SEML documentation

SEML: a Simplified English Modeling Language for Constructing Biological Models in Julia

• Introduction
• Installation
• API Documentation
• Output Files
  • in Julia
  • in Python
  • in Matlab
• Reproduce Examples
  • reproduce directly
  • reproduce from scratch
• Grammar
• Support or Contact

Introduction

SEML compiler is a code generator that transforms simplified English description of biological network into modeling written in a runnable programming language, such as the Julia, Python and Matlab. See our Github page

Installation

Requirement. In order to use SEML, the user needs to install Julia first.

Installation. Within Julia, press ] to enter pkg> mode. To install SEML.jl, issue

add https://github.com/varnerlab/SEML.git

To use SEML in your project simply issue the command:

using SEML

Test. To test SEML installation, use the following command in pkg> mode:

test SEML

Uninstallation. To delete SEML package use the following command in pkg> mode:

rm SEML

API Documentation

To use SEML in your project simply issue the command:

using SEML
make_model(in_path[, out_path=arg2, host=arg3, model=arg4, lang=arg5])

The make_model() command takes five arguments:

Argument	Required	Description	Options	Default
in_path	Yes	path to input file		none
out_path	No	path to where files are written		./autogeneratedmodel
host	No	host type	bacterial | mammalian	bacterial
model	No	model type	FBA | FVA | Kinetics | rFBA	Kinetics
lang	No	programming language	julia | python | python2 | python3 | matlab	julia

The default solvers in SEML generated codes:

Language	Constraint-based model	Kinetic model
Julia	ode23 in ODE.jl	GLPK.jl
Python	scipy.integrate.odeint	scipy.optimize.linprog
Matlab	builtin ode23	GLPK

Output Files

in Julia

FBA/FVA model files:

File	Description
DataDictionary.jl	contains all data of the model, including flux bounds, species concentration bounds, and objective coefficients, etc.
FluxDriver.jl	interface with GLPK solver to run FBA
FVA.jl	implementation of fastFVA from gudmundsson2010computationally
InputFile	SEML description of the model
Include.jl	contains all the include statements for the project.
Solve.jl	interface to run the simulation
stoichiometry.dat	Stoichiometric matrix of the model

rFBA model files:

File	Description
DataDictionary.jl	contains all data of the model, including flux bounds, species concentration bounds, and objective coefficients, etc.
FluxDriver.jl	interface with GLPK solver to run FBA
FVA.jl	implementation of fastFVA from gudmundsson2010computationally
InputFile	SEML description of the model
Include.jl	contains all the include statements for the project.
rRules.jl	encode regulatory rules
Solve.jl	interface to run the simulation
stoichiometry.dat	Stoichiometric matrix of the model

Kinetic model files:

File	Description
Balances.jl	encodes mass balance of the model
DataDictionary.jl	contains all data of the model, including initial condition, kinetic constants and Monod affinity constants, etc.
InputFile	SEML description of the model
include.jl	contains all the include statements for the project.
Kinetics.jl	calculates kinetic rates
SolveBalances.jl	interface to run the simulation
stoichiometry.dat	Stoichiometric matrix of the model

in Python

FBA/FVA model files:

File	Description
DataDictionary.py	contains all data of the model, including flux bounds, species concentration bounds, and objective coefficients, etc.
FluxDriver.py	interface with scipy.optimize.linprog to run FBA
FVA.py	implementation of fastFVA from gudmundsson2010computationally
InputFile	SEML description of the model
Solve.py	interface to run the simulation
stoichiometry.dat	Stoichiometric matrix of the model

rFBA model files:

File	Description
DataDictionary.py	contains all data of the model, including flux bounds, species concentration bounds, and objective coefficients, etc.
FluxDriver.py	interface with scipy.optimize.linprog to run FBA
FVA.py	implementation of fastFVA from gudmundsson2010computationally
InputFile	SEML description of the model
Solve.py	interface to run the simulation
rRules.py	encode regulatory rules
stoichiometry.dat	Stoichiometric matrix of the model

Kinetic model files:

File	Description
Balances.py	encodes mass balance of the model
DataDictionary.py	contains all data of the model, including initial condition, kinetic constants and Monod affinity constants, etc.
InputFile	SEML description of the model
Kinetics.py	calculates kinetic rates
SolveBalances.py	interface to run the simulation
stoichiometry.dat	Stoichiometric matrix of the model

in Matlab

FBA/FVA model files:

File	Description
DataDictionary.m	contains all data of the model, including flux bounds, species concentration bounds, and objective coefficients, etc.
FluxDriver.m	interface with GLPK solver to run FBA
FVA.m	implementation of fastFVA from gudmundsson2010computationally
InputFile	SEML description of the model
maximizeProductDictionary.m	modify the objective coefficient array.
Solve.m	interface to run the simulation
stoichiometry.dat	Stoichiometric matrix of the model

rFBA model files:

File	Description
DataDictionary.m	contains all data of the model, including flux bounds, species concentration bounds, and objective coefficients, etc.
FluxDriver.m	interface with GLPK solver to run FBA
FVA.m	implementation of fastFVA from gudmundsson2010computationally
InputFile	SEML description of the model
maximizeProductDictionary.m	modify the objective coefficient array.
rRules.m	encode regulatory rules
Solve.m	interface to run the simulation
stoichiometry.dat	Stoichiometric matrix of the model

Kinetic model files:

File	Description
Balances.m	encodes mass balance of the model
DataDictionary.m	contains all data of the model, including initial condition, kinetic constants and Monod affinity constants, etc.
InputFile	SEML description of the model
Kinetics.m	calculates kinetic rates
SolveBalances.m	interface to run the simulation
stoichiometry.dat	Stoichiometric matrix of the model

Reproduce Examples

The following Julia packages are required to run the demontration examples in Julia:

• GLPK
• ODE
• PyPlot

They can be installed by running in Julia:

using Pkg
]
(v1.1) pkg> add GLPK ODE PyPlot

reproduce directly

1. Download the example folder
2. Open a terminal window, cd to the example folder
3. To run the FBA model (Fig. 2):

• cd to fauto folder
• run julia Solve.jl

3. To run the kinetic model (Fig. 5):

• cd to kiauto folder
• run julia Simulation.jl (Note that while running the kinetic model, some figure(s) will be generated and pause the program, close the unwanted figure windows to get the final color figures as Fig. 5.)

reproduce from scratch

1. Download the example/testcase folder to get two models in SEML
2. Open a terminal window, go into julia and run using SEML
3. To build the FBA model (Fig. 2):

• run make_model("PathTo/fbacase.txt", model="FBA", out_path="outpath1")
• change the following parameters to corresponding values in the generated DataDictionary.jl

default_bounds_array = [
  ...
  0 1.0; # 2 1.0*m_A_c<catalyze:>1.0*m_B_c
  0 1.0; # 3 1.0*m_B_c<catalyze:>1.0*m_A_c
  0 2.0; # 4 1.0*m_A_c<catalyze:>1.0*m_C_c
      ...
]
...julia
species_bounds_array = [
  -10.0 10.0; # 1 m_A_e
  -10.0 10.0; # 2 m_B_e
  -10.0 10.0; # 3 m_C_e
   ...
]

• cd to outpath1 folder
• run julia Solve.jl

4. To build the kinetic model (Fig. 4 and 5):

• run make_model("PathTo/kinetcase.dat", out_path="outpath2")
• change the following parameters to corresponding values in the generated DataDictionary.jl

kcat_signaling = ones(7)  # kcat[#reaction]: reaction name
kcat_signaling[1] = 1.1e-3  # kcat: 1.0*m_A_e<uptake:1.0*p_TA_c>1.0*m_A_c
kcat_signaling[2] = 8e-4  # kcat: 1.0*m_B_c<secrete:1.0*p_TB_c>1.0*m_B_e
kcat_signaling[3] = 9e-4  # kcat: 1.0*m_C_c<secrete:1.0*p_TC_c>1.0*m_C_e
kcat_signaling[4] = 1.8e-4  # kcat: 1.0*m_A_c<catalyze:1.0*p_E1_c>1.0*m_B_c
...
W_value_dict = Dict{String, Float64}()
W_value_dict["W~m_B_c~mRNA_E4_c"] = 0.1
W_value_dict["W~m_A_c~mRNA_E2_c"] = 0.7
W_value_dict["W~m_C_c~mRNA_E3_c"] = 0.2
W_value_dict["W~m_A_e~mRNA_TA_c"] = 0.1
...
W_value_dict["W~m_A_c~mRNA_E1_c"] = 0.6
W_value_dict["W~m_C_c~mRNA_TC_c"] = 1.1

• cd to outpath2 folder
• run julia Simulation.jl (Note that while running the kinetic model, some figure(s) will be generated and pause the program, close the unwanted figure windows to get the final color figures as Fig. 5.)

Support or Contact

Having trouble at installation or function? Feel free to contact VarnerLab or authors.