Abstract
The engineering of molecular programs capable of processing patterns of multi-input biomarkers holds great potential in applications ranging from in vitro diagnostics (e.g., viral detection, including COVID-19) to therapeutic interventions (e.g., discriminating cancer cells from normal cells). For this reason, mechanisms to design molecular networks for pattern recognition are highly sought after. In this work, we explore how enzymatic networks can be used for both linear and nonlinear classification tasks. By leveraging steady-state analysis and showing global stability, we demonstrate that these networks can function as molecular perceptrons, fundamental units of artificial neural networks—capable of processing multiple inputs associated with positive and negative weights to achieve linear classification. Furthermore, by composing orthogonal enzymatic reactions, we show that multi-layer networks can be constructed to achieve nonlinear classification.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The work of G. Giordano was funded by the European Union through the ERC INSPIRE grant (project number 101076926); views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Council; neither the European Union nor the granting authority can be held responsible for them.