Inferring microbial interactions - GeomScale/gsoc21 GitHub Wiki

Inferring microbial interactions is among the most challenging and fundamental tasks in modern biology. Metabolic networks can provide essential insight on the underlying mechanisms that govern biological systems; thus they have been proved the most valuable in microbial interaction inference. Uniform random sampling is commonly used for obtaining an accurate picture of the whole solution space of a metabolic network. Sampling on metabolic networks at the microbial community level allows the robust highlighting of competition or/and cooperation among the organisms.

Since now, constraint-based metabolic modelling approaches at the community level have been implemented in the framework of the COBRA Toolbox on the mgPipe pipeline. micom is a Python package addressing the same challenges. However, to the best of our knowledge, there is no software package supporting random sampling at the community level.

The project could be split in the following tasks:

• The student should understand the theory and the algorithms of random sampling on metabolic networks as well as the basics of microbial interactions, by reading the literature and contacting the mentors in the bonding period.
• Apply sampling methods implemented in volestipy
• Implement sampling for pairs of metabolic networks.
• Extract reactions found to compete robustly.
• Write tests and documentation

This project will benefit the most a great range of biological fields; from medicine to ecology, microbial interactions is the key to explain the underlying mechasnisms. The Python interface will allow the wide use of the software by the biological community.

Students, please contact both mentors below after completing at least one of the tests below.

• Elias Tsigaridas <elias.tsigaridas at inria.fr> is an expert in computational nonlinear algebra and geometry with experience in mathematical software. He has contributed to the implementation, in C and C++, of several solving algorithms for various open source computer algebra libraries and has previous GSOC mentoring experience with the R-project (2019).

• Apostolos Chalkis <tolis.chal at gmail.com> is a PhD student in Computer Science. His research focuses on mathematical computing, optimization and computational finance. He has previous experience in GSoC 2018 and 2019 as a student under Org. R-project, implementing state-of-the-art algorithms for sampling from high dimensional multivariate distributions. He is one of the authors of volesti.

• Zafeirakis Zafeirakopoulos is an expert in implementing and benchmarking geometric and algebraic algorithms and has previous GSOC experience with the R-project (2018, 2019) and GeomScale (2020).

Students, please do one or more of the following tests before contacting the mentors above.

Easy: Compile and run volesti. Use the R extension to visualize sampling in a polytope.

Medium: Use the R extension to sample on the flux space of the metabolic network of E.coli

Hard: Move the sample points returned from the Medium test in Python and plot the distributions of two co-ordinates (reaction fluxes).

💯 IMPORTANT: For tips ask the mentors! :100:

Students, please post a link to your test results here.

Petros Petsinis, https://github.com/petsi97/geomscale-gsoc2021-tests