LiorZ / Kinetix

This is a simple framework for multiple reaction enzyme kinetics simulation

Kinetix - A simple enzyme kinetics simulation framework

This is a simple framework for single / multiple reaction enzyme kinetics simulation and plotting. It allows one to plot reaction kinetics that follows the reversible Michaelis - Menten model.

Installation

pip install kinetix

Usage

One can use the library to produce figures via a command line interface or programmatically (see below for examples)

CLI

Single reaction

As an example, the reaction kinetics of Glucose Kinase (EC: 2.7.1.2 is displayed as an example)

First, create a definition file in YAML format, that defines the reaction parameters:

name: "Glucose kinase simulation"
reactants: #Concentration of the reactants at the beginning of the reaction (mM)
  glucose: 0.2
  glucose_6_p: 0.0
enzymes: #Concentration of the enzyme(s) at the begining of the reaction (mM)
  gluk: 0.05
reaction: #Kinetic parameters of each of the enzymes for the forward and backward steps of the (reversible) reaction. Note that non-reversible reactions can simple be modeled with high Km for on of the directions.
  gluk:
    fwd: "glucose_6_p"
    back: "glucose"
    km_fwd: 0.24
    km_back: 21
    kcat_fwd: 61
    kcat_back: 15.9

Then, run the simulation with a simple command line and a few arguments:

kinetix glucose_kinase.yaml --plot_out gluc.png --csv_out gluc.csv

A figure showing the progression of the reaction as a function of time is generated:

A csv file containing the data used to generate the figure can also optionally be generated and saved (using the --csv_out flag)

A pathway

Kinetix can also simulate a pathway composed of several enzymes. This example shows a pathway composed of 3 different enzymes used in the production of allulose (D-psicose) a C3 epimer of fructose:

1. Fructose kinase (for the production of fructose-6-phosphate)
2. D-psicose-3-epimerase (converting fructose-6-phosphate to allulose-6-phosphate)
3. Alkaline phosphatase (converting allulose-6-phosphate to allulose)

The flow is similar to the one-enzyme case. First, define a yaml file with all the parameters:

---
name: "Allulose synthesis from fructose"
reactants:
  fructose: 1.8
  fructose_6_p: 0.0
  allulose: 0.0
  allulose_6_p: 0.0
enzymes:
  fruk: 0.05
  alsE: 0.05
  phosphatase: 0.05
reaction:
  fruk:
    fwd: "fructose_6_p"
    back: "fructose"
    km_fwd: 0.24
    km_back: 21
    kcat_fwd: 61
    kcat_back: 15.9
  alsE:
    fwd: "allulose_6_p"
    back: "fructose_6_p"
    km_fwd: 1.6 
    km_back: 1.6 
    kcat_fwd: 46
    kcat_back: 46
  phosphatase:
    fwd: "allulose"
    back: "allulose_6_p"
    km_fwd: 1 
    km_back: 100
    kcat_fwd: 100
    kcat_back: 1

Then, invoke the application just as before:

kinetix allulose.yaml --plot_out examples/figures/alu.svg --csv_out examples/csvs/alu.csv

To produce the reaction figure, which displays the concentration of each of the reactants as a function of time:

Programmatic Access

Kinetix also makes it really easy to simulate and plot a reaction more flexibly via a simple API. Example:

Kinetix/examples/programmatic.py

Lines 8 to 44 in 4d1107d

	r_fructose = Reactant('Fructose',2000) #2M
	r_NAD = Reactant('NAD',0.5) #0.5mM
	r_NADH = Reactant('NADH',0)
	r_co2 = Reactant('Co2',0)
	r_formate = Reactant('foramte',2000) #2M
	r_mannitol = Reactant('mannitol',0)
	p_fru_man = ReactionParameters(r_fructose,r_mannitol,0.24,21,61,15.9) #substrate,product,km_fwd,km_back,kcat_fwd,kcat_back)
	p_NAD_NADH_MDH = ReactionParameters(r_NAD,r_NADH,0.775,0.067,15.9,61)
	p_co2_formate = ReactionParameters(r_co2,r_formate,0.43,7.2,0.03,0.3)
	p_NAD_NADH_formate_dehydro = ReactionParameters(r_NAD,r_NADH,1.2,0.18,0.3,0.03)
	e1 = Enzyme('MDA',0.05)
	e1.add_reaction_params(p_fru_man)
	e1.add_reaction_params(p_NAD_NADH_MDH)
	e2 = Enzyme('Formate dehydrogenase',0.05)
	e2.add_reaction_params(p_co2_formate)
	e2.add_reaction_params(p_NAD_NADH_formate_dehydro)
	reaction = Experiment()
	reaction.add_enzyme(e1)
	reaction.add_enzyme(e2)
	reaction.add_reactant(r_fructose)
	reaction.add_reactant(r_NAD)
	reaction.add_reactant(r_NADH)
	reaction.add_reactant(r_co2)
	reaction.add_reactant(r_formate)
	reaction.add_reactant(r_mannitol)
	result = reaction.run(100000,dt=0.1)
	df = pd.DataFrame(result)
	sns.lineplot(data=df.melt(id_vars='step'),y='value',x='step',hue='variable')
	plt.savefig('test.png')