A metabolic network represents in an organism the complete set of biochemical reactions suitable to synthesise or break-down metabolites. These reactions drive the production of biomass and energy to support all cellular processes. The reconstruction of a full metabolic network occurring within each cell has advanced from early biochemical studies to algorithmically-generated pathway diagrams starting from genomic sequencing . Genome-scale models (GEMs) offer a comprehensive exploration and a rapid analysis of genomic data. GEMs have been used extensively to study metabolic engineering [2, 3], model-driven drug discoveries [4, 5], prediction of cellular phenotypes after perturbations [6, 7], analysis of evolutionary processes [8–11] and models of interspecies interactions . Organism-specific reconstructed metabolic networks may be further implemented to build mathematical models capable of simulating metabolic fluxes . GEM pathway reconstruction has been used in Caenorhabditis elegans to predict genes essentiality  and to better understand the biology of arthropods , including those with a negative impact (vectors of human or animal diseases, agricultural pests). The latter approach is particularly useful to control harmful species and to develop new precautionary strategies . Genome-scale metabolic modelling has also been successfully applied to study metabolic networks in microbes , including a Polychlorinated Biphenyl-degrading Pseudomonas [17, 18], thermophilic bacteria  and members of the human gut microbiota .
Among eukaryotic microbes, at least 25 models of Saccharomyces cerevisiae have been published since 2003 , helping to understand yeast metabolism. Ciliated protozoans may represent an alternative and useful eukaryotic model. Ciliates have been the main subject of projects supported by the EU Framework Programme Horizon 2020 such as the COST Actions. Moreover, the National Centre for Genome Resources and the Gordon and Betty Moore Foundation’s supported the Marine Microbiology Initiative (MMI). MMI created a valuable benchmark against the analysis of environmental transcriptomic data . Ciliates, as yeasts, are eukaryotic single cells, but their structural and functional complexity is comparable to human and other metazoan cells. These unicellular organisms are similar to differentiated animal cells with complex functions and membrane-bound structures . The potential number of extant ciliate species has been estimated at nearly 30,000 . They represent an important mediator in the food chain by transforming ultrafine organic matter useful for zooplankton. Being individual cells, they are directly exposed to environmental changes, making them good models for studying cell-stress response and adaptation. Ciliates propagate mainly asexually by transverse fission, even though they perform conjugation, a sexual process that “renews” the genetic material. The complexity of ciliates is further represented by the presence of two different nuclei: the diploid micronucleus involved in conjugation and the polyploid macronucleus. While the first represents the germinal line, the second represents the somatic line. The macronucleus is responsible for gene expression during the vegetative phase.
We describe below a preliminary open software tool (CiliateGEM), focused on the T. thermophila macronuclear genomic sequences, which allows ciliates researchers to analyse a reconstructed network via Flux Balance Analysis (FBA). By studying this ciliate, we could discover new mechanisms for evolution and adaptation within metabolism, population, species and host-symbiont association. Cellular responses described in T. thermophila can be of fundamental importance to understand the biology of all ciliates. CiliateGEM is to date the most advanced tool available for ciliates and contains the highest number of curated biochemical reactions.