Mambo md seeds

Mambo MG

From One Leg Up Cooperative, Mambo MG is a THC-dominant hybrid crossing Platinum Romulan with New Orleans Purple to produce green and purple buds covered with golden hairs. Its terpene profile gives off a spicy, musky, and skunky flavor. Mambo MG may leave you feeling talkative and hungry before slipping into a sleepy relaxation.

From One Leg Up Cooperative, Mambo MG is a THC-dominant hybrid crossing Platinum Romulan with New Orleans Purple to produce green and purple buds covered with golden hairs. Its terpene profile gives off a spicy, musky, and skunky flavor. Mambo MG may leave you feeling talkative and hungry before slipping into a sleepy relaxation.

Mambo md seeds

Codes for the functions that were used in the MAMBO algorithm described by Garza et al. (2018). These functions were written on Cython and are intended to be used integrated with COBRApy (https://github.com/opencobra/cobrapy) on a python2.7 platform, using a collection of genome-scale metabolic models with model SEED identifiers for metabolites and reactions.

The script “bottom_up_ecology.pyx” contains the functions for: 1) identifying exchange reactions, 2) setting-up an initial environment 3) Performing a Markov-Chain step to determine the accept/reject probabilities for random changes to the environment. In order to use these functions, one must install the prerequisites listed below and compile the script using the “setup_bte_functions.py” script.

The functions are written on cython, thus they require compilation. To do this, one can use the terminal command to compile to their working directory:

$ setup.py build_ext –inplace

Other ways of compiling Cython code should also be available. Check here

After compilation, the script may be imported into a python script by “import bottom_up_ecology”. An example of usage for maximizing the correlation between steady-state solutions for biomass functions and the relative abundance of species determined by metagenomic experiments is given below. For more details, see Garza et al.(2016).

See also  Flodawg seeds

###Example of MCMC optimization for this example, a python environment is assumed. It is also assumed that one has a group of GSMMs on a python list named “models” and, in the same order, a numpy array of numbers with the relative abundance of bacteria named “relative_abundance”

import bottom_up_ecology_functions as bte

evolution_of_exchange_reactions = <> # A python dictionary to store the results for the MCMC

initial_environment = bte.starting_environment(models) # defines an initial random environment

metabs = initial_environment.keys() # defines the metabolites that will conform the search-space.

for i in xrange(1000): # 1000 Markov chain optimization steps

List of dependencies with the versions that were used for the data described on the manuscript:

  • COBRApy (v. 0.4.1)
  • Numpy (v. 1.11.0)
  • Gurobi (v. 6.5)
  • Cython (v. 0.24)
  • Scipy (v. 0.17.1)

1562 genome-scale metabolic models were used in the study. These models can be obtained from this repository, by downloding the files: