publication . Conference object . Preprint . Research . 2018

Characterization of Biologically Relevant Network Structures form Time-series Data

Zoltan A. Tuza; Guy-Bart Stan;
Open Access English
  • Published: 24 Sep 2018
  • Publisher: IEEE
  • Country: United Kingdom
Abstract
Abstract High-throughput data acquisition in synthetic biology leads to an abundance of data that need to be processed and aggregated into useful biological models. Building dynamical models based on this wealth of data is of paramount importance to understand and optimize designs of synthetic biology constructs. However, building models manually for each data set is inconvenient and might become infeasible for highly complex synthetic systems. In this paper, we present state-of-the-art system identification techniques and combine them with chemical reaction network theory (CRNT) to generate dynamic models automatically. On the system identification side, Sparse...
Subjects
free text keywords: Computer Science - Systems and Control, cs.SY, Chemical reaction network theory, Ode, Data mining, computer.software_genre, computer, System identification, Network structure, Synthetic biology, Data acquisition, Time series, Computer science, Bayesian inference
Related Organizations
Funded by
UKRI| Engineering Fellowships for Growth: Systems and control engineering framework for robust and efficient synthetic biology
Project
  • Funder: UK Research and Innovation (UKRI)
  • Project Code: EP/M002187/1
  • Funding stream: EPSRC
,
EC| COSY-BIO
Project
COSY-BIO
Control Engineering of Biological Systems for Reliable Synthetic Biology Applications
  • Funder: European Commission (EC)
  • Project Code: 766840
  • Funding stream: H2020 | RIA
Validated by funder
Communities
FET H2020FET OPEN: FET-Open research and innovation actions
FET H2020FET OPEN: Control Engineering of Biological Systems for Reliable Synthetic Biology Applications
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