| 1. |
- Magni, Stefano, et al.
(författare)
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Inferring upstream regulatory genes of FOXP3 in human regulatory T cells from time-series transcriptomic data
- 2024
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Ingår i: npj Systems Biology and Applications. - : Springer Nature. - 2056-7189. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- The discovery of upstream regulatory genes of a gene of interest still remains challenging. Here we applied a scalable computational method to unbiasedly predict candidate regulatory genes of critical transcription factors by searching the whole genome. We illustrated our approach with a case study on the master regulator FOXP3 of human primary regulatory T cells (Tregs). While target genes of FOXP3 have been identified, its upstream regulatory machinery still remains elusive. Our methodology selected five top-ranked candidates that were tested via proof-of-concept experiments. Following knockdown, three out of five candidates showed significant effects on the mRNA expression of FOXP3 across multiple donors. This provides insights into the regulatory mechanisms modulating FOXP3 transcriptional expression in Tregs. Overall, at the genome level this represents a high level of accuracy in predicting upstream regulatory genes of key genes of interest.
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| 2. |
- Yue, Zuogong, et al.
(författare)
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Dynamic network reconstruction from heterogeneous datasets
- 2021
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Ingår i: Automatica. - Amsterdam : Elsevier. - 0005-1098 .- 1873-2836. ; 123
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Tidskriftsartikel (refereegranskat)abstract
- Performing multiple experiments is common when learning internal mechanisms of complex systems. These experiments can include perturbations of parameters or external disturbances. A challenging problem is to efficiently incorporate all collected data simultaneously to infer the underlying dynamic network. This paper addresses the reconstruction of dynamic networks from heterogeneous datasets under the assumption that the underlying networks share the same Boolean structure across all experiments. Parametric models are derived for dynamical structure functions, which describe causal interactions between measured variables. Multiple datasets are integrated into one regression problem with additional demands on group sparsity to assure network sparsity and structure consistency. To acquire structured group sparsity, we propose a sampling-based method, together with extended versions of l1-methods and sparse Bayesian learning. The performance of the proposed methods is benchmarked in numerical simulation. In summary, this paper presents efficient methods on network reconstruction from multiple experiments, and reveals practical experience that could guide applications. © 2020 Elsevier Ltd.
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| 3. |
- Yue, Zuogong, et al.
(författare)
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Linear Dynamic Network Reconstruction from Heterogeneous Datasets
- 2017
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Ingår i: IFAC PAPERSONLINE. - : ELSEVIER SCIENCE BV. ; , s. 10586-10591
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Konferensbidrag (refereegranskat)abstract
- This paper addresses reconstruction of linear dynamic networks from heterogeneous datasets. Those datasets consist of measurements from linear dynamical systems in multiple experiments subjected to different experimental conditions, e.g., changes/perturbations in parameters, disturbance or noise. A main assumption is that the Boolean structures of the underlying networks are the same in all experiments. The ARMAX model is adopted to parameterize the general linear dynamic network representation "Dynamical Structure Function" (DSF), which provides the Granger Causality graph as a special case. The network identification is performed by integrating all available datasets and promote group sparsity to assure both network sparsity and the consistency of Boolean structures over datasets. In terms of solving the problem, a treatment by the iterative reweighted l(1) method is used, together with its implementations via proximal methods and ADMM for large-dimensional networks. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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| 4. |
- Yue, Zuogong, et al.
(författare)
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Network Stability, Realisation and Random Model Generation
- 2019
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Ingår i: 2019 IEEE 58th Conference on Decision and Control (CDC). - New York, NY : IEEE. - 9781728113982 - 9781728113975 - 9781728113999 ; , s. 4539-4544
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Konferensbidrag (refereegranskat)abstract
- Dynamical structure functions (DSFs) provide means for modelling networked dynamical systems and exploring interactive structures thereof. There have been several studies on methods/algorithms for reconstructing (Boolean) networks from time-series data. However, there are no methods currently available for random generation of DSF models with complex network structures for benchmarking. In particular, it may be desirable to generate stable DSF models or require the presence of feedback structures while keeping topology and dynamics random up to these constraints. This work provides procedures to obtain such models. On the path of doing so, we first study essential properties and concepts of DSF models, including realisation and stability. Then, the paper suggests model generation algorithms, whose implementations are now publicly available. © 2019 IEEE.
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| 5. |
- Yue, Zuogong, et al.
(författare)
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On Definition and Inference of Nonlinear Boolean Dynamic Networks
- 2017
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Ingår i: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC). - : IEEE. - 9781509028733
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 6. |
- Yue, Zuogong, et al.
(författare)
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System Aliasing in Dynamic Network Reconstruction : Issues on Low Sampling Frequencies
- 2021
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Ingår i: IEEE Transactions on Automatic Control. - Piscataway : IEEE. - 0018-9286 .- 1558-2523. ; 66:12, s. 5788-5801
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Tidskriftsartikel (refereegranskat)abstract
- Network reconstruction of dynamical continuous-time (CT) systems is motivated by applications in many fields. Due to experimental limitations, especially in biology, data can be sampled at low frequencies, leading to significant challenges in network inference. We introduce the concept of "system aliasing" and characterize the minimal sampling frequency that allows reconstruction of CT systems from low sampled data. A test criterion is also proposed to detect the presence of system aliasing. With no system aliasing, the paper provides an algorithm to reconstruct dynamic networks from full-state measurements in the presence of noise. With system aliasing, we add additional prior information such as sparsity to overcome the lack of identifiability. This paper opens new directions in modelling of network systems where samples have significant costs. Such tools are essential to process available data in applications subject to experimental limitations. © 2020, IEEE
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