Edit Paper: Data-driven network models for genetic circuits from time-series data with incomplete measurements

You do not have permission to edit this page, for the following reason:

The action you have requested is limited to users in the group: Users.

Enter the information required for the paper. Required information is marked with a *.

Title*:	Data-driven network models for genetic circuits from time-series data with incomplete measurements
Authors*:	Enoch Yeung, Jim Kim, Ye Yuan, Jorge Gonçalves, Richard M. Murray
Source*:	Journal of the Royal Society Interface 18 (182), 20210413
Abstract*:	Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro, due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements. We introduce a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a historical genetic circuit and a new E. coli based transcriptional event detector.
URL:
Type:
ID:
Tag:
Funding:
DOI:
Flags:

Summary:

This is a minor edit

Watch this page

Cancel

Edit Paper: Data-driven network models for genetic circuits from time-series data with incomplete measurements

Navigation menu

Page actions

Page actions

Personal tools

Navigation

Search

Tools