Transcript adaptive_evolution

Escheria coli K-12 undergoes
adaptive evolution to achieve in
silico predicted optimal growth
Rafael U. Ibarra, Jeremy S. Edwards and Bernhard Ø. Palsson
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Pleased to meet you…
Escheria coli is a bacteria more commonly known as E.coli
Helloooo
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Pleased to meet you…
• E. Coli is one of the main species of bacteria that live in
the lower intestines of warm-blooded animals, including
birds and mammals
• They are necessary for the proper digestion of food.
• The name comes from its discoverer, Theodor Escherich.
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In silico results vs. in vivo
in silico is an expression used to mean "performed on
computer or via computer simulation."
In vivo is used to indicate the presence of a whole/living
organism (for example, in an experiment), in distinction to a
partial or dead organism or a computer model.
This paper tries to determine the relation between in silico
results and in vivo results.
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In silico vs. in vivo results
In the previous paper we saw how to analyze a reaction
network through the use of mathematical techniques and
computer programs.
In particular we showed that every feasible flux in the
reaction network can be characterized mathematically.
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In silico vs. in vivo results
Using what we have seen we can introduce constraints that
must be upheld such as: mass conservation, rules of
thermodynamics etc.
After imposing these constraints we end up with a feasible
solution space (feasible fluxes).
Properties of the organism are a function of the different
reactions taking place.
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Optimal growth
Growth of an organism for example is a function of the
reactions taking place within the reaction network.
Using techniques based on linear optimization (such as
linear programming) we can find the optimal value for growth
and the reactions necessary to insure it.
In this experiment it was checked whether an organism, after
going through an adaptive evolution process, will indeed
achieve the optimal growth calculate via the in silico model.
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The experiment
E. Coli bacteria was grown with the only source of carbon
available being malate.
They used various concentrations of the substrate (malate)
and tempretures to vary the malate uptake rate (MUR).
The MUR, the Oxygen uptake rate (OUR) and growth rate
were measured.
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The experiment
From the in silico model we know that optimal growth is
achieved on the line denoted LO.
In this region
there is too much
oxygen causing
a suboptimal
growth.
In this region
there is too
much malate
causing negative
growth.
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The Results
After running the experiment for 30 days (500 generations)
the following results were observed:
Day 30
All the
measurements
taken were
roughly on the
line of optimality
Day 0
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The Results
If we look at the results on a 2D plane:
Day 30
Day 0
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The Results
We saw that the results were all roughly on the line of
optimality as predicted by the in silico models.
Furthermore we saw that after 30 days the E. coli evolved to
be able to achieve a better growth rate by using malate.
The results were still on the line of optimality but the E. coli
was now able to utilize more malate and oxygen to achieve
the higher growth rate.
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The experiment
The experiment was repeated while using glucose instead of
malate.
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The Results
If we look at the results on a 2D plane:
Day 40
Day 0
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One more experiment
E. Coli bacteria was grown with the only source of carbon
available being glycerol.
The experiment was performed twice at a temperature of 30
degrees Celsius (E1 and E2) and once at a temperature of
37 degrees Celsius (E3).
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The Results
Over a period of 60 days the following measurments were
taken:
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The Results
At the beginning of the experiment the measurements were
scattered and not on the line of optimality.
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The Results
Throughout days 1-40 measurements were taken every 40
days and the we can clearly see convergernce towards the
line of optimality.
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The Results
On day 40 all the measurements were spread around the
line of optimality:
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The Results
The experiment was checked for an additional 20 days
where no changes were detected.
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