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\author[1]{Maurício Alexander de Moura Ferreira}%
\author[1]{Wendel Batista da Silveira}%
\author[2,3]{Zoran Nikoloski}%
\affil[1]{Department of Microbiology, Federal University of Viçosa}%
\affil[2]{Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany}%
\affil[3]{Systems Biology and Mathematical Modelling, Max Planck Institute of Molecular Plant Physiology}%


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  \date{June 12, 2024}


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\begin{abstract}
Protein allocation determines the activity of cellular pathways and
affects growth across all organisms. Therefore, different experimental
and machine learning approaches have been developed to quantify and
predict protein abundances, respectively. Yet, despite advances in
protein quantification, it remains challenging to predict
condition-specific allocation of enzymes in metabolic networks. Here we
propose a family of constrained-based approaches, termed PARROT, to
predict enzyme allocations based on the principle of minimizing the
enzyme allocation adjustment using protein-constrained metabolic models.
To this end, PARROT variants model the minimization of enzyme
reallocation using four different (combinations of) distance functions.
We demonstrate that the PARROT variant that minimizes the Manhattan
distance of enzyme allocations outperforms existing approaches based on
the parsimonious distribution of fluxes or enzymes for
both~\emph{Escherichia coli} and~\emph{Saccharomyces cerevisiae}.
Further, we show that the combined minimization of flux and enzyme
allocation adjustment leads to inconsistent predictions. Together, our
findings indicate that minimization of resource, rather than flux,
redistribution is a governing principle determining steady-state pathway
activity for microorganism grown in suboptimal conditions.

\par\null

\textbf{Availability and implementation}: The implementation of PARROT
can be found in the GitHub repository:
\url{https://github.com/mauricioamf/PARROT}%
\end{abstract}%



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