Evaluating transcriptomic integration for cyanobacterial constraint-based metabolic modelling

Metabolic modelling has wide-ranging applications, including for the
production of high-value compounds, understanding complex disease
and analysing community interactions. Integrating transcriptomic data with
genome-scale metabolic models is crucial for deepening our
understanding of complex biological systems, as it enables the
development of models tailored to specific conditions, such as
particular tissues, environments, or experimental
setups. Relatively little attention has been given to the
assessment of such integration methods in predicting
intracellular fluxes. While a few validation studies offer some
insights, their scope remains limited, particularly for
organisms like cyanobacteria, for which little metabolic flux
data are available. Cyanobacteria hold significant
biotechnological potential due to their ability to synthesize a
wide range of high-value compounds with minimal resource inputs. 
The impact of specific methodological decisions on integration, however,
has scarcely been assessed beyond human models, with no thorough
exploration of parameter choices in valve-based integration methods. By
implementing a novel analysis pipeline, we evaluated these
methodological decisions using the genome-scale model
for Synechocystis sp. PCC 6803 (iSynCJ816 [Joshi et al., 2017
doi.org/10.1016/j.algal.2017.09.013]) with existing transcriptomic data in
biomass-optimised scenarios. Our analyses indicate that selecting
an appropriate integration method may not always be straightforward
and depends on the initial model configuration - a factor which
is often overlooked during integration. By evaluating sets of
methods, we identified a trade-off between the buffering of light into the
system and maintenance of flux near system boundaries. Our
findings also highlighted how selection of an appropriate integration
method likely depends the choice of configuration, emphasising
the need to consider both together.