When many act as one, data-driven models can reveal key behaviors
Bioengineers' tool uncovers individual origins of collective behavior


Date:
July 16, 2020
Source:
Rice University
Summary:
Researchers have shown that data science approaches can reveal
subtle clues about the origins of such collective behaviors as
aggregation of bacteria.



FULL STORY ========================================================================== Biology is rife with examples of collective behavior, from flocks of
birds and colonies of bacteria to schools of fish and mobs of people. In
a study with implications from oncology to ecology, researchers from Rice University and the University of Georgia have shown that data science can unlock subtle clues about the individual origins of collective behavior.


==========================================================================
When a group of individuals move in synch, they can create patterns --
like the flocking of birds or "the wave" in a sports stadium -- that
no single individual could make. While these emergent behaviors can
be fascinating, it can be difficult for scientists to zero in on the
individual actions that bring them about.

"You see emergent behaviors by looking at the group rather than
the individual," said Rice bioengineer Oleg Igoshin, a theoretical
biophysicist who has spent almost 20 years studying emergent behavior
in cells -- be they cooperative bacteria, cancer cells or others.

In a study published online this week in the American Society for
Microbiology journal mSystems, Igoshin and Rice alumnus Zhaoyang Zhang developed a method to assess which aspects of individual behavior give
rise to emergent behavior.

To illustrate both the difficulty and the importance of understanding
these individual contributions to the collective group, Igoshin uses the example of metastatic cancer, where a group of cells with a particular
mutation are moving toward the surface of a tumor so they can break away
and form a new tumor elsewhere.

"Most of these cells fail to escape the original tumor, and the
question is, what determines which ones will succeed?" asked Igoshin,
a professor of bioengineering and senior scientist at Rice's Center
for Theoretical Biological Physics. "What property is a signal for
emergence? Is it how fast they move? Is it how long they move before
changing direction? Perhaps it's how frequently they stop. Or it could
be a combination of several signals, each of which is too weak to bring
about emergence on its own but which act to reinforce one another."
As a group, the potentially metastatic cancer cells share some key traits
and abilities, but as individuals, their performance can vary. And in a
large population of cells, these performance differences can be as stark
as those between Olympic athletes and couch potatoes. Above all, it is
this natural variation in individual performance, or heterogeneity, that
makes it so difficult to zero in on individual behaviors that contribute
to emergent behaviors, Igoshin said.



========================================================================== "Even for cells in a genetically homogeneous tumor, if you look
at individuals there will be a distribution, some heterogeneity in
performance that arises from some individuals performing 50% above
average and others 50% below average," he said. "So the question is,
'With all of this background noise, how can we find the weak trends
or signals associated with emergence?'" Igoshin said the new method incorporates data science to overcome some weaknesses of traditional
modeling. By populating their models with experimental data about the
movements of individual cells, Igoshin said he and Zhang, who received
his Ph.D. from Rice in May, simplified the search for individual behaviors
that influence group behaviors.

To demonstrate the technique, they partnered with Lawrence Shimkets,
whose lab at the University of Georgia (UGA) has spent years compiling
data about the individual and group behaviors of the cooperative soil
bacterium Myxococcus xanthus.

"They're predatory microbes, but they are smaller than many of the things
they eat," Igoshin said of M. xanthus. "They work together, kind of like
a wolf pack, to surround their prey and make the chemicals that will
kill it and digest it outside of their bodies, turning it into molecules
that are small enough for them to take in." During times of stress,
like when food is running short, M. xanthus exhibit a form of emergent
behavior that has been studied for decades. Like lines of cars flowing
into a city at rush hour, they stream together to form densely packed
mounds that are large enough to see with the naked eye. Mound formation
is an early step in the process of forming rugged long-lived spores that
can reestablish the colony when conditions improve.



==========================================================================
In a previous study, UGA's Chris Cotter, a co-author of the new study
and a graduate student in Shimkets' group, tracked individual behaviors
of wild-type cells and collaborated with Igoshin to develop a data-driven
model that uncovers the cellular behaviors that are key to aggregation. In
the new study, Cotter and Zhe Lyu, a former UGA postdoctoral researcher
now at Baylor College of Medicine in Houston, collected mound-forming
data from mixtures of three strains of M. xanthus: a naturally occurring
wild type and two mutants. On their own, the mutants were incapable of
forming mounds. But when a significant number of wild-type cells were
mixed with the mutants, they were "rescued," meaning they integrated
with the collective and took part in mound-building.

"One of the mutants is fully rescued while the other is only partially
rescued, and the goal is to understand how rescue works," Igoshin
said. "When we applied the methodology, we saw several things that were unexpected. For example, for the mutant that's fully rescued, you might
expect that it behaves normally, meaning that all its properties -- its
speeds, its behaviors -- will be exactly the same as the wild type. But
that's not the case. What we found was that the mutant performed better
than normal in some respects and worse in others. And those compensated
for one another so that it appeared to be behaving normally." Emergent behaviors in M. xanthus are well-studied, classic examples. In addition
to showing that their method can unlock some of the mysteries of M.

xanthus' behavior, Igoshin said the new study indicates the method can be
used to investigate other emergent behaviors, including those implicated
in diseases and birth defects.

"All we need is data on individuals and data on the emergent behavior,
and we can apply this method to ask whether a specific type of
individual behavior contributes to the collective, emergent behavior,"
he said. "It doesn't matter what kind of cell it is, and I think it could
even be applied to study animals in ecological models. For instance,
ecologists studying migration of a species into a new territory often
collect GPS-tracking data. In principle, with enough data on individual behavior, you should be able to apply this approach to study collective, herd-level behaviors." The research was supported by the National
Science Foundation (DMS-1903275, IOS-1856742, MCB-1411891, PHY-1427654)
and the Welch Foundation (C-1995).


========================================================================== Story Source: Materials provided by Rice_University. Note: Content may
be edited for style and length.


========================================================================== Journal Reference:
1. Zhaoyang Zhang, Christopher R. Cotter, Zhe Lyu, Lawrence
J. Shimkets,
Oleg A. Igoshin. Data-Driven Models Reveal Mutant Cell Behaviors
Important for Myxobacterial Aggregation. mSystems, 2020; 5 (4)
DOI: 10.1128/mSystems.00518-20 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/07/200716101543.htm

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