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@@ -33,7 +33,7 @@ This tutorial will walk you through the links below, which contain a breakdown o
 
 To work through this example, a variety of python packages will be necessary in addition to [IMP](https://integrativemodeling.org/). These packages are [numpy](https://numpy.org/), [os](https://docs.python.org/3/library/os.html), [warnings](https://docs.python.org/3/library/warnings.html), [sys](https://docs.python.org/3/library/sys.html), [itertools](https://docs.python.org/3/library/itertools.html), [pandas](https://pandas.pydata.org/), [matplotlib](https://matplotlib.org/), [pyRMSD](https://pypi.org/project/pyRMSD/), and [graphviz](https://graphviz.org/). Optionally, [UCSF Chimera](https://www.rbvi.ucsf.edu/chimera/), [UCSF ChimeraX](https://www.cgl.ucsf.edu/chimerax/), [gnuplot](http://www.gnuplot.info/), and [MATLAB](https://www.mathworks.com/products/matlab.html) can be used for visualizing structures or plotting data.
 
-The code for this tutorial is available through its [GitHub link](https://github.com/salilab/imp_spatiotemporal_tutorial). A complete, worked example of the tutorial is available in the `modeling` folder; meanwhile, the necessary code and input information without the resulting model are available in the `modeling_empty` folder, from which the user can run the code for themselves.
+The code for this tutorial is available through its [GitHub link](https://github.com/salilab/imp_spatiotemporal_tutorial). A complete, worked example of the tutorial is available in the `modeling` folder; meanwhile, the necessary code and input information without the resulting model are available in the `modeling_empty` folder, from which the user can run the code for themselves. A [Google Colab notebook](https://colab.research.google.com/github/salilab/imp_spatiotemporal_tutorial/blob/main/Jupyter/spatiotemporal-colab.ipynb) with the less computationally expensive steps is also available.
 
 Our tutorial begins by [modeling the heterogeneity of the system.] (@ref heterogeneity)