To use the package, the python module synbioweaver must be on the PYTHONPATH.
The usual python dependencies are required for running the base functionality: numpy, scipy, matplotlib. If you would like to export designs into sbml then you also need libsbml. The more involved examples require additional tools and are outlined below
An overview and tutorial is provided at the following location http://synbioweaver.readthedocs.io/en/latest/
A number of simple examples to illustrate how the package works are included in the folder examples/tutorial. More involved examples demonstrating the flexibility and applicability of the framework are given in the other folders in examples. These include:
Weaving together gene circuits
examples/weaving-circuits
Generation of transfer curves based on part libraries
examples/transfer-curves
Specifying different gene circuit designs and generating models
examples/model-generation
The circuits include:
- simple implementations of constitutive and inducible GFP expression
- the lux-AHL system
- the sender-receiver system found in Basu et. al. Spatiotemporal control of gene expression with pulse-generating networks (2004) http://www.pnas.org/content/101/17/6355
- the toggle-switch system found in Litcofsky et. al. Iterative plug-and-play methodology for constructing and modifying synthetic gene networks (2012) https://www.nature.com/articles/nmeth.2205
- the oscillator from Stricker et. al. A fast, robust and tunable synthetic gene oscillator (2008) https://www.nature.com/articles/nature07389
- the system described in Ceroni et. al. (2018) https://www.nature.com/articles/nmeth.4635 which uses combination of guide RNA and dCas9 to provide burden driven negative feedback
- a generic design for a whole-cell biosensor that makes use of an expressed two component system to drive expression of a reporter
Multicellular logic gates
examples/logic-gates
This example is based on the XOR gate described in Tamsir
et. al. Robust multicellular computing using genetically encoded NOR
gates and chemical ‘wires’ (2011)
https://www.nature.com/articles/nature09565. It also shows how
aspects can be used to generate input-output transfer curves for different circuits
using characterisation data.
Rule-based modelling
examples/rule-based-model
A design for the repressilator is converted into a rule-based model specified in the Kappa language and then subsequently simulated stochastically using KaSim. This example requires KaSim version 3.5 to be installed in /usr/local/share. This can be downloaded as a binary from https://github.com/Kappa-Dev/KaSim/releases
Automated model generation and simulation using cuda-sim
examples/context-simulation
For GPU based biochemical network simulation requires a suitable Nvidia GPU device, CUDA, PyCUDA must be installed and cuda-sim must be on PYTHONPATH
Bayesian inference using ABC-SysBio
examples/characterisation
In this example the OD and fluorescence of a simple constitutive GFP circuit are simulated over time. The resultant data are fit to an ODE model of the system using Bayesian statistics. This currently uses cuda-sim. In addition it requires the module abcsysbio to be on the PYTHONPATH.
Reverse engineering context dependence
examples/context-inference
This example is inspired by Catanach et. al. Context Dependence of Biological Circuits (2018) https://www.biorxiv.org/content/early/2018/07/03/360040. Multiple models are generated of a circuit under two contexts. Bayesian model selection is applied to formally test whether the systems have the same parameters.