i s Synergetic use of different computational methods to validate and analyze the experimental results Molecular Dynamics Self-organizing artificial neural networks Study of the evolution of the dynamic organization of the examined structures under the influence of electromagnetic fields Analysis of the resulting network configuration: occupancy – conflicts method
    35. 35. Molecular Dynamics software Ascalaph - Very flexible tool with many possible parameterizations for the force fields - Various dynamical optimization techniques - Graphical interface with many interactive methods for the development of molecular models - Quantum computation - Possibility to apply electric field
    36. 36. Tertiary structures of MTs and tubulin obtained from Protein Data Bank (PDB) and NANO_D INRIA group Tertiary structures of nanotubes and buckyballs included in Ascalaph Validation of the experimental results using molecular dynamics on MT, tubulin, CNT and BB under different level of electro-magnetic fields 1° simulation: absence of electric field 2° simulation: EF = 2 V/cm, f = 90 Hz 3° simulation: EF = 90 V/cm, f = 90 Hz Molecules in implicit water at 298,15°K AMBER64 force field C o m p u t a t i o n a l A n a l y s i s Molecular Dynamics simulations
    37. 37. TUBULIN MICROTUBULES Tubulin and Microtubules Simulation
    38. 38. CNT Simulation No Field 90 V/cm field
    39. 39. CNT Simulation BBs show to be insensible the to electric field. CNTs tend to move with a dynamic axial motion, which becomes a real regular pulse in the presence of electric field. The movement of Tubulin and MTs is slower due to their computational complexity.
    40. 40. Artificial Neural Networks C o m p u t a t i o n a l A n a l y s i s Simulation results were submitted to two different self- organizing artificial neural networks: SONNIA for the evaluation of specific output parameters ITSOM for the evaluation of the cahotic attractors of the dynamical systems constituted by the molecular structures The xyz values of the molecules after dynamic simulation (energy minimization) are used as input values for the ANNs
    41. 41. C o m p u t a t i o n a l A n a l y s i s Artificial Neural Networks A Self-Organizing Map is an Artificial Neural Network able to classify streams of input data by mapping them by vector quantization into a smaller dimension. The weights of the network adapt themselves to the input after a number of recursions (self-organization) and represent the classification itself.
    42. 42. C o m p u t a t i o n a l A n a l y s i s Artificial Neural Networks Any Artificial Neural Network can be considered as a dynamic system of n-dimensional differential equations describing the dynamics of n neurons. Each neuron is mathematically defined by its state x (i) and by its gain function gi=gi(xi) (tipically the logistic function). In particular, a Self-Organized Map (SOM) can be expressed as a non-linear dynamic model. The SOM dynamical evolution shows the typical self-organizing and chaotic behavior of the complex dynamic systems. SONNIA and ITSOM are SOM networks and highlight a self-organized and chaotic dynamic evolution in presence of organized data. Artificial Neural Networks (ANN) are effective non-linear classifiers, useful for complex patterns
    43. 43. SONNIA C o m p u t a t i o n a l A n a l y s i s SONNIA is a computational environment for the development and analysis of self-organizing neural networks Very useful in the field of drug discovery and protein prediction. It allows to classify a series of data sets, providing both supervised and unsupervised learning. In particolar, SONNIA can classify new molecules of known structure but unknown function, or viceversa In this research project we have instead decided to use the analysis tools provided by SONNIA to assess the degree of dynamic organization reached by the examined molecules when subjected to electromagnetic fields
    44. 44. C o m p u t a t i o n a l A n a l y s i s SONNIA Two parameters were represented: Occupancy number of patterns mapped onto the same neuron, indicating similarities in the input domain Conflicts neurons corresponding to inputs belonging to different classes For our case study we developed a Kohonen rectangular network structure with 9x6 neurons and a random initialization.
    45. 45. Occupancy:55 Conflict:251 No Field Occupancy: 51 Conflict: 384 EF=2V/cm f=90Hz EF=90V/cm f=90Hz Occupancy:37 Conflict:676 TUBULIN Occupancy:53 Conflict:1020 Occupancy:38 Conflict:117 Occupancy:35 Conflict:780 MICROTUBULES Tubulin: •No field: high values of occupancy (high regularity) and conflicts •Weak electric field: same occupancy and conflicts •Increasing the electric field the number of conflicts increases, showing a decrease in structural organization Microtubules: No field: less occupancy compared to tubulin, demonstrating that MTs have a more complex spatial conformation. Weak electric field: there are no changes. Increasing the electric field the conflicts decrease dramatically, showing an increase in structural
    46. 46. BUCKYBALL NANOTUBES O: 8 C: 0 C: 0O: 7 O: 13 C: 0 O: 6 C: 0 O: 7 C: 0 No Field EF=2V/cm f=90Hz EF=90V/cm f=90Hz C: 0O: 4 Buckyballs and nanotubes have low values of occupancy and no conflicts, because of their limited number of component and their stable configuration Nanotubes have a more complex structure, but their occupancy is still low. Zero conflicts mean good dynamical stability. Occupancy increases with the growing of the electric field, improving regularity.
    47. 47. ITSOM network C o m p u t a t i o n a l A n a l y s i s ITSOM is an evolution of Kohonen SOM, that highlight the chaotic dynamic evolution that the neural network follows in the presence of organized data
    48. 48. ITSOM network C o m p u t a t i o n a l A n a l y s i s The sequence of winning neurons forms a series of numbers that are repeated almost periodically (chaotic attractors). Each attractor uniquely identifies the input pattern. The graphical representation of the chaotic attractor provides a graphical representation of the dynamic organization of the pattern We developed in Matlab - Simulink a procedure that processes in form of dynamic attractors the series of winning neurons resulting from the output of ITSOM
    49. 49. BUCKYBALL NANOTUBES No Field EF=2V/cm f=90Hz EF=90V/cm f=90Hz Buckyballs: Behavior not modified by the electric field Nanotubes: Increase of spatial occupancy, with an interesting increase of order when electric field is applied C o m p u t a t i o n a l A n a l y s i s
    50. 50. No field EF=2V/cm f=90Hz EF=90V/cm f=90Hz Tubulin Generates a stable attractor in absence of field, that tends to become less structured when applying E-M field Microtubules Show the same strong organization as tubulin in absence of field, but on the contrary their attractors tend to become more compact when electric field is applied, focusing on a restricted spatial configuration, after a short transition phase TUBULIN MICROTUBULES C o m p u t a t i o n a l A n a l y s i s
    51. 51. The MD simulation shows that BBs are insensible the to electric field, as confirmed also by the Artificial neural Networks. CNTs tend clearly to move with a dynamic axial motion, which becomes a real regular pulse in the presence of electric field. The behavior of the neural network reflects this trend, which shows the extreme regularity of these nanostructures and an interesting (known in literature) behavior of CNTs in the presence of electric field, highlighted by the growing spatial regularity and extremely regular dynamic attractors, that are also highlighted by the pulsing behavior in the MD simulation. C o n c l u s i o n s Conclusions
    52. 52. Tubulin, despite its symmetric structure, seems to have internal forces that tend to resist a dynamic stabilization, and in the presence of electric field it does not show a regular behavior. Microtubules tend to stabilize their dynamical evolution with the growing of the electrical field, again showing an analogy with the CNT behavior. C o n c l u s i o n s Conclusions
    53. 53. Conclusions and Future Plans C o n c l u s i o n s The computational methods showed to be valuable for the analysis of complex biophysical phenomena The Artificial Intelligence approach supports the experimental evidences at the microscopic level, allowing a more correct and accurate interpretation of the results It was possible to justify the experimental results in light of structural and dynamic models, highlighting the actual existence of substantial effects of electromagnetic fields on the dynamic evolution of microtubules.
    54. 54. Conclusions and Future Plans C o n c l u s i o n s •The evidence of a specific behavior of MTs in presence of electromagnetic field and its explanation in terms of dynamical organization could be seen as a progress towards the study of the role of MTs in long distance cellular communication: not only in the neuronal system but also in the whole body cellular system. •The positive results obtained from the synergetic approach combining computational methods to biophysical experiments encourage us to continue our experimental and computational research. •A possible future development consists of the evaluation of the biophysical modifications of microtubules and tubulin due to potential conformational changes upon interaction with different ligands.
    55. 55. R. Pizzi, S. Fiorentini, G. Strini, and M. Pregnolato. “Exploring Structural and Dynamical Properties of Microtubules by Means of Artificial Neural Networks”. In: Complexity Science, Living Systems and Reflexing Interfaces: New Models and Perspectives. p. 78-91, 2012, IGI Global New York. Publications R. Pizzi, G. Strini, S. Fiorentini, V. Pappalardo and M. Pregnolato, “Evidences of new biophysical properties of Microtubules”, in Focus on artificial neural networks. p. 191-207, 2010, Nova Science New York. R. Pizzi, S. Fiorentini, “Artificial Neural Networks Identify the Dynamic Organization of Microtubules and Tubulin Subjected to Electromagnetic Field”, Proc. 9th WSEAS Int Conf. On Applied Computer Science, Genova 17-19 Oct. 2009, p. 103-106. P u b l i c a t i o n s

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