MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy
Abstract Complex biological systems often undergo sudden qualitative changes during their dynamic evolution. These critical transitions are typically characterized by a catastrophic progression of the system. Identifying the critical point is critical to uncovering the underlying mechanisms of compl...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-01-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-024-05667-z |
| _version_ | 1797276283633139712 |
|---|---|
| author | Yuke Xie Xueqing Peng Peiluan Li |
| author_facet | Yuke Xie Xueqing Peng Peiluan Li |
| author_sort | Yuke Xie |
| collection | DOAJ |
| description | Abstract Complex biological systems often undergo sudden qualitative changes during their dynamic evolution. These critical transitions are typically characterized by a catastrophic progression of the system. Identifying the critical point is critical to uncovering the underlying mechanisms of complex biological systems. However, the system may exhibit minimal changes in its state until the critical point is reached, and in the face of high throughput and strong noise data, traditional biomarkers may not be effective in distinguishing the critical state. In this study, we propose a novel approach, mutual information weighted entropy (MIWE), which uses mutual information between genes to build networks and identifies critical states by quantifying molecular dynamic differences at each stage through weighted differential entropy. The method is applied to one numerical simulation dataset and four real datasets, including bulk and single-cell expression datasets. The critical states of the system can be recognized and the robustness of MIWE method is verified by numerical simulation under the influence of different noises. Moreover, we identify two key transcription factors (TFs), CREB1 and CREB3, that regulate downstream signaling genes to coordinate cell fate commitment. The dark genes in the single-cell expression datasets are mined to reveal the potential pathway regulation mechanism. |
| first_indexed | 2024-03-07T15:26:02Z |
| format | Article |
| id | doaj.art-aa33bf3a954949749568b8e3c44dd1f0 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-07T15:26:02Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-aa33bf3a954949749568b8e3c44dd1f02024-03-05T17:07:21ZengBMCBMC Bioinformatics1471-21052024-01-0125111710.1186/s12859-024-05667-zMIWE: detecting the critical states of complex biological systems by the mutual information weighted entropyYuke Xie0Xueqing Peng1Peiluan Li2School of Mathematics and Statistics, Henan University of Science and TechnologySchool of Mathematics and Statistics, Henan University of Science and TechnologySchool of Mathematics and Statistics, Henan University of Science and TechnologyAbstract Complex biological systems often undergo sudden qualitative changes during their dynamic evolution. These critical transitions are typically characterized by a catastrophic progression of the system. Identifying the critical point is critical to uncovering the underlying mechanisms of complex biological systems. However, the system may exhibit minimal changes in its state until the critical point is reached, and in the face of high throughput and strong noise data, traditional biomarkers may not be effective in distinguishing the critical state. In this study, we propose a novel approach, mutual information weighted entropy (MIWE), which uses mutual information between genes to build networks and identifies critical states by quantifying molecular dynamic differences at each stage through weighted differential entropy. The method is applied to one numerical simulation dataset and four real datasets, including bulk and single-cell expression datasets. The critical states of the system can be recognized and the robustness of MIWE method is verified by numerical simulation under the influence of different noises. Moreover, we identify two key transcription factors (TFs), CREB1 and CREB3, that regulate downstream signaling genes to coordinate cell fate commitment. The dark genes in the single-cell expression datasets are mined to reveal the potential pathway regulation mechanism.https://doi.org/10.1186/s12859-024-05667-zDynamic network biomarker (DNB)Mutual informationCritical stateDifferential entropy |
| spellingShingle | Yuke Xie Xueqing Peng Peiluan Li MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy BMC Bioinformatics Dynamic network biomarker (DNB) Mutual information Critical state Differential entropy |
| title | MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy |
| title_full | MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy |
| title_fullStr | MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy |
| title_full_unstemmed | MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy |
| title_short | MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy |
| title_sort | miwe detecting the critical states of complex biological systems by the mutual information weighted entropy |
| topic | Dynamic network biomarker (DNB) Mutual information Critical state Differential entropy |
| url | https://doi.org/10.1186/s12859-024-05667-z |
| work_keys_str_mv | AT yukexie miwedetectingthecriticalstatesofcomplexbiologicalsystemsbythemutualinformationweightedentropy AT xueqingpeng miwedetectingthecriticalstatesofcomplexbiologicalsystemsbythemutualinformationweightedentropy AT peiluanli miwedetectingthecriticalstatesofcomplexbiologicalsystemsbythemutualinformationweightedentropy |