Electrodynamic Signaling by the Dendritic Cytoskeleton (Google Workshop on Quantum Biology)

>>
The next speaker is going to be Jack Tuszynski from the University of Alberta. He's going
to talk about cytoskeletal information processing, intracellular information processing.
>> TUSZYNSKI: Thank you very much for the invitation. I'd like to start by thanking
the organizers for putting this together. It's a great workshop and a great place, very
inspirational for all of us. I'm a theoretical physicist who is now a professor of experimental
oncology, so I guess I've reached my level of incompetence. But I'll be talking about
something else today. First of all, I acknowledge the sources of funding and, in particular,
technology innovations with Mike Weiner who has been faithfully supporting my group for
a number of years. But there's a lot of other sources of funding. Most of it, by the way,
is for a different purpose, the--and collaborators. I want to acknowledge collaborators with Stuart
and Vahid Rezania and Holly Freedman and Avner Priel, Horacio Cantiello and John Dixon, Miljko
Sataric. And being here, I'm not going to go and give any references whatsoever. Just
Google "Tuszynski tubulin" and you'll all get the references so I'm not going to waste
any information on these slides. Okay. My day job is actually computational drug design
and this is where most of the funding is coming from. That's a fraction of my group. I don't
have kind of pictures available. We do computer design and testing and silico searches for
novel cancer chemotherapy drugs. So that's basically what we do for a living. But because
microtubules are everywhere, microtubules play a role in both the oncology aspects as
well as the neuroscience aspect, which is more of a longstanding interest. And I'm going
to try and follow the best I can the tough acts that we heard today, this morning, this
afternoon. It's not easy and especially not with 5 o'clock, and for some of you, probably
it's early morning. I want to start by saying that--and integrating some of these observations
that were made today that in my opinion, the human brain is the world's fastest, most portable
supercomputer cluster. And every word has a meaning here. And I think this is quantum
biology. I will not say too much about quantum, being a quantum physicist, and overstepping
my competence already. But on the issue of human brain and some of these capabilities
that were mentioned, there are some numbers to quote. So we have 10 to the 10th neurons,
plus-minus some percentage. Roughly 10 to the 15th synapses, so there's a hundred thousand
synapses per neuron. And synapses operate at about 10 impulses per second, so that's
10 to the 14th FLOPS--sorry, 10 to the 16th synapse operations per second, which compares
to 10 to the 14th FLOPS of the Blue Genes. So even at the most crude, most classical
level, the brain is better than the Blue Gene. But I'm going to try and convince you that
actually the brain is orders of magnitude better than the Blue Gene, maybe all computers
in the world put together. A single brain. And of course, much more energy efficient
as well because it only consumes 25 watts as opposed to 1.5 megawatts for the Blue Gene.
So, I said it's a supercomputer cluster. So what are the little computers inside the brain?
So is there anything inside? So, I'm giving you the--I'm also a bit tired so cut me some
slack, please. Each computer is a neuron and each neuron has its own processing units,
and you probably guessed which ones are these. But as a physicist, I also want to understand
what sort of physics to apply to different spatial ranges or dimensions. So typically,
we would apply thermodynamics to the level of an organism. And at the mesoscale, now
we are grappling with nanotechnology, we don't really know what it is. Is it quantum, is
it classical? So this is where cells and sub-cellular structures operate at. And biomolecules with
10ł atoms, we are stretching into the range of quantum chemistry and eventually, the single
level, the single atom or tens of atoms, quantum physics. It's important also to keep in mind
the energy scale in biology. These are all the important biological distances and energy
scales and also affinity. So it's all biochemistry, reactions and how fast they precede their
questions about time scales. I really want to draw your attention to the bottom of this
scale, the GDP/ATP hydrolysis. And that's probably the only time I'll mention quantum
today--the energy quantum, biological energy quantum. GTP is a bit smaller in energy than
ATP--and I don't know what happened with the number there--it's between 2 and 10 KTI. I
don't know; it got deleted, maybe it's below the frame. So, this is above the threshold
of noise but not really incredibly much above the threshold of noise. All the quantum--all
the biological processes use either GTP or ATP energy to continue. Therefore, it's important
to keep this in mind. Different computers--again, this is cutting things off. And again, for
the third time today, I want to mention Guenter Albrecht-Buehler. And you can Google "cell
intelligence" and you'll find all about him and what he's been doing for the past 30 years.
What actually Albrecht-Buehler demonstrated is--and this is the premise of my hierarchical
scale in the organization of this supercomputer cluster. He demonstrated actually that a single
cell is intelligent. And when you go to his website, you will see these hundreds of pages
of illustrations, and this is one of the videos I'll try to show you. And it's actually--the
cell is perceiving infrared signals. So it's electromagnetic signaling. Of course, it also
perceives mechanical signals and chemical signals, which everybody knows about. But
the fact that electromagnetic energy is received and analyzed by the cell, by a single cell,
is incredibly important in this context, because the cell is already a computer in its own
right and a very powerful one at that. Okay, so we'll just go with the flow. So, some of
it was covered, so I'll try to quickly go through it. Microtubules and mitosis in dividing
cells and non-dividing cells, different roles, different architectures, they definitely play
a central role in every cell. Every eukaryotic cell has microtubules. There is no time to
discuss the variety of the building blocks, which is incredibly interesting; the two billion
years of evolution, this experiment on our planet in terms of testing. It's a lab, changing,
mutating and finding out what is the best for what different cells. Use different building
blocks. Tubulin is expressed by 20 genes in the human and different variants are used
for different cells. Cancer also makes choices, which is incidental to this talk. In nerve
cells, you have microtubules packing densely the axon and also present in dendrites. So
I don't know if anything will work today. No? Okay. So, I just wanted to show this dynamic
instability. There's a video that doesn't show, but microtubules are very dynamically
unstable. They grow and shrink. It's a very different polymer from anything else in the
body. DNA actin filaments, intermediate filaments, they don't behave like microtubules. So there's
something special about it biologically and we would like to know what is special about
microtubules physically or biochemically. And that's another cartoon which I stole from
Stuart Hameroff in this particular case. So they self-assemble and disassemble. So I want
to add another buzz word to it: evolvable computer, a recyclable computer. If we can
capture this power, that will be incredible. And talking microtubules interacting with
actin filaments with ion channels, this is documented in the literature. Now the challenge
is the integration of these various levels in a hierarchy. And bottom up or top down,
I don't care. We will try to look at it from the bottom and create models that stretch
different orders of magnitude. So that's shown here in a schematic with a tubulin dimer and
microtubule with maps and a bundle of microtubules inside the dendrite. And probably none of
this will work either. And this will not work. And this will not work. So, sorry about this,
but... >> [INDISTINCT] the directory, play it directly
from the directory. You can do that. >> TUSZYNSKI: Maybe. Everything is skipping
it. So, Anne O'Brian talked about the geometry, the topology, and beautiful crystal structures.
I have some--what I'll show you is some computer reconstructions, an in silico understanding
of these structures built together atom by atom. And this is incredibly powerful today.
You can use computational resources to reconstruct biological structures that otherwise you can't
see. Crystallography gives you fixed images at some level of resolution. So this is a
video from actual Berkeley from Eva Nogales and her group, showing the formation of microtubules
from tubulin dimers and the structure of dimers in great detailed ribbons, the helices and
these sheets, and how they fit into this cylindrical structure, how depolarization is asymmetric.
It's different from polymerization, the rings. And actually, gamma tubulin is used to nucleate
microtubules, so it's a different building block forming rings from which alpha and beta-tubulin
dimers grow into cylinders. I will stop this moving in a second because we don't want to
fall asleep. But Stuart mentioned GTP and GDP hydrolysis, so beta-tubulin has exchangeable
binding site for GTP and that is one of the possible modes of behavior. Conformational
change is caused by hydrolysis of GTP. That's, again, the quantum of energy, just about two
and a half KT, just above noise level, enough to cause these instabilities to take place.
So it's a very powerful nanomachinery if you want to look at it this way. All right, I'll
stop it now and go back to the slideshow. One of the structures that was not mentioned
today and plays an incredibly important role in microtubule biology that we know of and,
in my opinion, also in all the processes that were mentioned today is C-termini. C-termini
of tubilin are not crystal or graphically-resolved. These are little tails, whiskers, that decorate
the surface of a microtubule and they are very dynamic. They contain forty percent of
the electro-static charge of the protein, being only a very small fraction of the mass,
and they're involved in conformational dance that is highly dependent on the sequences.
So these C-termini have sequence variability which is completely unique to each species
and each form of tubilin in the human body. And I think that's where a lot of research
will go into. And a word of caution if you use laboratory techniques, Anne O'Brien and
colleagues, they have to be very careful what sort of tubulin they are using because each
tubulin has different C-termini that are absolutely crucial to their functioning. Here's a reconstruction
of the microtubule in silico. That's the entire--its millions of atoms here actually. They went
into the computer program that created this image and you have C-termini that I mentioned
decorating the surface. They have dynamical structures and I have some videos to show
you how they have different modes of oscillations. So we've talked today about eight megahertz,
twelve, eleven megahertz. Some of it may have to do actually with the dynamics of C-termini.
And they are shown here prominently in a reconstruction of the tubulin dimers. So these are the protruding,
in this particular case, straight up but that's not necessarily the confirmation that they
always adopt. It was just for the purpose of simulation. Okay, so again, more--for the
physicists actually, electrostatics plays a crucial role here because the way the structure
is put together in this beautiful lattice, including the ring, reflects the attraction
between the negative and positive charges as they are expressed on the surface of tubulin.
That shows you how the ring is made for example. And this is the formation of the microtubule
from dimers also following the principles of electrostatics. This has not been completely
understood to the greatest detail and the energy involved in the formation but definitely
the principle is there. And that's a reconstruction with an electrostatic surface rendition. So
the red is negative charge, blue is positive. It has actually a very important meaning because
microtubules have a net polarity. They are not--it's not arbitrary, which is the +end
and the -end. Both from the biological point of view, one is more actively growing than,
plus, than the other, minus, but also they have electrostatic polarity. So there's an
electric feed along the microtubule which would explain some of the effects that we're
talking about today and also some of the experiments that I participated in with Doctor Cantiello
from Harvard. This slide also shows you the values of these charges which maybe, in view
of these technical difficulties, I'll just skip. What I want to spend a few minutes on
is C-termini and their dynamical states. Those of you who are physicists in the audience
will right away hopefully think about spin-up, spin-down. Actually, the C-termini have two
stable states that we demonstrated by molecular dynamic simulations. One is up and one is
down. In fact, it makes contact with a positively charged patch on the opposite dimer. So a
beta-tubulin C-terminus touches the alpha-tubulin positive dimer and vice versa. This actually
also causes conformational changes, work that is currently submitted for publication. So
at least four states, at least four, because for the dimer, up and down for beta, up and
down for alpha. So 2x2 is four. And this is another simulation that I dare not touch that
shows you how actually dynamically these states evolve. You can start with a down-state or
bent C-terminus and run the simulation for a few nanoseconds and then they'll be--you'll
see it unbind and vice versa. So these things oscillate. There are natural oscillations
between these two states. I'll give up on all the videos, by the way, so. And these
are the ones that I maybe I would like to show but maybe at the end. We'll come back
to it, time permitting. What I'm showing you here, and we've classified them completely,
it's almost like a library of dynamical states. So I'll step away from the microphone and
talk like this for a second so everybody understands the message. We have these C-termini states
that may go down or up. But they also depend very strongly on where they--what kind of
tubulin they come from. So it's bovine tubulin or if you don't like it--or human tubulin.
In the lingo of tubulin microtubules, it's alpha one, two, three, four, five. They are
isoforms, isotypes of tubulin and beta one, two, three, four, five, six, eight. Each one
has a different mode, has a characteristic dynamical mode. It's like a dancer. Somebody
is dancing the waltz, the other is dancing the tango, somebody is cha-cha; they have
their own little dancers. And I think this is very much in terms of resonance. They--so
if you can now imagine the microtubule decorated with these dancers and it's made--one cell
is making dancers from alpha-6 and the other from beta-4, they will be engaged in different
dances and therefore, different resonant frequencies. I hope this message is getting through. And
I'll show you these dancers. I have all of them but assembled--okay. So that's under the topic of C-terminal tail
dynamics. And they--we have estimated that they oscillate on the frequency of gigahertz.
So, maybe not eight megahertz but much faster than that. And this is now the experiment
that was performed at Harvard with Horacio Cantiello. Horacio is a very skilled biochemist-biophysicist
who is, to my knowledge, the only person in the world maybe outside of Tacuba who can
actually grab microtubules with micropipets and manipulate them. He used micropipets which
were electrodes also. So he sent electric pulses from one pipet, micropipette, to the
other, measured the signal, and to his astonishment and mine also, this is what they found. Actually,
these electrical pulses were amplified. So if--so the blue one is the initial pulse at
the first end and the red is at the receptor end of the transmitter and receptor. And the
amplitude is increased between, depending on the simulation, between two and five times.
So this is a big puzzle and not--in addition to all these crazy things that Stuart talked
about and Anne O'Brian mentioned, this is in a published experiment in the respectable
journals, biophysical journals, reporting an amplification of electric pulses along
the microtubule surface. We have published papers also theoretically explaining what's
happening here and they involve actual ionic. So now, to some physical concepts and explanations.
Ionic waves which were sent by the--one of the electrodes sends an ionic cloud which
senses the surface of it, of the microtubule. The microtubule is actually acting to attract
ions because it's negatively charged. We have positive ions, sodium-potassium, and they
congregate around it and they feel it. So on one level, microtubule surface is a capacitor.
On another, water is a resistor. Ions and water resist the flow because of the viscosity,
so we have two of these. And there is also helicity that Anne O'Brien mentioned. So it
also can involve inductions. And we created the RLC model that--sorry--that is sketched
here. Its electrical circuit model which--with parameter values that were estimated from
molecular biophysics. And one complication that is interesting and important to observe,
especially in the context of the work from Tacuba, is that a microtubule is not a solid
surface. It has pores, nanopores, periodically located between alpha and beta dimers. So
ions actually are drawn inside the lumen because of the potential difference between the outside
surface which is more negative than the inside surface which is more positive. So we've created
a three-dimensional model that includes flow along, inside, and through the lumen. And
this is published in this year's Physical Review. Again, Google it. And we found actually
that it explains asymmetry--this is an I-V characteristic--asymmetry of the microtubule's
conductive property. So Stuart mentioned this and now let me try and summarize some numbers.
I started out by saying that the brain is probably more powerful than the most powerful
supercomputer. I think we are scratching the surface. And here's a conservative estimate
in terms of if you take into account the number of microtubules, the number of neurons in
the brain, and the number of states that each dimer can encode--this is actually before
we do the phosphor-relation calculation--C-termini states at least a four-pair dimer. Electron
hopping, they're at--I didn't have the time here to show you that electrons also have
double-well states in which they can hop, so let's say four. And conformational changes,
this is documented experimentally, two per dimer. So conservatively speaking, you have
32 states per dimer for 1 gigabyte of processing power per neuron with a 100 billion neurons
in the brain if you use your brain to full capacity. Einstein said that the only 10 percent
and even, I think, he would be in this category. So let's, say, put these numbers together.
That's 10 to the 20th bits per brain is my estimate of the computational capability of
the average human brain. And if these transitions can occur at nanosecond scale, then you have
10 to the 28th FLOPS. Actually, some of you may be very skeptical of this estimate and
will laugh at me. Let me just--don't laugh, because Von Neumann in 1950 came up with the
same number, 10 to the 20th. Subsequently, we became more and more cynical about our
ability to think. And you have the--you know, some of the gurus of modern computational
science estimating only ten--one gigabyte--one gigabit per brain with two bits per second
of visual, verbal, tactile, musical memory, and a human lifetime of two and a half billion
seconds. This is incredibly depressing. How does it compare? My estimate is in red, and you can challenge me on this, 10 to the
19th if we used bytes. Okay, so that's still 100%. The total number of the data on the
Web is 10 to the 15th, probably growing by one percent every minute. The Library of Congress,
three times 10 to the 15th. So, I think we are definitely capable of incredible amounts
of information storage. The question of how do you read and--write and read is of course
of absolute importance. And with Stuart's explanation of how we see phosphorylation
sites on tubulin as potential memory storage, we're getting closer and closer. I want to say this is not--tubulin and using
proteins as memory chips is not our original idea. This has been around and I want to give
credit to David Wishart from the University of Alberta who has been thinking about this
for a long time. And perhaps, there can be even a greater capacity than what I mentioned
here. But what I want to--before I forget and before it's--the time is over, I want
to say something that may be a little bit on the speculative side. You must have heard
about near-death experiences and people going through this life-history in their mind and
everything is encoded upon. If we believe these stories, then if you want to store all
this information that you ever received through sensory perception, this will be an incredible
amount of information. And these numbers that Landauer and others are talking about are
insufficient for that, so you really have to look deeper. And the second possibility
is hypnosis. Also, under hypnosis, people recall a lot of events that normally you don't
have access to, so they may be stored quite deeply in the brain. And this--just one more
addition to Stuart's description of our calmodulin kinase. The original idea for this actually
came recently two years ago through the experiment report in Science Daily and, I think, subsequently
in science of PNAS--I remember the journal--where actually calmodulin kinase was inhibited in
mice and the mice lost the memory of training in the previous half an hour. So that's how
it kind of started the search for how calmodulin kinase is involved in memory and we linked
it to phosphorylation sites on tubulin. Remember these pictures. So, my estimate regarding
the capacity of the human brain through microtubules did not involve this aspect at all. That's
another means of storing information. Now, we did something more that Stuart didn't mention.
Travis Craddock, my student, asked if--the following question following after, actually,
the advice from Stuart; where do anesthetics really bind? Only to receptors and synapses
or is the possible--any possibility of anesthetics binding to tubulin, and if so, where? So,
this is unpublished work but finished calculations. And I'm going to give you the sites of anesthetic
binding on tubulin that we discovered through computational modeling. And it's very telling.
So, I've highlighted in red some sites for anesthetic binding. Halothane was one of these,
the volatile anesthetics that we tested. They bind in places where oncologic agents, the
chemotherapy agents bind; taxol, vinblastine, colchicine, and also where GDP binds. So,
this is a testable prediction that we'll be confirming in the lab. In other words, if
this is so, anesthetics will be inhibitors of things like taxol. Another is they bind
to a lot of tryptophans and interestingly, the serines and threonines in C-terminal region.
This is exactly where calmodulin kinase binds. These pictures that Stuart and I just a second
ago showed, the--what do you call it? The poodle? Right, the nano-poodle. The nano-poodle
binds exactly where anesthetics binds; they're one of the sites. So it makes sense that you'll
lose memory because the nano-poodle doesn't work. And finally, also bind in the positive
patches where C-termini binds. I told you that C-termini bend over and bind to the respective
part--positive patches on the beta or alpha tubulin. They also bind there. So, there is
a lot of interesting information, I guess, to process. We have the skeleton of the model.
And just to add to the complexity, you can have--in addition to these rather fast changes
and information storage capabilities, you may also have in the brain long term effects
of learning and sometimes, pathological developments. Stuart mentioned Alzheimer's disease and decoupling
of TAO maps--map TAOS from microtubules. You can also have strengthening of these connections
through enforcement--through reinforcement through learning and repetition. So this,
again, a video clip that I dare not touch, shows you how these map interconnections dynamically
change architecture. So that's another level of--and I guess I'm done. Allow me just to
show one of these dancing--dances maybe with--after I go through the conclusions. The human brain,
in my opinion, has an amazing computational potential. I call it supercomputer cluster.
The neuron is a single computational unit. A cell, like any other cell, can exist on
their own and performs complex computational processes at the level of quantum of biological
energy. I dare not say quantum of physics but quantum of biological energy. If--it's
an evolvable--itself, the neuron is an evolvable computer which communicates with up to 100,000
nearby computers. So, think about these communicating networks inside the brain. And within each
computer, you have micro chips, which we call microtubules, as elementary computational
elements with approximately one gigabyte of memory or random access memory or whatever
else there may be there. And this is all I prepared for today. And I apologize for all
these technical difficulties. Thank you very much.
>> Okay. And we have some time for questions for Jack. Any questions? Okay, if there are
no questions, I'll--oh, I'm sorry. >> Yeah, great talk. Thank you very much.
I wanted to thank you especially for bringing up Albrecht-Buehler's work. And I wanted to
highlight one other thing that you didn't bring out. And Albrecht-Buehler has an area
he refers to as functional anarchy of the genome. I want to comment and tie this a little
bit back into what Elizabeth talked about earlier. The first stake through the heart
of the central dogma of biology was really when Barbara McClintock won the Nobel Prize
for the notion of jumping genes. And in her acceptance speech, she said we will be surprised
to find out the degree to which the genome senses its environment and shapes itself in
response to that. Now, if that doesn't make Schrodinger's cat start to howl, the idea
of a sensor and a shaper, it did to me when I was first looking at some random mutations.
So I would be happy to direct anybody into the genomic literature. The second stake through
the heart of the central dogma of biology now is with the sequencing of the genome,
less than two percent of the human genome codes for any protein. And people who look
at that say, "How can you generate something as complex as a human being from such a small
number of genes?" They--what I think they haven't come to terms with yet are the non-coding
portion of the genome. Forty percent of the human genome is mobile. It gets up and it
moves around and it rearranges itself. And Albrecht-Buehler talks about this in the functional
anarchy of genome. So, if we can talk some more, fine.
>> TUSZYNSKI: Thank you very much. It's a very good comment, actually. And I think there
is also a talkback to the gene. So, there's two-way communication. It's not just instruction;
it's actual information flowing back in. And I wouldn't blame microtubules for everything
but they may also play a role in this. >> Okay. Jack, you want to describe your video?
>> TUSZYNSKI: So this is one of the C-termini. It's from beta-tubulin two. I'll show you--so
that's basically--you remember these whiskers? They're not just randomly flailing around
subject to thermal wind. When you stimulate this in the computer, you'll see that each
one of them has a slightly different mode of vibration or oscillation. I'm going to
show--I have all of them classified. We've done extensive simulations with my former
student, Tyler Luchko who's now at Rutgers. And this is--you see quite different. And
each of these modes has a different frequency so you can communicate with them. That could
be another dynamical way of encoding. That's--this address, you know, this--the shape that I'm
showing you is encoded by the gene. So it's in the genome, by the way, so it's beta 3
tubulin, beta 2 tubulin. So a different gene and every organism will have a different one.
So if you want to cure cancer in mice, maybe you'll need a different frequency. And that's
another one. So, I have many of these. But just to illustrate the point that they complete
a--they execute completely different motions. So, that's the explanation.
>> Okay, thank you. Let's thank the speaker last time.