TY - GEN
T1 - Wasserstein distances for estimating parameters in stochastic reaction networks
AU - Öcal, Kaan
AU - Grima, Ramon
AU - Sanguinetti, Guido
PY - 2019/11/15
Y1 - 2019/11/15
N2 - Modern experimental methods such as flow cytometry and fluorescence in-situ hybridization (FISH) allow the measurement of cell-by-cell molecule numbers for RNA, proteins and other substances for large numbers of cells at a time, opening up new possibilities for the quantitative analysis of biological systems. Of particular interest is the study of biological reaction systems describing processes such as gene expression, cellular signalling and metabolism on a molecular level. It is well established that many of these processes are inherently stochastic [1–3] and that deterministic approaches to their study can fail to capture properties essential for our understanding of these systems [4, 5]. Despite recent technological and conceptual advances, modelling and inference for stochastic models of reaction networks remains challenging due to additional complexities not present in the deterministic case. The Chemical Master Equation (CME) [6] in particular, while frequently used to model many types of reaction networks, is difficult to solve exactly, and parameter inference in practice often relies on a variety of approximation schemes whose accuracy can vary widely and unpredictably depending on the context [6–8].
AB - Modern experimental methods such as flow cytometry and fluorescence in-situ hybridization (FISH) allow the measurement of cell-by-cell molecule numbers for RNA, proteins and other substances for large numbers of cells at a time, opening up new possibilities for the quantitative analysis of biological systems. Of particular interest is the study of biological reaction systems describing processes such as gene expression, cellular signalling and metabolism on a molecular level. It is well established that many of these processes are inherently stochastic [1–3] and that deterministic approaches to their study can fail to capture properties essential for our understanding of these systems [4, 5]. Despite recent technological and conceptual advances, modelling and inference for stochastic models of reaction networks remains challenging due to additional complexities not present in the deterministic case. The Chemical Master Equation (CME) [6] in particular, while frequently used to model many types of reaction networks, is difficult to solve exactly, and parameter inference in practice often relies on a variety of approximation schemes whose accuracy can vary widely and unpredictably depending on the context [6–8].
KW - bayesian optimization
KW - chemical master equation
KW - parameter estimation
KW - wasserstein distance
U2 - 10.1007/978-3-030-31304-3_24
DO - 10.1007/978-3-030-31304-3_24
M3 - Conference contribution
AN - SCOPUS:85075228659
SN - 9783030313036
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 347
EP - 351
BT - Computational Methods in Systems Biology - 17th International Conference, CMSB 2019, Proceedings
A2 - Bortolussi, Luca
A2 - Sanguinetti, Guido
PB - Springer
T2 - 17th International Conference on Computational Methods in Systems Biology, CMSB 2019
Y2 - 18 September 2019 through 20 September 2019
ER -