Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data
The rapid progress in biological experimental technologies has generated a huge amount of experimental data to investigate complex regulatory mechanisms. Various mathematical models have been proposed to simulate the dynamic properties of molecular processes using the experimental data. However, it...
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| Format: | Article |
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MDPI AG
2022-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/24/4748 |
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| author | Wenlong He Peng Xia Xinan Zhang Tianhai Tian |
| author_facet | Wenlong He Peng Xia Xinan Zhang Tianhai Tian |
| author_sort | Wenlong He |
| collection | DOAJ |
| description | The rapid progress in biological experimental technologies has generated a huge amount of experimental data to investigate complex regulatory mechanisms. Various mathematical models have been proposed to simulate the dynamic properties of molecular processes using the experimental data. However, it is still difficult to estimate unknown parameters in mathematical models for the dynamics in different cells due to the high demand for computing power. In this work, we propose a population statistical inference algorithm to improve the computing efficiency. In the first step, this algorithm clusters single cells into a number of groups based on the distances between each pair of cells. In each cluster, we then infer the parameters of the mathematical model for the first cell. We propose an adaptive approach that uses the inferred parameter values of the first cell to formulate the prior distribution and acceptance criteria of the following cells. Three regulatory network models were used to examine the efficiency and effectiveness of the designed algorithm. The computational results show that the new method reduces the computational time significantly and provides an effective algorithm to infer the parameters of regulatory networks in a large number of cells. |
| first_indexed | 2024-03-09T16:08:34Z |
| format | Article |
| id | doaj.art-e7f77bc350294a8bbd64525600aa019b |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T16:08:34Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-e7f77bc350294a8bbd64525600aa019b2023-11-24T16:29:02ZengMDPI AGMathematics2227-73902022-12-011024474810.3390/math10244748Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell DataWenlong He0Peng Xia1Xinan Zhang2Tianhai Tian3School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, ChinaSchool of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, ChinaSchool of Mathematics and Statistics, Central China Normal University, Wuhan 430079, ChinaSchool of Mathematics, Monash University, Clayton, VIC 3800, AustraliaThe rapid progress in biological experimental technologies has generated a huge amount of experimental data to investigate complex regulatory mechanisms. Various mathematical models have been proposed to simulate the dynamic properties of molecular processes using the experimental data. However, it is still difficult to estimate unknown parameters in mathematical models for the dynamics in different cells due to the high demand for computing power. In this work, we propose a population statistical inference algorithm to improve the computing efficiency. In the first step, this algorithm clusters single cells into a number of groups based on the distances between each pair of cells. In each cluster, we then infer the parameters of the mathematical model for the first cell. We propose an adaptive approach that uses the inferred parameter values of the first cell to formulate the prior distribution and acceptance criteria of the following cells. Three regulatory network models were used to examine the efficiency and effectiveness of the designed algorithm. The computational results show that the new method reduces the computational time significantly and provides an effective algorithm to infer the parameters of regulatory networks in a large number of cells.https://www.mdpi.com/2227-7390/10/24/4748population modelparameter inferenceheterogeneityregulatory network |
| spellingShingle | Wenlong He Peng Xia Xinan Zhang Tianhai Tian Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data Mathematics population model parameter inference heterogeneity regulatory network |
| title | Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data |
| title_full | Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data |
| title_fullStr | Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data |
| title_full_unstemmed | Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data |
| title_short | Bayesian Inference Algorithm for Estimating Heterogeneity of Regulatory Mechanisms Based on Single-Cell Data |
| title_sort | bayesian inference algorithm for estimating heterogeneity of regulatory mechanisms based on single cell data |
| topic | population model parameter inference heterogeneity regulatory network |
| url | https://www.mdpi.com/2227-7390/10/24/4748 |
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