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0:00
So my
name is Jens Nielsen,
and I'm going to talk about
impact of systems biology
on metabolic engineering.
OK, so I have a background
as a chemical engineer
and have always been interested
in biological systems,
so I started to quite early to look
into modeling of microorganisms.
And that has then led to
further also using these models
for engineering these microorganisms
for different kind of applications.
So currently, my group is working
very much on engineering yeast
for production of different
chemicals and biofuels.
But in this context, we're using
a lot of systems biology tools
for analysis of yeast metabolism.
0:37
So we go to the first
slide, which shows basically
the so-called biorefinery concept.
According to this
concept, the objective
is to take biomass, pre-treat that,
degrade it to get sugars that can
then be used by
microbial fermentation
to produce fuels and chemicals,
as illustrated to the right.
And one typically applied
cell factory is yeast,
and I'll come back and talk
a little bit more about that.
But in connection with that,
the enabling technology
is what we call
metabolic engineering.
That means that we are
engineering these factories such
that their metabolism can
efficiently convert these sugars
to these different
fuels and chemicals.
There are already quite
a lot of processes
running like this,
of course, the most
famous being production
of bioethanol.
But in the future, we are likely
to see that some of these process
facilities can be upgraded
to produce new biofuels
and biochemicals that have higher
value and may be better use.
1:29
Next slide shows the typical
process of metabolic engineering.
So it typically starts
with modeling and design.
Typically, one goes in
looks into metabolism
and finds how are we going to
change the metabolism in order
to produce this new
chemical compound.
Then we move on to
string construction.
The resulting strains are
characterized by fermentation.
Phenotypic characterization, where
we kind of narrow in and see what
was the impact of these genetic
modifications we introduced.
Often, we combine that
with a very detailed
phenotypic characterization
using omics analysis.
This can be, for example, looking
into transcription profiling,
proton profiling, or
metabolomics, and so on.
Often when we have these data
here, we need to integrate them
in the context of models, and this
can lead to refinement of the model
and then the model
can be used to further
the identification of
new design strategies.
So this is what we
normally refer to as
the metabolic
engineering cell factory.
And what is very clear is that we
have the things marked to the left
here, is very much the core of what
we normally call systems biology,
namely, high throughput
analysis and also modeling
and integrated analysis
of these kind of data.
So traditionally, modeling
has played an active role
in metabolic engineering, but there
are still relatively few examples
where modeling has really
led to the design of this.
And one of the reasons of this
as we go to the next slide
