Bayesian topology inference of regulatory networks under partial observability
Biological systems, such as microbial communities in metagenomics and gene regulatory networks (GRNs) in genomics, are composed of a vast number of interacting components observed through inherently noisy data. These systems play a critical role in understanding fundamental biological processes, inc...
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Elsevier
2025-06-01
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| Series: | Results in Control and Optimization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666720725000566 |
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| author | Mohammad Alali Mahdi Imani |
| author_facet | Mohammad Alali Mahdi Imani |
| author_sort | Mohammad Alali |
| collection | DOAJ |
| description | Biological systems, such as microbial communities in metagenomics and gene regulatory networks (GRNs) in genomics, are composed of a vast number of interacting components observed through inherently noisy data. These systems play a critical role in understanding fundamental biological processes, including gene regulation, microbial interactions, and cellular dynamics. For example, microbial communities involve complex interactions between microbes, bacteria, genes, and small molecules observed through omics data, while GRNs consist of numerous interacting genes observed via various gene-expression technologies. However, reconstructing the topology of such networks poses significant challenges due to their large scale, high dimensionality, and the presence of noise. Existing inference techniques often struggle with scalability, interpretability, and overfitting, making them unsuitable for analyzing large and complex biological systems. To overcome these challenges, this paper proposes a Bayesian topology optimization framework for efficient and scalable inference of regulatory networks modeled as partially-observed Boolean dynamical systems (POBDS). The method combines the Boolean Kalman Filter (BKF) as an optimal estimator for POBDS, with Bayesian optimization, which employs Gaussian Process regression and a topology-inspired kernel function to model the log-likelihood function. Numerical experiments demonstrate the superior performance of our framework. In the p53-MDM2 network, our method accurately infers topology with 8 and 16 unknown regulations, achieving higher log-likelihood with 100 and 200 evaluations, respectively. For the mammalian cell cycle network with 10 unknown regulations, proposed method identifies the correct topology among 59,049 possibilities with lower error and faster convergence. |
| format | Article |
| id | doaj-art-2dd7181ce80940248c52c7e96151f1af |
| institution | OA Journals |
| issn | 2666-7207 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Control and Optimization |
| spelling | doaj-art-2dd7181ce80940248c52c7e96151f1af2025-08-20T02:35:47ZengElsevierResults in Control and Optimization2666-72072025-06-011910057010.1016/j.rico.2025.100570Bayesian topology inference of regulatory networks under partial observabilityMohammad Alali0Mahdi Imani1Corresponding author.; Northeastern University, 360 Huntington Ave, Boston, MA, 02115, United States of AmericaNortheastern University, 360 Huntington Ave, Boston, MA, 02115, United States of AmericaBiological systems, such as microbial communities in metagenomics and gene regulatory networks (GRNs) in genomics, are composed of a vast number of interacting components observed through inherently noisy data. These systems play a critical role in understanding fundamental biological processes, including gene regulation, microbial interactions, and cellular dynamics. For example, microbial communities involve complex interactions between microbes, bacteria, genes, and small molecules observed through omics data, while GRNs consist of numerous interacting genes observed via various gene-expression technologies. However, reconstructing the topology of such networks poses significant challenges due to their large scale, high dimensionality, and the presence of noise. Existing inference techniques often struggle with scalability, interpretability, and overfitting, making them unsuitable for analyzing large and complex biological systems. To overcome these challenges, this paper proposes a Bayesian topology optimization framework for efficient and scalable inference of regulatory networks modeled as partially-observed Boolean dynamical systems (POBDS). The method combines the Boolean Kalman Filter (BKF) as an optimal estimator for POBDS, with Bayesian optimization, which employs Gaussian Process regression and a topology-inspired kernel function to model the log-likelihood function. Numerical experiments demonstrate the superior performance of our framework. In the p53-MDM2 network, our method accurately infers topology with 8 and 16 unknown regulations, achieving higher log-likelihood with 100 and 200 evaluations, respectively. For the mammalian cell cycle network with 10 unknown regulations, proposed method identifies the correct topology among 59,049 possibilities with lower error and faster convergence.http://www.sciencedirect.com/science/article/pii/S2666720725000566InferenceBayesian optimizationPartially-observed Boolean dynamical systemsBiological networks |
| spellingShingle | Mohammad Alali Mahdi Imani Bayesian topology inference of regulatory networks under partial observability Results in Control and Optimization Inference Bayesian optimization Partially-observed Boolean dynamical systems Biological networks |
| title | Bayesian topology inference of regulatory networks under partial observability |
| title_full | Bayesian topology inference of regulatory networks under partial observability |
| title_fullStr | Bayesian topology inference of regulatory networks under partial observability |
| title_full_unstemmed | Bayesian topology inference of regulatory networks under partial observability |
| title_short | Bayesian topology inference of regulatory networks under partial observability |
| title_sort | bayesian topology inference of regulatory networks under partial observability |
| topic | Inference Bayesian optimization Partially-observed Boolean dynamical systems Biological networks |
| url | http://www.sciencedirect.com/science/article/pii/S2666720725000566 |
| work_keys_str_mv | AT mohammadalali bayesiantopologyinferenceofregulatorynetworksunderpartialobservability AT mahdiimani bayesiantopologyinferenceofregulatorynetworksunderpartialobservability |