Doctoral thesis (Dissertations and theses)
Dynamic Network Reconstruction in Systems Biology: Methods and Algorithms
Yue, Zuogong
2018
 

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Keywords :
Network reconstruction; System identification; Dynamical structure function; Systems biology
Abstract :
[en] Dynamic network reconstruction refers to a class of problems that explore causal interactions between variables operating in dynamical systems. This dissertation focuses on methods and algorithms that reconstruct/infer network topology or dynamics from observations of an unknown system. The essential challenges, compared to system identification, are imposing sparsity on network topology and ensuring network identifiability. This work studies the following cases: multiple experiments with heterogeneity, low sampling frequency and nonlinearity, which are generic in biology that make reconstruction problems particularly challenging. The heterogeneous data sets are measurements in multiple experiments from the underlying dynamical systems that are different in parameters, whereas the network topology is assumed to be consistent. It is particularly common in biological applications. This dissertation proposes a way to deal with multiple data sets together to increase computational robustness. Furthermore, it can also be used to enforce network identifiability by multiple experiments with input perturbations. The necessity to study low-sampling-frequency data is due to the mismatch of network topology of discrete-time and continuous-time models. It is generally assumed that the underlying physical systems are evolving over time continuously. An important concept system aliasing is introduced to manifest whether the continuous system can be uniquely determined from its associated discrete-time model with the specified sampling frequency. A Nyquist-Shannon-like sampling theorem is provided to determine the critical sampling frequency for system aliasing. The reconstruction method integrates the Expectation Maximization (EM) method with a modified Sparse Bayesian Learning (SBL) to deal with reconstruction from output measurements. A tentative study on nonlinear Boolean network reconstruction is provided. The nonlinear Boolean network is considered as a union of local networks of linearized dynamical systems. The reconstruction method extends the algorithm used for heterogeneous data sets to provide an approximated inference but improve computational robustness significantly. The reconstruction algorithms are implemented in MATLAB and wrapped as a package. With considerations on generic signal features in practice, this work contributes to practically useful network reconstruction methods in biological applications.
Research center :
- Luxembourg Centre for Systems Biomedicine (LCSB): Systems Control (Goncalves Group)
Disciplines :
Electrical & electronics engineering
Author, co-author :
Yue, Zuogong ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Life Science Research Unit
Language :
English
Title :
Dynamic Network Reconstruction in Systems Biology: Methods and Algorithms
Defense date :
21 February 2018
Number of pages :
181
Institution :
Unilu - University of Luxembourg, Luxembourg
Degree :
DOCTEUR DE L’UNIVERSITÉ DU LUXEMBOURG EN SCIENCES DE L’INGÉNIEUR
Focus Area :
Computational Sciences
FnR Project :
FNR9247977 - Causal Dynamical Network Reconstruction From Intrinsic Noise, 2014 (01/09/2014-14/03/2018) - Zuogong Yue
Name of the research project :
Causal Dynamical Network Reconstruction from Intrinsic Noise
Funders :
FNR - Fonds National de la Recherche [LU]
Available on ORBilu :
since 03 May 2018

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