Use Case

Mapping Transcriptome Data to Metabolic Models of Gut Microbiota

A. Douglas (Cornell) with J. Chaston (Brigham Young University), A. Moya (University of Valencia), G. Thomas (University of York), A. Heddi (INSA Lyon), and B. Barker (Cornell CAC)


  • Use host and gut-microbial express data to infer which metabolic reactions occur in each organism as a result of dietary inputs

Why Aristotle?

  • Heterogeneous instance types and sizes with high availability to allow for unpredictable computational demands
  • Allows generation of reproducible computational biology pipelines in the form of VMs or VM configurations


  • Created a Windows instance for MATLAB and a Samba server for file sharing across Windows and Linux.
  • Developed a transcriptomic pipeline for Docker.
  • Documented scalable file access for cross-cloud and many-instance file/app access.
  • Investigated Cornell Supercloud as possible means to scale out to other clouds.
  • Developed and published a computational model for the whiteflies system.
  • Analyzed and compared three independently-evolved communities in xylem-feeding insects. Generated advanced draft metabolic reconstructions for 5 bacteria needed to simulate Drosophila-microbial community metabolic interactions. The draft models of the 2 Lactobacilli have an average of 586 genes, 1193 metabolites and 750 reactions. The draft models of the 3 Acetobacter have an average of 686 genes, 1362 metabolites and 1129 reactions.
  • Initiated a computational strategy on Aristotle, based on a new optimization framework SteadyCom, to obtain the metabolic fluxes for optimized relative abundances of the partners under equilibrium conditions of constant growth rates.
  • Integrated the SteadyCom computational framework to model multi-species metabolic interaction in the insect gut.
  • Performed SteadyCom on test models in Aristotle using a newly confirmed Windows VM with MATLAB and the COBRA Toolbox.
  • Built a Linux VM with Docker, MATLAB, and Gurobi to containerize the simulation environment.
  • Perform a Flux Variability Analysis on the four microbes composing the Drosophila gut microbiome.
  • Constructed multi-species networks in silico of 2, 3, 4 and 5 bacterial species, quantified predicted metabolite fluxes, and wrote a MATLAB function to restore constraints to a multi-species model created from a single species.
  • Verified multi-species models behave as expected with standard flux algorithms.


  • Construct and analyze multi-compartment metabolic models of host-microbiome interactions in Drosophila.
  • Construct models of varying complexity, comprising up to 5 microbial taxa, for which the individual metabolic models were constructed and curated in the last year, and the host model that is under development.
  • Perform SteadyCom flux analysis using verified SteadyCom and multi-species construction procedure on multi-compartment models in a nutrient-rich and minimal media, to identify community metabolism, i.e., metabolic products of multi-species metabolic networks that are not produced by a single species in isolation, and compare community composition in silico with empirical data on community composition.
  • Integrate these empirical data into SteadyCom as additional constraints and analyze flux patterns, with emphasis on community metabolism.
  • Design fluxomics experiments informed by flux analysis of the models.


Angela Douglas explains how exploiting animal-microbe symbiosis may lead to pest-resistant crops

Next   Learn more