Sketchbook is a multi-platform application for the synthesis of Boolean network models. It provides a user-friendly interface for designing a Boolean network sketch and inferring admissible BNs. You can find the newest version of the Sketchbook at this Github repository.
Boolean network sketches, introduced in this paper, serve as a method for integrating various kinds of prior knowledge and experimental data. The sketch consists of an influence graph, partially specified update functions, update function properties, and dynamic properties (including experimental data). Sketchbook presents a way to design all these components and more.
Once you finish designing your sketch, you can run the state-of-the-art synthesis algorithms and symbolically compute all BNs consistent with your requirements. You can then sample individual BNs, or export the results for further analysis with libraries like AEON.py.
In the provided manual.pdf, we discuss the details regarding
- installation,
- editing BN sketches in Sketchbook,
- running BN inference in Sketchbook,
- format and syntax used.
We also provide an introduction to Sketchbook using a short video tutorial that you can find as tutorial.mp4.
This tutorial covers basics on how to edit an sketch and run the inference.
For more details and explanations, see the manual.
The installation is also summarized below, with an additional development guide.
If you used Sketchbook for some academic work, we'd be very happy if you could cite it using the following publication:
Beneš, N., Brim, L., Huvar, O., Pastva, S., & Šafránek, D. (2023).
Boolean network sketches: a unifying framework for logical model inference.
Bioinformatics, 39(4), https://doi.org/10.1093/bioinformatics/btad158.
We provide pre-built binaries and installation files for the application in the release section (includes versions for Windows, Linux and macOS).
To start using Sketchbook, choose the latest release, download binary for your operating system - you choose between .app and .dmg for macOS, .AppImage, .deb and .rpm for Linux, or .exe and .msi for Windows.
If you need a different pre-built binary for a specific platform, let us know!
Note that the binaries are not signed with official developer certificates, so macOS and Windows will most likely require you to grant special permissions to run the app. On newer versions of macOS, the message is that the app is "corrupted". This is still the same issue regarding app certificates. You should run
xattr -c /path/to/biodivine-sketchbook.appto "enable" the app.
Alternatively, if you want to build the tool locally, the instructions are provided in the Development guide below. Note that the local build requires additional dependencies to be installed.
The following instructions describe the local installation of the application and relevant frameworks. We recommend using the pre-built binaries described in the previous section.
For a summary of all technologies and detailed project structure, see project-docs/architecture.md. The directory project-docs also contains other documents relevant to the design/development.
We also provide web-based Rust documentation (including internal modules), currently hosted on these GitHub pages. Instructions to generate Rust documentation locally are given below.
First, make sure you have Rust and NPM installed. For Rust, we recommend following the instructions on rustlang.org. For instructions on NPM and Node installation, feel free to check their website.
On some Linux distributions, additional packages might be needed for developing with Tauri. We recommend checking the setup instructions by Tauri.
We have tested the app using the following versions:
- npm 10.9.0
- node 22.11.0
- rust 1.82.0
Then, after cloning the repository, run npm install to download all JS/TS dependencies. Rust dependencies are downloaded automatically during the build (next step).
To build a release version of the app, run npm run tauri build. It will create an installation bundle at src-tauri/target/release/bundle/ (the exact path will be displayed at the end of the command's standard output). Note that the first build can take a few minutes as the application backend needs to be compiled. Subsequent builds should be a lot faster.
To start the application in debug mode, run npm run tauri dev. Note that upon startup, the application window can be unresponsive for a few seconds when using development mode. This is because the whole application is running in debug mode without any optimizations. This startup delay should be substantially reduced when using the release binaries produced by npm run tauri build.
This section describes the setup and instructions for static analysis tools, testing frameworks, and documentation generation. You don't need any of these to run the application, but they are useful for development. For all the following, run the cargo commands inside src-tauri folder, and npm/npx commands in the main project directory.
For format checking/fixing for the TypeScript part of the project, you can run npx eslint "src/**/*.{js,jsx,ts,tsx}" --config .eslintrc.yml --fix. You can use cargo fmt and cargo clippy for the Rust side.
To run the full Rust test suite, use cargo test. To run the TypeScript tests, run npx vitest --run or npm test.
To automatically generate Rust documentation, execute cargo doc --no-deps --document-private-items.
All previous commands listed together for simplicity:
npx eslint "src/**/*.{js,jsx,ts,tsx}" --config .eslintrc.yml --fix
npx vitest --run
cd src-tauri
cargo fmt
cargo clippy
cargo test
cargo doc --no-deps --document-private-items
We also utilize an end-to-end Selenium-based testing framework. Note that these tests require additional dependencies, and they are limited for Linux and Windows (due to MacOS issues with WebDriver). You should also update the test configs based on your system.
You can follow this detailed tutorial for setup. In short, you should install tauri-driver (with cargo install tauri-driver), and then you will need either WebKitWebDriver on Linux or Microsoft Edge Driver on Windows (make sure that you have updated Microsoft Edge too). The mocha test runner can be installed with npm install mocha chai selenium-webdriver. You might need to update the configuration at the top of the testing script test/test.js, mainly the path to your Sketchbook binary and to your webdriver.
To run the tests, first build the app with cargo tauri build and then use npx mocha (you might need a longer timeout, like npx mocha --timeout 20000).
The framework was tested on Windows with Microsoft Edge WebDriver version 130.0.2849.89.
However, note that we found the testing framework a bit unstable when the testing machine is overloaded with other tasks. Sometimes, the tests do not go through due to internal WebDriver issues, and we are investigating this.
The benchmark models, results, and more details are in data/benchmarks.
There is also a README with further instructions, you can follow it.
Tldr, to run the performance benchmarks, you can use python and execute them all with python3 run_performance_eval.py.
Sketches and datasets relevant to cases studies on biological models and real datasets are in data/real_cases. There is also a README with further details.
An example sketch used to introduce the framework is in data/small_example. Sketch is available in AEON and JSON format. We also provide results of the inference and the resulting sampled BN candidate.