TY - GEN
T1 - Information content analysis reveals desirable aspects of in vivo experiments of a synthetic circuit
AU - Gomez Cabeza, D.
AU - Bandiera, L.
AU - Balsa-Canto, E.
AU - Menolascina, F.
N1 - Acceptance date set to exclude from REF OA Policy
PY - 2019/8/8
Y1 - 2019/8/8
N2 - Synthetic biology is predicated upon the paradigm of rationally engineering biological systems to enrich them with new functions. Departing from the path outlined by other engineering disciplines, synthetic biology has made limited use of mathematical models so far. Indeed, their laborious inference leverages on noisy data that often provide partial insights into the system behaviour. If the quality of data generated via a perturbation is a road-block to inference, how should we analyse the informativeness of a stimulus? Which are the factors that contribute to it? Here we combine ideas from System Identification and Bayesian inference to quantify the information content of in vivo experiments for the calibration of a deterministic model of the genetic toggle switch. Beyond establishing a link between Bayes-and Fisher Information-based inference approaches, we find a ∼ 40% gain can be ascribed to the dynamical properties of the perturbation scheme. Our results hint at the importance of including Bayesian experimental design in the characterisation of synthetic circuits.
AB - Synthetic biology is predicated upon the paradigm of rationally engineering biological systems to enrich them with new functions. Departing from the path outlined by other engineering disciplines, synthetic biology has made limited use of mathematical models so far. Indeed, their laborious inference leverages on noisy data that often provide partial insights into the system behaviour. If the quality of data generated via a perturbation is a road-block to inference, how should we analyse the informativeness of a stimulus? Which are the factors that contribute to it? Here we combine ideas from System Identification and Bayesian inference to quantify the information content of in vivo experiments for the calibration of a deterministic model of the genetic toggle switch. Beyond establishing a link between Bayes-and Fisher Information-based inference approaches, we find a ∼ 40% gain can be ascribed to the dynamical properties of the perturbation scheme. Our results hint at the importance of including Bayesian experimental design in the characterisation of synthetic circuits.
UR - http://www.scopus.com/inward/record.url?scp=85071440947&partnerID=8YFLogxK
U2 - 10.1109/CIBCB.2019.8791449
DO - 10.1109/CIBCB.2019.8791449
M3 - Conference contribution
AN - SCOPUS:85071440947
T3 - 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019
BT - 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019
A2 - Baruzzo, Giacomo
A2 - Daberdaku, Sebastian
A2 - Di Camillo, Barbara
A2 - Furini, Simone
A2 - Giordano, Emanuele Domenico
A2 - Nicosia, Giuseppe
PB - Institute of Electrical and Electronics Engineers
T2 - 16th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2019
Y2 - 9 July 2019 through 11 July 2019
ER -