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GENA stands for Genome-Based Network Analysis. It is the core brick designed to the building and simulation of digital twins of cell metabolism.
Digital twins of cell metabolism (a.k.a digital organisms) are mathematical models designed to mimic the functional metabolic capabilities of living organisms (bacteria, viruses, fungi, plants, animals, …). The basic theory behind digital organisms relies on metabolic flux analysis (MFA), a theory that emerged during the 80's and 90's (Papoutsakis, 1984; Fell and Small, 1986; Savinell and Palsson, 1994). MFA is an experimental and mathematical technique used to examine production and consumption rates of metabolites in a biological system.
Metabolic flux analysis (MFA) has many applications such as:
- determining limits on the ability of a biological system to produce a biochemical,
- predicting the response to gene knockout,
- guiding the identification of bottleneck enzymes in metabolic networks for metabolic engineering efforts,
- predicting the metabolic interactions between organisms (e.g. host pathogen interactions, microbial interactions).
Several mathematical paradigms were developed in the past to model metabolic fluxes in living organisms. They led to the emergence of a new omics discipline called fluxomics. Fluxomics is composed of two major branches:
- Flux balance analysis (FBA),
- 13-C fluxomics (and more generally labeled-fluxomics).
Flux balance analysis
A good overview of FBA is given by Orth, Thiele and Palsson in 2010. FBA assumes that internal metabolites are at the dynamic steady state, i.e. the sum of fluxes to and from a metabolite is kept almost zero during cell growth. This is the so-called quasi steady state assumption (QSSA). It is major biological assumption reflecting the homeostatic regulatory role of cell metabolism. FBA relies on a linear algebraic framework, requires few parametrization, and then allows handling large-size metabolic models.
13-C fluxomics (13C-MFA) is quite similar to FBA but substrate enriched with 13-C labelled carbons are used to feed living cells (generally using labelled glucose). The metabolic fate of labelled carbons are then tracked through the cells (Wiechert, 2001). 13C-MFA allows high precision reconstruction and diagnostic of the metabolic routes of substrates but the mathematical complexity of the approach limits its application to small-size models and targeted metabolic pathways.
Flux-balance analysis resources include biomodels, BIGG database, the COBRA toolbox, MetaNetX, etc.
Whole genome metabolic modelling
With the democratisation of high throughput omics data (genomics, transcriptomics, proteomics, metabolomics), the size of omics models exponentially increased, opening the way to genome-scale metabolic models (also called whole-genome metabolic models).
Genome-scale metabolic modelling consists in a series of methods that are used to capture the metabolic changes encoded by the whole genome of living organisms. Several tools are used going from in vivo, in vitro experiments, to in silico analyses. FBA is today the most popular in silico tool used to model flux distribution in living organisms at the scale of the whole genome.
GENA brick relis on the upcycling of open ontology and molecular data collected into the BIOTA brick.
We believe in open innovation and designed our platforms to accelerate the standardisation and integration of open digital resources in biology. GENA relies on the EMBL-EBI databases, NCBI taxonomy data. To learn more, please refer to the BIOTA brick