Release v0.5.1 (#44)

* Fix indent level

* Simplify README

* Update README.md

* Update docs

* Set default viz_type to 'original'

* small fix

* Bump version to 0.5.1

* Update ref

* Remove nightly

Author: Hiroaki Imoto

.github/workflows/ci.yml

@@ -10,8 +10,8 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.5'
-          - 'nightly'
+          - '1'
+          - '1.6'
         os:
           - ubuntu-latest
         arch:

Project.toml

@@ -1,7 +1,7 @@
 name = "BioMASS"
 uuid = "324734c7-f323-4536-9335-775d9be9d101"
 authors = ["Hiroaki Imoto <himoto@protein.osaka-u.ac.jp>"]
-version = "0.5.0"
+version = "0.5.1"
 
 [deps]
 CMAEvolutionStrategy = "8d3b24bd-414e-49e0-94fb-163cc3a3e411"

README.md

@@ -7,63 +7,88 @@
 
 This module provides a Julia interface to the [BioMASS](https://github.com/biomass-dev/biomass) parameter estimation.
 
-![](docs/src/assets/result.png)
-
-## Features
+## Installation
 
-BioMASS.jl supports:
+The package is a registered package, and can be installed with `Pkg.add`.
 
-- parameter estimation of ODE/DDE models
-- visualization of simulation results
-- bifurcation analysis
+```julia
+julia> using Pkg; Pkg.add("BioMASS")
+```
 
-## Usage
+or through the `pkg` REPL mode by typing
 
-### Parameter estimation
+```
+] add BioMASS
+```
 
-```julia
-using BioMASS
+## Example
 
-model = Model("./examples/fos_model");
+### Model development
 
-# Estimate unknown model parameters against experimental observations.
-optimize(model, 1, max_generation=20000, allowable_error=0.5)
+This example shows you how to build a simple Michaelis-Menten two-step enzyme catalysis model. [`pasmopy.Text2Model`](https://pasmopy.readthedocs.io/en/latest/model_development.html) allows you to build a BioMASS model from text. You simply describe biochemical reactions and the molecular mechanisms extracted from text are converted into an executable model.
 
-# Save simulation results to figure/ in the model folder
-run_simulation(model, viz_type="best", show_all=true)
+Prepare a text file describing the biochemical reactions (e.g., `michaelis_menten.txt`)
 ```
+E binds S <--> ES | kf=0.003, kr=0.001 | E=100, S=50
+ES dissociates to E and P | kf=0.002, kr=0
 
-### Conversion of optimized parameters into BioMASS format
+@obs Substrate: u[S]
+@obs E_free: u[E]
+@obs E_total: u[E] + u[ES]
+@obs Product: u[P]
+@obs Complex: u[ES]
 
-```julia
-param2biomass("./examples/fos_model")
+@sim tspan: [0, 100]
 ```
 
-## Installation
-
-The package is a registered package, and can be installed with `Pkg.add`.
+Convert the text into an executable model
 
-```julia
-julia> using Pkg; Pkg.add("BioMASS")
+```shell
+$ python  # pasmopy requires Python 3.7+
+```
+```python
+>>> from pasmopy import Text2Model
+>>> description = Text2Model("michaelis_menten.txt", lang="julia")
+>>> description.convert()  # generate 'michaelis_menten_jl/'
 ```
 
-or through the `pkg` REPL mode by typing
+Simulate the model using BioMASS.jl
 
+```shell
+$ julia
 ```
-] add BioMASS
+```julia
+using BioMASS
+
+model = Model("./michaelis_menten_jl");
+run_simulation(model)
 ```
 
-## References
+![michaelis_menten](https://raw.githubusercontent.com/pasmopy/pasmopy/master/docs/_static/img/michaelis_menten_sim.png)
+### Parameter estimation
 
-- Nakakuki, T. _et al._ Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. _Cell_ **141**, 884–896 (2010). https://doi.org/10.1016/j.cell.2010.03.054
+```julia
+using BioMASS
 
-- Inoue, K. _et al._ Oscillation dynamics underlie functional switching of NF-κB for B-cell activation. _npj Syst. Biol. Appl._ **2**, 16024 (2016). https://doi.org/10.1038/npjsba.2016.24
+model = Model("./examples/fos_model");
 
-- Yao, G., Lee, T. J., Mori, S., Nevins, J. R. & You, L. A bistable Rb-E2F switch underlies the restriction point. _Nat. Cell Biol._ **10**, 476–482 (2008). https://doi.org/10.1038/ncb1711
+# Estimate unknown model parameters against experimental observations.
+optimize(model, 1, max_generation=20000, allowable_error=0.5)
+
+# Save simulation results to figure/ in the model folder
+run_simulation(model, viz_type="best", show_all=true)
+
+# Convert optimization results into BioMASS format
+param2biomass("./examples/fos_model")
+```
+
+![estimated_parameter_sets](https://raw.githubusercontent.com/biomass-dev/biomass/master/docs/_static/img/estimated_parameter_sets.png)
+
+## References
 
-- Barr, A. R., Heldt, F. S., Zhang, T., Bakal, C. & Novák, B. A Dynamical Framework for the All-or-None G1/S Transition. _Cell Syst._ **2**, 27–37 (2016). https://doi.org/10.1016/j.cels.2016.01.001
+- Imoto, H., Zhang, S. & Okada, M. A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway. _Cancers_ **12**, 2878 (2020). https://doi.org/10.3390/cancers12102878
 
-- Rata, S. _et al._ Two Interlinked Bistable Switches Govern Mitotic Control in Mammalian Cells. _Curr. Biol._ **28**, 3824-3832.e6 (2018). https://doi.org/10.1016/j.cub.2018.09.059
+- Imoto, H., Yamashiro, S. & Okada, M. A text-based computational framework for patient -specific modeling for classification of cancers. _iScience_ (2022). https://doi.org/10.1016/j.isci.2022.103944
 
 ## License
 

docs/src/index.md

@@ -6,12 +6,6 @@
 
 This module provides a Julia interface to the [BioMASS](https://github.com/biomass-dev/biomass) parameter estimation.
 
-## Features
-
-- Parameter estimation
-- Visualization of simulation results
-- Bifurcation analysis
-
 ## Installation
 
 ```

docs/src/usage/model_construction.md

@@ -0,0 +1,56 @@
+# Model Construction
+
+[`pasmopy.Text2Model`](https://pasmopy.readthedocs.io/en/latest/model_development.html) allows you to build a BioMASS model from text. You simply describe biochemical reactions and the molecular mechanisms extracted from text are converted into an executable model.
+
+## Example
+
+This example shows you how to build a simple Michaelis-Menten two-step enzyme catalysis model with Pasmopy.
+
+> E + S ⇄ ES → E + P
+
+_An enzyme, E, binding to a substrate, S, to form a complex, ES, which in turn releases a product, P, regenerating the original enzyme._
+
+1. Prepare a text file describing biochemical reactions (e.g., `michaelis_menten.txt`)
+
+    ```
+    E binds S <--> ES | kf=0.003, kr=0.001 | E=100, S=50
+    ES dissociates to E and P | kf=0.002, kr=0
+
+    @obs Substrate: u[S]
+    @obs E_free: u[E]
+    @obs E_total: u[E] + u[ES]
+    @obs Product: u[P]
+    @obs Complex: u[ES]
+
+    @sim tspan: [0, 100]
+    ```
+
+1. Convert the text into an executable model
+
+    ```shell
+    $ python
+    ```
+    ```python
+    >>> from pasmopy import Text2Model
+    >>> description = Text2Model("michaelis_menten.txt", lang="julia")
+    >>> description.convert()  # generate 'michaelis_menten_jl/'
+    Model information
+    -----------------
+    2 reactions
+    4 species
+    4 parameters
+    ```
+
+1. Run simulation
+
+    ```shell
+    $ julia
+    ```
+    ```julia
+    using BioMASS
+
+    model = Model("./michaelis_menten_jl");
+    run_simulation(model)
+    ```
+
+    ![michaelis_menten](https://raw.githubusercontent.com/pasmopy/pasmopy/master/docs/_static/img/michaelis_menten_sim.png)

src/continuation.jl

@@ -331,7 +331,7 @@ function new_curve!(
         for i in 2:length(dx)
             if abs(dx[fix_num]) < abs(dx[i])
                 fix_num = i
-        end
+            end
         end
 
             # Stop calc.

src/visualize.jl

@@ -388,7 +388,7 @@ end
 
 function run_simulation(
         model::Model;
-        viz_type::String,
+        viz_type::String="original",
         show_all::Bool=false,
         stdev::Bool=false,
         save_format::String="pdf")