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- So we are here today with Danny Seara,
who is a graduate student in physics,
and in the Integrated Graduate Program
in Physical and Engineering Biology at Yale.
So Danny,
it'd be great to hear a bit about
what systems biology means to you.
- Biology is complicated
because not only there are a lot of
moving parts inside of a cell,
but those parts all interact with each other.
And so systems biology really studies
the interactions
between all these different components,
which are full of feedbacks and feed forwards,
and nonlinearities that lead
to different phenotypes
that we see in biology.
So really, systems biology is the study of when
one plus one doesn't necessarily equal two, in biology.
- I see. Sounds complex.
And so how does that all relate to cancer?
- Well, like the rest of biology, cancer is complicated.
And I think the biggest issue for me
is that cancer is not just one disease,
it's a whole family of diseases
that all act the same way
with cells dividing a lot,
and migrating a lot when they're not supposed to.
And so if we instead focus on say the generic interactions
that can lead to all of these kind of stereotyped behaviors
that we see in cancer,
rather than focusing on the individual components
that lead to the individual diseases,
then maybe we can get a better understanding
of what cancer actually is.
And this is the type of thinking that systems biology brings
to the problem.
- And, you know, systems biology,
I mean, it has the word "biology" in it,
and cancer, you tend to think of
sort of very medical things,
and again, biology.
But yet you're a theoretical physicist.
And so you conduct all of your research
and you do your projects on the computer.
And so can you tell us a bit more about your role
as a physicist in cancer systems biology?
- So, like I said before,
systems biology is the study
of how a lot-
when a lot of different things interact,
what kind of behavior comes out of that?
And it turns out physics
has an entire field that studies this.
It's called statistical physics.
And so I use the ideas of statistical physics,
things that were traditionally applied
to heat engines, and fluids and gases,
and try to use those same ideas
to try to understand the biological problems
that are being studied by the people in my lab,
who are everything from biologists,
to engineers, to physicists like me.
- So what do you actually do in the lab?
I mean,
what's your project?
You know, is it more on the fundamental side
or the applied side of research,
and how does that relate to cancer systems biology?
- I'm definitely more on the basic side.
And what I do is I am interested in
ideas of metabolism,
or how a cell uses the energy that it gets
by using the food that you eat.
And so-
But the problem is that
measuring energy use in a cell
is quite hard without really disturbing
what the cell is doing.
And we'd want to watch the cell on its natural-
in its natural state.
So I come up with algorithms that-
and tools that can estimate how much energy is being used
inside of the cells
based on the experimental data that my lab mates take.
And so with that,
we can hopefully try to understand
how healthy versus diseased cells use their energy.
What sort of processes are they really focusing on?
And then hopefully that can give us some insight into-
into these diseases specifically.
But I'm much more on the basic end
and just trying to figure out
how do we measure that energy,
or metabolic costs in the first place?
- That's really important, right?
Cause a lot of times those are-
that's the information that then gives us really good ideas
on how to apply, you know, new technologies
to now known complex diseases or systems.
- Right, exactly.
I mean, if we know
where a disease cell is putting all of its energy,
then we also know
where we can try
to treat with drugs as well.
And so hopefully this can also be used,
not only as a quantifying tool of how much,
but also sort of a discovery-
a tool of discovery.
Exactly figuring out where things are being
reallocated when a cell is diseased versus healthy.
- Great! Well thanks so much for your insight
and keep up the good work.
- Yeah. Thank you so much, Doro.
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