The communication between cells and other molecular components is one of
the innumerable biological processes that support the behaviors, physiology,
and life of living creatures. It is known that these molecular constituents
can communicate with one another through a variety of mechanisms, such as
mechanical wave exchange or the processes of diffusion and electrical
depolarization.
A new study by Yale University researchers sought to determine the
metabolic cost of this information transmission between molecular components
and cells. Their work, which was published in Physical Review Letters,
presents a novel instrument that may be applied to the research and
comprehension of cellular networks.
Researchers Benjamin B. Machta and others have been considering this idea
in one way or another for some time, as Machta informed Phys.org.
"About ten years ago, my Ph.D. adviser Jim Sethna and I originally talked
about concepts that would eventually become this project, but for a variety
of reasons that work never quite took off. Sam and I got to speak about this
when we were considering ways to make sense of the energy costs that biology
must incur in order to compute—a topic that runs through a lot of his
doctoral work—and perhaps in a broader sense, to make sure that its
components are cohesive and under control. He worked out how to perform
these calculations."
Inspiration for the latest study by Machta and colleague Samuel J. Bryant
comes from past studies that were released in the late nineties, especially
from Simon Laughlin and associates. This group of researchers had attempted
to ascertain through experimentation how much energy neurons expend during
information transmission.
The amount of energy required to delete a bit of information is
significantly larger than the "fundamental" constraint of ~ KBT/bit, also
known as the Landauer bound, according to research by Laughlin and
colleagues, which varied depending on the circumstances, Machta said.
We were curious as to whether this was an instance of biology being
wasteful in any manner. It's also possible that there were additional
expenses that needed to be covered; in instance, the Landauer limit doesn't
include geometry or other specifics. Although it is not the main subject of
this discussion, applying the Landauer bound is subtle in and of itself
because it is only paid on deleting information, making it feasible to
compute reversibly, never delete anything, and pay NO computational
cost."
Machta and Bryant's latest work also sought to ascertain if optimizing
these energy costs may provide insight into the reasons for the diverse
physical procedures that molecular systems use to interact with one another
under various conditions. For example, whereas electrical impulses are the
usual means of communication between neurons, other kinds of messages can
also be sent by chemical diffusion.
"Our goal was to determine the optimal regime for each of these (and other)
in terms of energy cost per bit," stated Machta. "All of our computations
take into account data that is sent via a physical channel, from a physical
information sender (such as an ion channel that is used for'sending'
signals) to a physical receiver (such as a voltage detector in the membrane
that may also be an ion channel). The information rate across a Gaussian
channel is calculated using a textbook method at its core, with a few novel
twists."
First off, Machta and his associates' calculations are always based on a
physical channel, where electrical charges and currents of actual particles
are transported in accordance with the physics of a cell. Second, the group
has long believed that thermal noise in the cellular environment corrupts a
channel.
"The 'fluctuation dissipation theorem,' which relates the spectrum of
thermal fluctuations to the near equilibrium response functions, allows us
to calculate the spectrum of this noise," Machta said.
The fact that the team's calculations were made with very simple models
adds to their uniqueness. Because of this, the researchers were able to
consistently set cautious lower constraints on the amount of energy needed
to drive physical currents in a biological system and power a channel.
Machta stated, "We often calculate costs with a geometric prefactor
doubling "KBT/bit," since the signal has to overcome thermal noise.
"The size of the transmitter and receiver may be seen as this geometric
element; a larger sender can spread a dissipative current across a greater
region, hence reducing costs per bit. Furthermore, a bigger receiver enables
greater averaging across temperature variations, allowing the same
information to be carried by a weaker overall signal."
For electrical communication, for instance, we obtain a form for the cost
per bit that grows like r2/σI σO kBT/bit, where σI,σO are the sender and
receiver's sizes and r represents their distance from one another.
Importantly, this cost might potentially be several orders of magnitude more
than kT/bit, which simpler (or more basic) considerations propose as a
bottom bound, for ion channels that are a few nanometers broad but that
transmit information over microns."
All things considered, the computations carried out by Machta and
associates validate the substantial energy cost linked to information
transit between cells. In the end, these approximations may serve as a basis
for an explanation of the high information processing cost seen in
experimental investigations.
"Our explanation relies on the geometry of neurons and ion channels, among
other details, making it less 'fundamental' than the Landauer bound," Machta
stated. But if biology is affected by these specifics, it's possible that,
rather than just being inefficient, neurons—for instance—are efficient and
facing actual energy or information constraints. While these calculations
are by no means sufficient to conclude that a certain system is efficient at
this point, they do indicate that transmitting data into space may need very
high energy expenditures."
This latest work by Machta and colleagues may provide insights into
exciting new biological research in the future. The researchers also
included a "phase diagram" in their study, which depicts scenarios where it
is best to employ particular communication mechanisms (such as chemical
diffusion, electrical signaling, etc.) sparingly.
Soon, this graphic may aid in a better understanding of the fundamentals
behind various cell signaling techniques. For example, it might explain why
E. coli bacteria use diffusion to transmit information about their chemical
environment and why neurons use chemical diffusion to communicate at
synapses but use electrical signals when sending information over hundreds
of microns from dendrites to the cell body.
Machta continued, "One thing we are working on right now is trying to apply
this framework towards understanding the energetics of a concrete signal
transduction system,"
"In actual systems, there are usually information processing networks;
implementing our constraint necessitates a knowledge of the information flow
in these networks. Our previous work only examined the abstract cost of
conveying information between two isolated components. Application of our
computations to particular geometries (such as a'spherical' neuron or an
axon that resembles a tube, both significantly different from the endless
plain we employed here) presents further technological challenges in pursuit
of this aim.
Journal information:
Physical Review Letters
