Tutorial in sampling based metabolic flux analysis in biology - GeomScale/gsod2020 GitHub Wiki

Overview

Sampling-based metabolic flux analysis is important tools that can model biological system as a metabolic network. The physical and biochemical constraints that define a metabolic network have the form of a polyhedral convex set, which in turn is the set of feasible flux vectors. Uniform sampling of this set provides an unbiased characterization of its metabolic capabilities. GeomScale's software provides the fastest uniform sampling functionality in high dimensions. In addition, it provides subroutines useful for this biological application; in particular, sampling steady states of a metabolic network consists of 4 main computational steps and GeomScale provides plenty of alternatives for each step. This project will result a detailed tutorial and documentation which will present all the methods for each step.

Related work

See the tutorial and the documentation of opencobra MATLAB toolbox.

Details of your coding project

The project could be split in the following tasks:

  • Documentation of the rounding methods.
  • Documentation of the uniform high dimensional samplers.
  • Documentation of the diagnostic tools (practical criteria to stop sampling).
  • Documentation of the plotting functions that GeomScale provides.
  • Tutorial based on real data and metabolic networks.

Mentors

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

  • Vissarion Fisikopoulos <vissarion.fisikopoulos at gmail.com> is an expert in mathematical software, computational geometry and optimization, and has previous GSOC mentoring experience with Boost C++ libraries (2016-2019) and the R-project (2017-2019).

  • 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.

  • 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).