@article{775add7f54f0432497fc06b23b05749e,
title = "Mechanism-based and data-driven modeling in cell-free synthetic biology",
abstract = "Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.",
keywords = "Synthetic Biology/methods, Cell-Free System, Gene Regulatory Networks, Models, Biological",
author = "Angelina Yurchenko and G{\"o}k{\c c}e {\"O}zkul and {van Riel}, {Natal A.W.} and {van Hest}, {Jan C.M.} and {de Greef}, {Tom F.A.}",
year = "2024",
month = jun,
day = "28",
doi = "10.1039/d4cc01289e",
language = "English",
volume = "60",
pages = "6466--6475",
journal = "Chemical Communications, ChemComm",
issn = "1359-7345",
publisher = "Royal Society of Chemistry",
number = "51",
}