Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2

Eva Balsa-Canto; Lucia Bandiera; Filippo Menolascina

doi:10.1007/978-1-0716-1032-9_11

Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2

Eva Balsa-Canto, Lucia Bandiera, Filippo Menolascina

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

Abstract / Description of output

Dynamic modeling in systems and synthetic biology is still quite a challenge-the complex nature of the interactions results in nonlinear models, which include unknown parameters (or functions). Ideally, time-series data support the estimation of model unknowns through data fitting. Goodness-of-fit measures would lead to the best model among a set of candidates. However, even when state-of-the-art measuring techniques allow for an unprecedented amount of data, not all data suit dynamic modeling.Model-based optimal experimental design (OED) is intended to improve model predictive capabilities. OED can be used to define the set of experiments that would (a) identify the best model or (b) improve the identifiability of unknown parameters. In this chapter, we present a detailed practical procedure to compute optimal experiments using the AMIGO2 toolbox.

Original language	English
Title of host publication	Synthetic Gene Circuits
Subtitle of host publication	Methods and Protocols
Editors	Filippo Menolascina
Publisher	Springer
Pages	221-239
Number of pages	19
Volume	2229
ISBN (Electronic)	978-1-0716-1032-9
ISBN (Print)	978-1-0716-1031-2, 978-1-0716-1034-3
DOIs	https://doi.org/10.1007/978-1-0716-1032-9_11
Publication status	Published - 7 Jan 2021

Publication series

Name	Methods in molecular biology (Clifton, N.J.)
Publisher	Humana Press
Volume	2229
ISSN (Print)	1064-3745

Keywords / Materials (for Non-textual outputs)

Biological systems
dynamic models
Optimal experimental design
practical identifiability

Access to Document

10.1007/978-1-0716-1032-9_11

McSynC: in vivo automatic Model calibration of Synthetic Circuits components
Menolascina, F.
EPSRC
1/11/18 → 31/10/20
Project: Research
An intelligent approach to the automatic characterisation and design of synthetic promoters in mammalian cells
Menolascina, F.
EPSRC
29/06/18 → 28/03/22
Project: Research
CONDSYC- CONtextual Design of SYnthetic Circuits
Bandiera, L.
EPSRC
1/07/17 → 30/06/20
Project: Research

Cite this

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title = "Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2",

abstract = "Dynamic modeling in systems and synthetic biology is still quite a challenge-the complex nature of the interactions results in nonlinear models, which include unknown parameters (or functions). Ideally, time-series data support the estimation of model unknowns through data fitting. Goodness-of-fit measures would lead to the best model among a set of candidates. However, even when state-of-the-art measuring techniques allow for an unprecedented amount of data, not all data suit dynamic modeling.Model-based optimal experimental design (OED) is intended to improve model predictive capabilities. OED can be used to define the set of experiments that would (a) identify the best model or (b) improve the identifiability of unknown parameters. In this chapter, we present a detailed practical procedure to compute optimal experiments using the AMIGO2 toolbox.",

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author = "Eva Balsa-Canto and Lucia Bandiera and Filippo Menolascina",

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Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2. / Balsa-Canto, Eva; Bandiera, Lucia ; Menolascina, Filippo.
Synthetic Gene Circuits : Methods and Protocols. ed. / Filippo Menolascina. Vol. 2229 Springer, 2021. p. 221-239 (Methods in molecular biology (Clifton, N.J.); Vol. 2229).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

TY - CHAP

T1 - Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2

AU - Balsa-Canto, Eva

AU - Bandiera, Lucia

AU - Menolascina, Filippo

PY - 2021/1/7

Y1 - 2021/1/7

N2 - Dynamic modeling in systems and synthetic biology is still quite a challenge-the complex nature of the interactions results in nonlinear models, which include unknown parameters (or functions). Ideally, time-series data support the estimation of model unknowns through data fitting. Goodness-of-fit measures would lead to the best model among a set of candidates. However, even when state-of-the-art measuring techniques allow for an unprecedented amount of data, not all data suit dynamic modeling.Model-based optimal experimental design (OED) is intended to improve model predictive capabilities. OED can be used to define the set of experiments that would (a) identify the best model or (b) improve the identifiability of unknown parameters. In this chapter, we present a detailed practical procedure to compute optimal experiments using the AMIGO2 toolbox.

AB - Dynamic modeling in systems and synthetic biology is still quite a challenge-the complex nature of the interactions results in nonlinear models, which include unknown parameters (or functions). Ideally, time-series data support the estimation of model unknowns through data fitting. Goodness-of-fit measures would lead to the best model among a set of candidates. However, even when state-of-the-art measuring techniques allow for an unprecedented amount of data, not all data suit dynamic modeling.Model-based optimal experimental design (OED) is intended to improve model predictive capabilities. OED can be used to define the set of experiments that would (a) identify the best model or (b) improve the identifiability of unknown parameters. In this chapter, we present a detailed practical procedure to compute optimal experiments using the AMIGO2 toolbox.

KW - Biological systems

KW - dynamic models

KW - Optimal experimental design

KW - practical identifiability

U2 - 10.1007/978-1-0716-1032-9_11

DO - 10.1007/978-1-0716-1032-9_11

M3 - Chapter (peer-reviewed)

C2 - 33405225

SN - 978-1-0716-1031-2

SN - 978-1-0716-1034-3

VL - 2229

T3 - Methods in molecular biology (Clifton, N.J.)

SP - 221

EP - 239

BT - Synthetic Gene Circuits

A2 - Menolascina, Filippo

PB - Springer

ER -

Optimal Experimental Design for Systems and Synthetic Biology Using AMIGO2

Abstract / Description of output

Publication series

Keywords / Materials (for Non-textual outputs)

Access to Document

Fingerprint

Projects

McSynC: in vivo automatic Model calibration of Synthetic Circuits components

An intelligent approach to the automatic characterisation and design of synthetic promoters in mammalian cells

CONDSYC- CONtextual Design of SYnthetic Circuits

Cite this