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Escheria coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth

Escheria coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Rafael U. Ibarra, Jeremy S. Edwards and Bernhard Ø. Palsson. Pleased to meet you…. Helloooo. Escheria coli is a bacteria more commonly known as E.coli. Pleased to meet you….

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Escheria coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth

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  1. Escheria coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth Rafael U. Ibarra, Jeremy S. Edwards and Bernhard Ø. Palsson

  2. Pleased to meet you… Helloooo Escheria coli is a bacteria more commonly known as E.coli

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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 malate causing negative growth. In this region there is too much oxygen causing a suboptimal growth.

  10. 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

  11. The Results If we look at the results on a 2D plane: Day 30 Day 0

  12. 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.

  13. The experiment The experiment was repeated while using glucose instead of malate.

  14. The Results If we look at the results on a 2D plane: Day 40 Day 0

  15. 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).

  16. The Results Over a period of 60 days the following measurments were taken:

  17. The Results At the beginning of the experiment the measurements were scattered and not on the line of optimality.

  18. The Results Throughout days 1-40 measurements were taken every 40 days and the we can clearly see convergernce towards the line of optimality.

  19. The Results On day 40 all the measurements were spread around the line of optimality:

  20. The Results The experiment was checked for an additional 20 days where no changes were detected.

  21. Executive Producer: Guy Flysher Directed by: Guy Flysher Music Consultant: Guy Flysher Make Up: Guy Flysher Sound Effects: Guy Flysher Special Effects: Guy Flysher Stunt Double: Guy Flysher Casting By Guy Flysher Caterer Guy Flysher Nerd Images Courtesy of Google

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