Speaker
Description
DNA strand displacement systems have enabled the construction of molecular circuits capable of performing complex computations, typically relying on irreversible reactions and non-equilibrium dynamics for signal amplification and restoration. An alternative paradigm is equilibrium computation, where outputs are determined by the steady-state concentrations of molecular species.
In this talk, we investigate the computational limits of equilibrium seesaw networks. We prove that, under constraints on species concentrations, the influence of an input signal decays exponentially with its distance from the input. This result implies that signal amplification is impossible and that large-scale equilibrium circuits inherently suffer from vanishing signal propagation. Extending our analysis, we connect seesaw networks to broader classes of equilibrium systems, including ligand–receptor and dimerization networks, and discuss how similar limitations arise in these models.
Overall, our results identify a fundamental barrier to scalable equilibrium computation in molecular systems, highlighting the necessity of non-equilibrium mechanisms or carefully engineered regimes for robust information processing at scale.
