This brief paper takes an unusual approach to presenting the experimental results of simulating two case studies from [1]: a model of Escherichia coli chemotaxis and a model of Dictyostelium discoideum aggregation. It introduces readers to the proposed methodology of statistical analysis, and then presents its use for replicating the results in a Git repository.
Pictures, graphs, and tables are absent from the paper; however, they are made available after the reader implements the scripts and tools in a Git repository. Detailed installation instructions are available online (http://bit.ly/2WMoi2E). It is unclear whether a repository hosting service or a version control system was used.
Readers will find that they can get the results for the first case study faster using a standard personal computer (PC) equipped with at least a 3.5GHz Intel Core i7 processor and 16GB of random-access memory (RAM). The second case study uses a 64-core server. In both cases, readers can get live results faster with a top-of-the-line PC/server, rather than reading the detailed results on paper. Human eyes move much more slowly than the speed of processors.
Seven required tools for the E. coli and D. discoideum codebases are listed, including Python 3.5 (or later), NumPy, SciPy, and Matplotlib. NumPY is used to compute multidimensional matrices. SciPy is short for scientific computing in Python. Matplotlib speaks for itself; it contains a library of plotting algorithms in Python.
The E. coli codebase depends on pyGPs for Guassian processes and StochPy for stochastic modeling algorithms. The D. discoideum codebase needs GPflow (https://www.gpflow.org/) to build flows in Gaussian process models.
Loreti kept “this replicated computations results report” brief, but should have included a graph or two to give readers a better idea of what [1] is all about. Those interested in the challenges of replicating the results for two biological case studies should read the paper and use the Git repository tools and scripts to replicate results.
