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We derive a universal lower bound on the Fano factors of general biochemicaldiscriminatory networks involving irreversible catalysis steps, based on thethermodynamic uncertainty relation, and compare it to a numerically exactPareto optimal front. This bound is completely general, involving only thereversible entropy production per product formed and the error fraction of thesystem. We then show that by judiciously choosing which transitions to includein the reversible entropy production, one can derive a family of bounds thatcan be fine-tuned to include physical observables at hand. Lastly, we test ourbound by considering three discriminatory schemes: a multi-stageMichaelis-Menten network, a Michaelis-Menten network with correlations betweensubsequent products, and a multi-stage kinetic proofreading network, where forthe latter application the bound is altered to include the hydrolytic cost ofthe proofreading steps. We find that our bound is remarkably tight.

Universal thermodynamic bounds on the Fano factor of discriminatory networks with unidirectional transitions