with this new approach, researchers are
beginning to uncover some of the key
events in immune signaling. Chakraborty
and his team have studied how some
immune cells operate in an on-or-off digital manner and have explored how the
cells learn to distinguish invaders from
the body’s own tissues. Current studies
using the statistical physics approach are
looking at how immune cells create and
keep memories of past infections. Others
are developing ne w theoretical models to
explain how immunity adapts to keep up
with ever-changing pathogens.
Understanding how immunity works
as a single system may help scientists
find new ways to manipulate and control it, leading to more effective vaccines
or improved treatments for persistent
autoimmune disorders.
By the numbers
The immune system’s first line of defense
is to prevent bacteria, parasites, viruses
and other infectious agents from entering the body. Skin and the cells that line
the nose and stomach serve as a front line,
keeping out and wafting away dirt, dust
and germs. Specialized cells also patrol
the bloodstream, attacking and devouring any germs that get through.
If a bug manages to outwit this system
and infect a cell, the adaptive immune
system kicks in. This special team serves
as a backup defense, designed to identify
offenders and produce legions of cells
ready for combat. The process starts
when white blood cells swoop in to capture and kill the invaders, taking their
remains to the lymph nodes. There, protein fragments of the invading pathogen
are displayed on the surface of antigen-presenting cells. Serving as a red flag, the
fragments identify the bug and signal the
immune system to attack cells infected
by that agent. Leading this attack are the
T cells, white blood cells that work to
neutralize infectious agents.
Generally, the various immune cell
types exist in small numbers. But under
threat, cell numbers can quickly expand
into the millions. These cells use chemicals to talk and coordinate an attack.
“It’s a game of numbers as much as
it is a game of molecular and cellular
biology,” says biologist Rustom Antia of
Emory University in Atlanta.
Alan Perelson, a theoretical immunologist at the Los Alamos National Laboratory in New Mexico, was one of the first
to show how mathematical models could
be used to study immune changes occurring during infection. In the early 1990s,
his team developed models to track how
the number of T cells shifts in response
to HIV, the virus that causes AIDS.
At the time, many scientists considered
AIDS to be a slow-acting viral infection
because it takes many years — on average
a decade — for symptoms to appear.
Using models to analyze the amount
of virus in the body, Perelson’s group
showed that the virus is, in fact, quite
active during this seemingly latent
period. The findings revealed that once
a person is infected, the virus is cleared
from the body quickly. But, in the absence
of drug therapy, it multiplies just rapidly
enough to keep up with this clearance.
Since Perelson’s work, advances in
instrumentation have made it possible
to collect much more quantitative data.
“I think people are starting to recognize
the value of having quantitative information,” Perelson says.
While simple mathematical models that rely on a limited amount of
data can track the movements and
actions of large numbers of players,
explaining the play-by-play actions of
smaller teams within the team is much
harder. In the same way that football has
special teams that are called in only
during a punt or a field goal attempt,
the immune system has special units
— made up of molecular players —
that get called in for certain situations.
Chakraborty says such orchestration creates a “hierarchy” of organized
cooperative processes. With so many
interacting components, calculating the behavior of any particular
individual is not straightforward.
This is where statistical physics can help. By using equations that
take all the interactions into account,
researchers can estimate the likelihood
that a certain result will occur.
Chakraborty, a chemical engineer and
biochemist by training, became interested in immunology after hearing about
a paper on the immunological synapse, a
crucial communications point for T cells
and antigen-presenting cells. Studies
had shown that when the two cells meet,
various receptors and molecules bind
across the synapse, organizing into a
doughnut-shaped pattern. But researchers couldn’t figure out what function the
structure served.
After thinking about its possible signaling function, Chakraborty developed
a series of computer simulations to look
at all the possible biochemical events that
the synapse might influence. The simulations showed that the synapse acts as an
adaptive control device, ramping up to
enhance T cells’ sensitivity to an antigen,
but backing off so as not to overstimulate
or kill the T cell.
Signal sums different mathematical approaches can be used
to understand t cell signaling
depending on the complexity of
the system.
Uniform ordinary differential equations can be used
when signaling components
in the cell are uniformly
distributed, intrinsic chance
events don’t play a role and
average concentration is
what researchers are after.
Dynamic When signaling
molecules are organized
in a specific way and that
organization can change
over time, calculations
using partial differential
equations can supplement
those that use ordinary
differential equations.
Increased complexity and computational time
Scarce if there are fewer
copies of signaling molecules within a cell, then
chance events become
important. in this case,
researchers turn to more
complex calculations, conducting what are called
Monte carlo simulations.
SourcE: a. chakraborty and J. daS/nature
reviews immunology 2010
