= Practical 5 =
= Linear Programming and Flux Balance Analysis with ScrumPy =
In   current research into biofuels, there is interest in investigating the   feasibility of using microorganisms to produce alkanes for biodiesel   from renewable carbon sources.  The starting points for alkane formation   are fatty acids, which are formed by the fatty acid biosynthesis   pathway.  Here we will investigate the feasibility and efficiency of   making a representative C15 alkane in ''E. coli'' from various carbon substrates, starting with glucose.

We will use a small model of about 70 reactions that represents the central carbon metabolism of ''E. coli''.    It is a derivative of a model developed in Srienc's group (Trinh, C.   T., Unrean, P., Srienc, F., 2008. Applied and environmental   microbiology.74, 3634-3643) for designing metabolic engineering   strategies. The model was used in our theoretical/experimental project to increase alkane prooduction in E coli: Fatma'' et al, Model-assisted metabolic engineering of Escherichia coli for long chain alkane and alcohol production'', Metabolic Engineering, 45, 134-141 (2018). [[https://doi.org/10.1016/j.ymben.2018.01.002|DOI]]

Before you start, read the documentation of the {{{ScrumPy}}} LP module, [[http://mudshark.brookes.ac.uk/ScrumPy/Doc/LinProg|here]]. It would be a good idea to open this page in a separate tab so that you can refer to it as you work on the practical. (Note that the examples in the documentation are '''not for the same model''' that you will be using; they are generic examples.)

 1. Download the archive containing the [[http://mudsharkstatic.brookes.ac.uk/Download/Models/Nepal/EcoliCCMforNepal.zip|model]] and extract the files.
  a. Start !ScrumPy   in the folder containing the .spy files and load the top-level model   file EcoliCCM.spy to create a model object.  Note that the model is   created in a modular fashion, and the top-level file will load the   different components of the model and open a window for each.
  a. Examine   how the model is created.  Note that the reactions that you need to   manipulate for this practical are those contained in Transport.spy.
  a. If you want to check the details of any of the reactions or metabolites in the database-derived component of the model - !AutoColi.spy - you can paste the reaction or metabolite identifier into the search box at [[http://www.metacyc.org|MetaCyc]].

 1. Your   first   task is to set up and solve an LP problem where the objective   is to   minimise glucose consumption, as sole carbon source, while   producing 1 flux unit of palmitate.
  a. Create the LP object with the E coli model as argument as shown in the first steps of the documentation.
  a. Constrain the flux of the pentadecane transport reaction to -1.0 using the {{{SetFixedFlux}}} function of the LP object.<<BR>>
   . [[Meetings/Nepal2018/Prac5/Hint1|Hint 1]]

  a. As   can be seen from Transport.spy, the model can allow both uptake and   production of a number of carbon compounds.  To make sure that glucose   is used as the only substrate, the import of the other carbon substrates   must be constrained to zero, though their export can be allowed so  that  we can determine whether production of pentadecane requires the  formation  of any co-products. This can be done with the {{{SetFluxBounds}}}   function, which constrains fluxes within a specified range.  For   example, to prevent uptake of ethanol and acetate, whilst allowing their   production, we could write the following.  Check that you understand   how the range specifiers below work :<<BR>>
   . {{{#!highlight python

lp = m.GetLP()

Bounds = {
     "eth_tx":(None, 0),
     "ACE_tx":(None, 0)
}

lp.SetFluxBounds(Bounds)
}}}
  a. Since the optimisation   direction is minimisation, and this is set by default, you don't need to   change the direction, though the documentation explains how to do  this.  <<BR>>
  a. Next,     identify the name of the glucose uptake reaction that should be   minimised and enter this as an argument to the objective function method   described in the documentation. (Note that the argument   must be a   list of reaction(s), i.e. your argument should be a list  with  one   reaction).<<BR>>
  a. Solve the LP. (ie lp.Solve())  The message 'Optimal solution' should appear.  To obtain the solution use the LP method {{{GetPrimSol()}}}.     This method returns a dictionary object of reactions in the solution    as  keys and flux values as values, so for convenience assign a name  to  this solution, e.g. ''sol''.  Examine the solution as follows:<<BR>>
   . {{{#!highlight python
for k in sol.keys():
    print k, sol[k]
}}}
  a. What is the  objective   value, i.e. the flux of the glucose uptake reaction,   representing the   minimum amount of glucose to make 1 pentadecane?  What fraction of the   glucose carbon is converted to pentadecane?  What are the relative fluxes   of glucose through glycolysis (e.g. 6PFRUCTPHOS-RXN) and the pentose   phosphate pathway (e.g. GLU6PDEHYDROG-RXN)? <<BR>>
 1. Now   determine which of the other carbon metabolites listed in  Transport.spy  can function as substrates for fatty acid production.  To  do this
  a. Generate a new lp object.
  a. Set the flux constraint on pentadecane transport, and set appropriate flux bounds on the other carbon metabolites;
  a. Set uptake of your target substrate as the minimisation objective.
  a. For those substrates that can be used, what is the carbon conversion efficiency to pentadecane? <<BR>>
 1. The results so far have corresponded to aerobic conditions.  Pentadecane cannot be made in strict anaerobic conditions as the final reaction requires one oxygen per pentadecane.  It might be possible to make pentadecane in micro-aerobic condtions from some of the substrates.  Which ones can be utilised?
  a. Represent micro-aerobic conditons by constraining the oxygen transport flux to 1.0 (the same as the pentadecane formation rate).
  a. Repeat   the analyses above.  For those substrates that can be used, determine   any change in the carbon conversion efficiency and whether co-products   are formed.