Engineering Sequestration-Based Biomolecular Classifiers with Shared Resources.

ABSTRACT: Constructing molecular classifiers that enable cells to recognize linear and nonlinear input patterns would expand the biocomputational capabilities of engineered cells, thereby unlocking their potential in diagnostics and therapeutic applications. While several biomolecular classifier schemes have been designed, the effects of biological constraints such as resource limitation and competitive binding on the function of those classifiers have been left unexplored. Here, we first demonstrate the design of a sigma factor-based perceptron as a molecular classifier working based on the principles of molecular sequestration between the sigma factor and its antisigma molecule. We then investigate how the output of the biomolecular perceptron, i.e., its response pattern or decision boundary, is a

SUBMITTER: Moghimianavval H 

PROVIDER: S-EPMC11494701 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature