TERRAFORMING TERRA
We discuss and comment on the role agriculture will play in the containment of the CO2 problem and address protocols for terraforming the planet Earth.
A model farm template is imagined as the central methodology. A broad range of timely science news and other topics of interest are commented on.
Saturday, March 30, 2019
The Math That Tells Cells What They Are
Rather obviously, a lot more is happening than can be explained by the present intelectual despensation.
Let us tackle all this.
We experience a life through third tier matter. It is operated through second tier matter scaled at the electron level. Now none of this acts to store information. Both produce and read information but within our concept of continuous time. Information is stored as number on first tier Mobius strips in the first tier of matter on a 3D manifold but with two dimensional ribbons that also construct photons as well. This is the tier of creation in which a Mobius strip is formed in what is a holographic space for want of a better word. This produces two orthogonal axis or a binary creation necessary to produce a universe. Such a holographic space exists both independent of time and across time and is thus naturally self optimizing. Thus life itself becomes self optimizing.
The Math That Tells Cells What They Are
During
development, cells seem to decode their fate through optimal
information processing, which could hint at a more general principle of
life.
Cells
in embryos need to make their way across a “developmental landscape” to
their eventual fate. New findings bear on how they may do this so
efficiently.
https://www.quantamagazine.org/the-math-that-tells-cells-what-they-are-20190313/? In
1891, when the German biologist Hans Driesch split two-cell sea urchin
embryos in half, he found that each of the separated cells then gave
rise to its own complete, albeit smaller, larva. Somehow, the halves
“knew” to change their entire developmental program: At that stage, the
blueprint for what they would become had apparently not yet been drawn
out, at least not in ink.
Since then, scientists have been trying to understand what goes into
making this blueprint, and how instructive it is. (Driesch himself,
frustrated at his inability to come up with a solution, threw up his
hands and left the field entirely.) It’s now known that some form of
positional information makes genes variously switch on and off
throughout the embryo, giving cells distinct identities based on their
location. But the signals carrying that information seem to fluctuate
wildly and chaotically — the opposite of what you might expect for an
important guiding influence.
“The [embryo] is a noisy environment,” said Robert Brewster,
a systems biologist at the University of Massachusetts Medical School.
“But somehow it comes together to give you a reproducible, crisp body
plan.”
The same precision and reproducibility emerge from a sea of noise
again and again in a range of cellular processes. That mounting evidence
is leading some biologists to a bold hypothesis: that where information
is concerned, cells might often find solutions to life’s challenges
that are not just good but optimal — that cells extract as much useful
information from their complex surroundings as is theoretically
possible. Questions about optimal decoding, according to Aleksandra Walczak, a biophysicist at the École Normale Supérieure in Paris, “are everywhere in biology.”
Biologists haven’t traditionally cast analyses of living systems as
optimization problems because the complexity of those systems makes them
hard to quantify, and because it can be difficult to discern what would
be getting optimized. Moreover, while evolutionary theory suggests that
evolving systems can improve over time, nothing guarantees that they
should be driven to an optimal level.
Yet when researchers have been able to appropriately determine what
cells are doing, many have been surprised to see clear indications of
optimization. Hints have turned up in how the brain responds to external
stimuli and how microbes respond to chemicals in their environments.
Now some of the best evidence has emerged from a new study of fly larva
development, reported recently in Cell.
Cells That Understand Statistics
For decades, scientists have been studying fruit fly larvae for clues
about how development unfolds. Some details became apparent early on: A
cascade of genetic signals establishes a pattern along the larva’s
head-to-tail axis. Signaling molecules called morphogens then diffuse
through the embryonic tissues, eventually defining the formation of body
parts.
Particularly important in the fly are four “gap” genes, which are
expressed separately in broad, overlapping domains along the axis. The
proteins they make in turn help regulate the expression of “pair-rule”
genes, which create an extremely precise, periodic striped pattern along
the embryo. The stripes establish the groundwork for the later division
of the body into segments.
Early
in the development of fruit flies, four “gap” genes are expressed at
different levels along the long axis of the larval body. That pattern
lays the foundation for the expression of “pair-rule” genes in periodic
bands later, which give rise to specific body segments. The purple stain
in the embryo at left shows the expression of one gap protein; the
staining in the later larva at right reveals one pair-rule protein.
How cells make sense of these diffusion gradients has always been a
mystery. The widespread assumption was that after being pointed in
roughly the right direction (so to speak) by the protein levels, cells
would continuously monitor their changing surroundings and make small
corrective adjustments as development proceeded, locking in on their
planned identity relatively late. That model harks back to the
“developmental landscape” proposed by Conrad Waddington in 1956. He
likened the process of a cell homing in on its fate to a ball rolling
down a series of ever-steepening valleys and forked paths. Cells had to
acquire more and more information to refine their positional knowledge
over time — as if zeroing in on where and what they were through “the 20
questions game,” according to Jané Kondev, a physicist at Brandeis University.
Such a system could be accident prone, however: Some cells would
inevitably take the wrong paths and be unable to get back on track. In
contrast, comparisons of fly embryos revealed that the placement of
pair-rule stripes was incredibly precise, to within 1 percent of the
embryo’s length — that is, to single-cell accuracy.
That prompted a group at Princeton University, led by the biophysicists Thomas Gregor and William Bialek,
to suspect something else: that the cells could instead get all the
information they needed to define the positions of pair-rule stripes
from the expression levels of the gap genes alone, even though those are
not periodic and therefore not an obvious source for such precise
instructions. And that’s just what they found.
Over the course of 12 years, they measured morphogen and gap-gene
protein concentrations, cell by cell, from one embryo to the next, to
determine how all four gap genes were most likely to be expressed at
every position along the head-to-tail axis. From those probability
distributions, they built a “dictionary,” or decoder — an explicit map
that could spit out a probabilistic estimate of a cell’s position based
on its gap-gene protein concentration levels.
Around five years ago, the researchers — including Mariela Petkova,
who started the measurement work as an undergraduate at Princeton (and
is currently pursuing a doctorate in biophysics at Harvard University),
and Gašper Tkačik,
now at the Institute of Science and Technology Austria — determined
this mapping by assuming it worked like what’s known as an optimal
Bayesian decoder (that is, the decoder used Bayes’ rule for inferring
the likelihood of an event from prior conditional probabilities). The
Bayesian framework allowed them to flip the “unknowns,” the conditions
of probability: Their measurements of gap gene expression, given
position, could be used to generate a “best guess” of position, given
only gap gene expression.
The team found that the fluctuations of the four gap genes could
indeed be used to predict the locations of cells with single-cell
precision. No less than maximal information about all four would do,
however: When the activity of only two or three gap genes was provided,
the decoder’s location predictions were not nearly so accurate. Versions
of the decoder that used less of the information from all four gap
genes — that, for instance, responded only to whether each gene was on
or off — made worse predictions, too.
According to Walczak, “No one has ever measured or shown how well
reading out the concentration of these molecular gradients … actually
pinpoints a specific position along the axis.” Now they had: Even given the limited number of molecules and
underlying noise of the system, the varying concentrations of the gap
genes was sufficient to differentiate two neighboring cells in the
head-to-tail axis — and the rest of the gene network seemed to be
transmitting that information optimally.
“But the question always remained open: Does the biology actually
care?” Gregor said. “Or is this just something that we measure?” Could
the regulatory regions of DNA that responded to the gap genes really be
wired up in such a way that they could decode the positional information
those genes contained?
The biophysicists teamed up with the Nobel Prize-winning biologist Eric Wieschaus
to test whether the cells were actually making use of the information
potentially at their disposal. They created mutant embryos by modifying
the gradients of morphogens in the very young fly embryos, which in turn
altered the expression patterns of the gap genes and ultimately caused
pair-rule stripes to shift, disappear, get duplicated or have fuzzy
edges. Even so, the researchers found that their decoder could predict
the changes in mutated pair-rule expression with surprising accuracy.
“They show that the map is broken in mutants, but in a way that the
decoder predicts,” Walczak said.
Lucy Reading-Ikkanda/Quanta Magazine
“You could imagine that if it was getting information from other
sources, you couldn’t trick [the cells] like that,” Brewster added.
“Your decoder would fail.”
These findings represent “a signpost,” according to Kondev, who was
not involved with the study. They suggest that there’s “some physical
reality” to the inferred decoder, he said. “Through evolution, these
cells have figured out how to implement Bayes’ trick using regulatory
DNA.”
How the cells do it remains a mystery. Right now, “the whole thing is kind of wonderful and magical,” said John Reinitz, a systems biologist at the University of Chicago.
Even so, the work provides a new way of thinking about early development, gene regulation and, perhaps, evolution in general.
A Steeper Landscape
The findings provide a fresh perspective on Waddington’s idea of a
developmental landscape. According to Gregor, their work indicates that
there’s no need for 20 questions or a gradual refinement of knowledge
after all. The landscape “is steep from the beginning,” he said. All the
information is already there.
“Natural selection [seems to be] pushing the system hard enough so
that it … reaches a point where the cells are performing at the limit of
what physics allows,” said Manuel Razo-Mejia, a graduate student at the California Institute of Technology.
It’s possible that the high performance in this case is a fluke:
Since fruit fly embryos develop very quickly, perhaps in their case
“evolution has found this optimal solution because of that pressure to
do everything very rapidly,” said James Briscoe,
a biologist at the Francis Crick Institute in London who did not
participate in this study. To really cement whether this is something
more general, then, researchers will have to test the decoder in other
species, including those that develop more slowly.
Even so, these results set up intriguing new questions to ask about
the often-enigmatic regulatory elements. Scientists don’t have a solid
grasp of how regulatory DNA codes for the control of other genes’
activities. The team’s findings suggest that this involves an optimal
Bayesian decoder, which allows the regulatory elements to respond to
very subtle changes in combined gap gene expression.
“We can ask the
question, what is it about regulatory DNA that encodes the decoder?”
Kondev said. And “what about it makes it do this optimal decoding?” he added. “That’s a question we could not have asked before this study.”
“That’s really what this work sets up as the next challenge in the
field,” Briscoe said. Besides, there may be many ways of implementing
such a decoder at the molecular level, meaning that this idea could
apply to other systems as well. In fact, hints of it have been uncovered
in the development of the neural tube in vertebrates, the precursor of
their central nervous system — which would call for a very different
underlying mechanism.
Moreover, if these regulatory regions need to perform an optimal
decoding function, that potentially limits how they can evolve — and in
turn, how an entire organism can evolve. “We have this one example …
which is the life that evolved on this planet,” Kondev said, and because
of that, the important constraints on what life can be are unknown.
Finding that cells show Bayesian behavior could be a hint that
processing information effectively may be “a general principle that
makes a bunch of atoms stuck together loosely behave like the thing that
we think is life.”
But right now, it is still only a hint. Although it would be “kind of
a physicist’s dream,” Gregor said, “we are far from really having proof
for this.”
From Wires Under Oceans to Neurons in the Brain
The concept of information optimization is rooted in electrical
engineering: Experts originally wanted to understand how best to encode
and then decode sound to allow people to talk on the telephone via
transoceanic cables. That goal later turned into a broader consideration
of how to transmit information optimally through a channel. It wasn’t
much of a leap to apply this framework to the brain’s sensory systems
and how they measured, encoded and decoded inputs to produce a response.
Now some experts are trying to think about all kinds of “sensory
systems” in this way: Razo-Mejia, for instance, has studied how
optimally bacteria sense and process chemicals in their environment, and
how that might affect their fitness. Meanwhile, Walczak and her
colleagues have been asking what a “good decoding strategy” might look
like in the adaptive immune system, which has to recognize and respond
to a massive repertoire of intruders.
“I don’t think optimization is an aesthetic or philosophical idea.
It’s a very concrete idea,” Bialek said. “Optimization principles have
time and again pointed to interesting things to measure.” Whether or not
they are correct, he considers them productive to think about.
“Of course, the difficulty is that in many other systems, the
property being decoded is more difficult than one-dimensional position
[along the embryo’s axis],” Walczak said. “The problem is harder to
define.”
That’s what made the system Bialek and his colleagues studied so
tantalizing. “There aren’t many examples in biology where a high-level
idea, like information in this case, leads to a mathematical formula”
that is then testable in experiments on living cells, Kondev said.
It’s this marriage of theory and experiment that excites Bialek. He
hopes to see the approach continue to guide work in other contexts.
“What’s not clear,” he said, “is whether the observation [of
optimization] is a curiosity that arises in a few corners, or whether
there’s something general about it.”
If the latter does prove to be the case, “then that’s very striking,”
Briscoe said. “The ability for evolution to find these really efficient
ways of doing things would be an incredible finding.”
Kondev agreed. “As a physicist, you hope that the phenomenon of life
is not just about the specific chemistry and DNA and molecules that make
living things on planet Earth — that it’s broader,” he said. “What is
that broader thing? I don’t know. But maybe this is lifting a little bit
of the veil off that mystery.”
Correction added on March 15: The text was updated to acknowledge the contributions of Mariela Petkova and Gašper Tkačik.
