savageau/dst2 repository overview

⁠The Design Space & The Design Space Toolbox

The Design Space methodology and its software implementations have been developed over the last decade (for examples of this methodology, see [1-7]). The method was originally conceived by Michael A. Savageau in 2009 [1-2]. Later, Rick Fasani automated the construction and analysis of the Design Space by introducing the Design Space Toolbox for Matlab. Rick’s contribution also included providing a formal description of Design Space and a detailed explanation of its construction [3]. More recently, Jason Lomnitz introduced the Design Space Toolbox V2 (DST2) [7], a new collection of tools comprised of a stand-alone library, written in the C language, that implements its own symbolic algebra engine and leverages open-source compiled libraries for linear algebra and linear optimization (via the GLPK library). This new toolbox applies concurrent approaches to leverage the parallelizable nature of the System Design Space approach by analyzing each qualitatively-distinct phenotype of the system independently from every other qualitatively-distinct phenotype using multi-threaded concurrent algorithms. The new functionality of the toolbox includes: (1) automating the local stability analysis for model phenotypes; (2) enumerating the vertices of the feasible regions in up to three dimensions, both numerically and symbolically; (3) extending the capabilities of the symbolic algebra component to facilitate the analytical discovery of design principles; (4) defining constraints on the dependent variables and parameters of the system (i.e., to define architectural constraints and biological constraints), among many others.

The DST2 contained in this Docker image is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. The toolbox supports plot generation and data visualization through the NumPy (www.numpy.org⁠ ), Scipy (www.scipy.org⁠) and Matplotlib (www.matplotlib.org⁠) packages.

⁠Docker Image

This image was built by Miguel Á. Valderrama-Gómez ([email protected]⁠) in the Savageau Laboratory (https://savageaulab.wordpress.com/⁠). It aims at facilitating the usage of DST2 in different operating systems.

⁠Getting Started

To use the DST2 Docker Image:

1. Download Image: docker pull savageau/dst2

2. Create Container: docker run -d -p 8888:8888 savageau/dst2

3. Open http://localhost:8888⁠ in a browser window

see special instructions for Windows users: https://savageaulab.wordpress.com/software/⁠

The Ipython Notebook Tutorial contained in the folder /Tutorials provides an introduction to the widget-based user interface of the DST2, which provides access to the core functionalities of the DST2. Users with advanced python programming knowledge can create their own workflows. See Ipython Notebooks contained in the folder /Examples for examples.

The Design Space Toolbox V2 was maintained by Jason G. Lomnitz ([email protected]⁠) as of December 2017. Current software development and maintenance is being performed by Miguel Á. Valderrama-Gómez ([email protected]⁠) under supervision of Michael A. Savageau.

Tutorial and Example notebooks provided with this Docker Image were originally developed by Jason G. Lomnitz (https://jlomnitz.github.io/design-space-toolbox/⁠) and adapted by Miguel Á. Valderrama-Gómez.

⁠Software Installation on Local Machine

The DST2 installation provided in this docker image is self-contained and requires no further modifications.

A customized installation of the DST2 on the local machine can be performed by using the Design Space Toolbox V2 update and installation script (see https://bitbucket.org/jglomnitz/toolbox-update-script⁠ for detailed instructions). This script manages the Git repositories associated with the C Library, Python Interface, as well as a modified version of the GNU Linear Programming Kit (GLPK) library used by the Design Space Toolbox Project.

⁠References

1. Savageau MA, Fasani RA (2009) Qualitatively distinct phenotypes in the design space of biochemical systems. FEBS Lett. Dec 17;583(24):3914-22. Epub. Review. PubMed PMID: 19879266; PubMed Central PMCID: PMC2888490.

2. Savageau MA, Coelho PMBM, Fasani RA, Tolla DA, and Salvador A (2009) Phenotypes and tolerances in the design space of biochemical systems. Proc. Natl. Acad. Sci. U.S.A. 106, 6435–6440.

3. Fasani RA, and Savageau MA (2010) Automated construction and analysis of the design space for biochemical systems. Bioinformatics 26:2601–2609.

4. Savageau MA, and Lomnitz JG (2013) Deconstructing Complex Nonlinear Models in System Design Space, in Discrete and Topological Models in Molecular Biology (Jonoska, N., and Saito, M., Eds.). Springer.

5. Lomnitz JG, and Savageau, MA (2013) Phenotypic deconstruction of gene circuitry. Chaos 23, 025108.

6. Lomnitz JG, and Savageau MA (2014) Strategy Revealing Phenotypic Differences among Synthetic Oscillator Designs. ACS Synth Biol 3(9):686–701.

7. Lomnitz JG, Savageau MA (2016) Design Space Toolbox V2: Automated software enabling a novel phenotype-centric modeling strategy for natural and synthetic biological systems. Front. Genet. 7, 118.