Metabolic interactions inference using community flux sampling - GeomScale/gsoc24 GitHub Wiki

Overview

The project will enhance dingo by incorporating various approaches for modeling microbial communities and conducting sampling in their flux space.

These will include at least:

  • a bag-of-reactions model: the union of all reactions and metabolites within at least one model of the community members
  • a bag of genomes model: each member of the community is depicted as a separate compartment, and their interactions are facilitated by shuttle reactions.

Flux sampling will be then conducted using the supported samplers within the dingo framework with or without a given set of elementary conversion modes. The latter, would focus on the exchange reactions of the community model.

Cross-feeding interactions will be inferred from the samples based on the dependencies between fluxes of the pair.

The results obtained will undergo validation against experimental data. A comparative analysis of the employed modeling approaches will be performed.

Related work

Community Genome-Scale models have been used for some time now [1]. Yet, there are several ways to model a community; each of whom having their own pros and cons [2]. Random flux sampling has become feasible for GEMs in recent years [3], but to scale pathway analysis it is necessary to focus on a subset of pathways or a subnetwork of the full metabolic network.

All features build is going to be in Python. Methods such as ecmtool will be investigated to get elementary conversion modes. All methods and analyses will be perform in Python.

References:

[1] Apostolos Chalkis, Vissarion Fisikopoulos, Elias Tsigaridas, Haris Zafeiropoulos, Geometric algorithms for sampling the flux space of metabolic networks, 2021.

[2] Khandelwal, Ruchir A., et al. "Community flux balance analysis for microbial consortia at balanced growth." PloS one 8.5 (2013): e64567.

[2] Khandelwal, R. A., Olivier, B. G., Röling, W. F., Teusink, B., & Bruggeman, F. J. (2013). Community flux balance analysis for microbial consortia at balanced growth. PloS one, 8(5), e64567.

[3] Wedmark, Y. K., Vik, J. O., & Øyås, O. (2023). A hierarchy of metabolite exchanges in metabolic models of microbial species and communities. bioRxiv, 2023-09.

Details of your coding project

ecmtool can be used for get the elementary flux (conversion) modes. the minimal set of conformal generators of a conversion cone (Urbanczik and Wagner, 2005; Clement et al., 2021). Like EFPs, ECMs can be

Methods such as ecmtool will be investigated to get elementary conversion modes.

Already implemented algorithms, regarding both the creation of the community model and the enumeration of the conversion modes will be considered for that, e.g. (/exchange-enumeration/multicellular_analysis_ove.ipynb)(https://gitlab.com/YlvaKaW/exchange-enumeration/-/blob/main/multicellular_analysis_ove.ipynb)

Difficulty: Hard

Size

Large (350 hours)

Skills

  • Required: python, basic knowledge in mathematics (especially linear algebra and/or geometry)
  • Preferred: Experience with mathematical software, C++ and/or biology is a plus

Expected impact

The scientific impact of this project could be fundamental. Only recently was explained how flux sampling when focusing on exchange reactions, could be implemented in community models. This will be the first implementation of this feature in Python and in large scale.

Mentors

  • Haris Zafeiropoulos <haris.zafeiropoulos at kuleuven.be> is working on metabolic modeling software development and applications as a post-doc in the Lab of Systems Biology at KU Leuven and has previous GSoC student experience (2021) and mentoring experience with GeomScale (2022) and NRNB (2023).

  • Apostolos Chalkis <tolis.chal at gmail.com> is a Research Engineer at Quantagonia GmbH. He is an expert in statistical software, computational geometry, and optimization, and has previous GSoC student experience (2018 & 2019) and mentoring experience with GeomScale (from 2020 to 2023).

Tests

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

  • Easy: compile and run dingo. Use the documentation to sample from the flux space of the e_coli model.

  • Medium: Sample the flux space of each species of a pair of models known from the literature. Describe potential cross-feeding reactions based on these individual samples.

  • Hard: Enumerate Elementary Conversion Modes on e_colie_Core.