This passage is adapted from Sarah Lewin, “New Mathematics Could Neutralize Pathogens that Resist Antibiotics.” ©2015 Scientific American. Adapted from Theresa Mendoza, “What I Did Not Know about Antibiotic Resistance.” © 2016 by DFWHC Foundation.
Bacteria that make us sick are bad enough, but many of
them also continually evolve in ways that help them develop
resistance to common antibiotic drugs, making our medications
less effective. Doctors try to reduce the evolution by cycling
through various drugs over time, hoping that as resistance
develops to one, the increased use of a new drug will catch
some of the bugs off guard.
The plans for cycling drugs don’t always work efficiently,
allowing bacteria to continue to develop resistance. Now a new
algorithm that deciphers how bacteria genes create resistance in
the first place could greatly improve such a plan. The “time
machine” software, developed by biologists and
mathematicians, could help reverse resistant mutations and
render the bacteria vulnerable to drugs again.
Miriam Barlow, a biologist, first hit on the idea while trying
to predict how antibiotic resistance would evolve several years
ago. But she lacked the mathematics to quantify it. “We were
pushing evolution forward, trying to predict how antibiotic
resistance would evolve, and we saw a lot of trade-offs,”
Barlow says. Introducing an antibiotic might lead to bacteria
developing resistance but it might also lead to them losing
resistance to some other medication. So Barlow partnered with
mathematicians and tried to figure out a series of steps to make
those losses of resistance as likely as possible.
The researchers took as a starting point TEM-1, a protein
stemming from a common gene that confers resistance to
penicillin. They considered four possible independent
mutations that can occur in the gene, all of which confer
resistance to new antibiotics, and they selected a range of 15
commonly used antibiotics. They then measured the growth
rates of Escherichia coli bacteria, as each mutation was
exposed to each of the antibiotics, which let them work out the
probability that the overall population of E. coli would gain or
lose a mutation to adapt. In this way the researchers could
directly model possible changes to drug-resistant genes.
The researchers were able to sketch a network of different
mutation combinations and figure out the probabilities of
moving from one to the other, given certain antibiotics. They
called the software for finding the path back to TEM-1 the
“Time Machine.” Although in the real world a bacterium
would not revert to its prior genetic form once it had evolved,
this mathematical goal revealed the best genetic targets for
slowing resistance.
The researchers were surprised to find that most mutations
didn’t need a long chain of antibiotics to revert to TEM-1.
They also found they could revert most mutations with a 60
percent probability, which is more efficient than current
antibiotic cycling schemes. And they found that they could
reach a high level of reliability with just a few antibiotics in the
cycle.
Direct network modeling like this is becoming more
common in biology as researchers learn how to distill problems
into the correct mathematical formats. But mathematicians are
still learning the best ways to optimize networks of connections
that can grow in complexity. Researchers still need to pinpoint
how long the cycles should last and the necessary dosages as
well as looking into how the system adapts to more complex
mutations.
Robert Beardmore, a mathematical bioscientist, describes
this work as trying to find the signal in the noise of bacterial
resistance development. Future lab work will reveal whether
the interactions the team found are strong enough to define
what happens in more complex scenarios. “At the heart of what
everybody wants to know is how predictable is evolution—and
if it’s predictable, can we reverse it?” he says.
Bacteria that make us sick are bad enough, but many of
them also continually evolve in ways that help them develop
resistance to common antibiotic drugs, making our medications
less effective. Doctors try to reduce the evolution by cycling
through various drugs over time, hoping that as resistance
develops to one, the increased use of a new drug will catch
some of the bugs off guard.
The plans for cycling drugs don’t always work efficiently,
allowing bacteria to continue to develop resistance. Now a new
algorithm that deciphers how bacteria genes create resistance in
the first place could greatly improve such a plan. The “time
machine” software, developed by biologists and
mathematicians, could help reverse resistant mutations and
render the bacteria vulnerable to drugs again.
Miriam Barlow, a biologist, first hit on the idea while trying
to predict how antibiotic resistance would evolve several years
ago. But she lacked the mathematics to quantify it. “We were
pushing evolution forward, trying to predict how antibiotic
resistance would evolve, and we saw a lot of trade-offs,”
Barlow says. Introducing an antibiotic might lead to bacteria
developing resistance but it might also lead to them losing
resistance to some other medication. So Barlow partnered with
mathematicians and tried to figure out a series of steps to make
those losses of resistance as likely as possible.
The researchers took as a starting point TEM-1, a protein
stemming from a common gene that confers resistance to
penicillin. They considered four possible independent
mutations that can occur in the gene, all of which confer
resistance to new antibiotics, and they selected a range of 15
commonly used antibiotics. They then measured the growth
rates of Escherichia coli bacteria, as each mutation was
exposed to each of the antibiotics, which let them work out the
probability that the overall population of E. coli would gain or
lose a mutation to adapt. In this way the researchers could
directly model possible changes to drug-resistant genes.
The researchers were able to sketch a network of different
mutation combinations and figure out the probabilities of
moving from one to the other, given certain antibiotics. They
called the software for finding the path back to TEM-1 the
“Time Machine.” Although in the real world a bacterium
would not revert to its prior genetic form once it had evolved,
this mathematical goal revealed the best genetic targets for
slowing resistance.
The researchers were surprised to find that most mutations
didn’t need a long chain of antibiotics to revert to TEM-1.
They also found they could revert most mutations with a 60
percent probability, which is more efficient than current
antibiotic cycling schemes. And they found that they could
reach a high level of reliability with just a few antibiotics in the
cycle.
Direct network modeling like this is becoming more
common in biology as researchers learn how to distill problems
into the correct mathematical formats. But mathematicians are
still learning the best ways to optimize networks of connections
that can grow in complexity. Researchers still need to pinpoint
how long the cycles should last and the necessary dosages as
well as looking into how the system adapts to more complex
mutations.
Robert Beardmore, a mathematical bioscientist, describes
this work as trying to find the signal in the noise of bacterial
resistance development. Future lab work will reveal whether
the interactions the team found are strong enough to define
what happens in more complex scenarios. “At the heart of what
everybody wants to know is how predictable is evolution—and
if it’s predictable, can we reverse it?” he says.