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See detailDynamic Network Reconstruction in Systems Biology: Methods and Algorithms
Yue, Zuogong UL

Doctoral thesis (2018)

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 ... [more ▼]

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. [less ▲]

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See detailOn definition and inference of nonlinear Boolean dynamic networks
Yue, Zuogong UL; Thunberg, Johan UL; Ljung, Lennart et al

in On definition and inference of nonlinear Boolean dynamic networks (2017, December)

Network reconstruction has become particularly important in systems biology, and is now expected to deliver information on causality. Systems in nature are inherently nonlinear. However, for nonlinear ... [more ▼]

Network reconstruction has become particularly important in systems biology, and is now expected to deliver information on causality. Systems in nature are inherently nonlinear. However, for nonlinear dynamical systems with hidden states, how to give a useful definition of dynamic networks is still an open question. This paper presents a useful definition of Boolean dynamic networks for a large class of nonlinear systems. Moreover, a robust inference method is provided. The well-known Millar-10 model in systems biology is used as a numerical example, which provides the ground truth of causal networks for key mRNAs involved in eukaryotic circadian clocks. In addition, as second contribution of this paper, we suggest definitions of linear network identifiability, which helps to unify the available work on network identifiability. [less ▲]

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See detailLinear Dynamic Network Reconstruction from Heterogeneous Datasets
Yue, Zuogong UL; Thunberg, Johan UL; Pan, Wei et al

in Linear Dynamic Network Reconstruction from Heterogeneous Datasets (2017, July)

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See detailInverse Problems for Matrix Exponential in System Identification: System Aliasing
Yue, Zuogong UL; Thunberg, Johan UL; Goncalves, Jorge UL

in 22nd International Symposium on Mathematical Theory of Networks and Systems (2016)

This note addresses identification of the A-matrix in continuous time linear dynamical systems on state-space form. If this matrix is partially known or known to have a sparse structure, such knowledge ... [more ▼]

This note addresses identification of the A-matrix in continuous time linear dynamical systems on state-space form. If this matrix is partially known or known to have a sparse structure, such knowledge can be used to simplify the identification. We begin by introducing some general conditions for solvability of the inverse problems for matrix exponential. Next, we introduce “system aliasing” as an issue in the identification of slow sampled systems. Such aliasing give rise to nonunique matrix logarithms. As we show, by imposing additional conditions on and prior knowledge about the A-matrix, the issue of system aliasing can, at least partially, be overcome. Under conditions on the sparsity and the norm of the A-matrix, it is identifiable up to a finite equivalence class. [less ▲]

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See detailAn Iterative Projection method for Synchronization of Invertible Matrices Over Graphs
Thunberg, Johan UL; Colombo, Nicolo UL; Yue, Zuogong UL et al

in 22nd International Symposium on Mathematical Theory of Networks and Systems (2016)

This paper addresses synchronization of invertible matrices over graphs. The matrices represent pairwise transformations between n euclidean coordinate systems. Synchronization means that composite ... [more ▼]

This paper addresses synchronization of invertible matrices over graphs. The matrices represent pairwise transformations between n euclidean coordinate systems. Synchronization means that composite transformations over loops are equal to the identity. Given a set of measured matrices that are not synchronized, the synchronization problem amounts to fining new synchronized matrices close to the former. Under the assumption that the measurement noise is zero mean Gaussian with known covariance, we introduce an iterative method based on linear subspace projection. The method is free of step size determination and tuning and numerical simulations show significant improvement of the solution compared to a recently proposed direct method as well as the Gauss-Newton method. [less ▲]

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