1
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Ren J, Li P, Yan J. CPMI: comprehensive neighborhood-based perturbed mutual information for identifying critical states of complex biological processes. BMC Bioinformatics 2024; 25:215. [PMID: 38879513 PMCID: PMC11180411 DOI: 10.1186/s12859-024-05836-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 06/10/2024] [Indexed: 06/19/2024] Open
Abstract
BACKGROUND There exists a critical transition or tipping point during the complex biological process. Such critical transition is usually accompanied by the catastrophic consequences. Therefore, hunting for the tipping point or critical state is of significant importance to prevent or delay the occurrence of catastrophic consequences. However, predicting critical state based on the high-dimensional small sample data is a difficult problem, especially for single-cell expression data. RESULTS In this study, we propose the comprehensive neighbourhood-based perturbed mutual information (CPMI) method to detect the critical states of complex biological processes. The CPMI method takes into account the relationship between genes and neighbours, so as to reduce the noise and enhance the robustness. This method is applied to a simulated dataset and six real datasets, including an influenza dataset, two single-cell expression datasets and three bulk datasets. The method can not only successfully detect the tipping points, but also identify their dynamic network biomarkers (DNBs). In addition, the discovery of transcription factors (TFs) which can regulate DNB genes and nondifferential 'dark genes' validates the effectiveness of our method. The numerical simulation verifies that the CPMI method is robust under different noise strengths and is superior to the existing methods on identifying the critical states. CONCLUSIONS In conclusion, we propose a robust computational method, i.e., CPMI, which is applicable in both the bulk and single cell datasets. The CPMI method holds great potential in providing the early warning signals for complex biological processes and enabling early disease diagnosis.
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Affiliation(s)
- Jing Ren
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000, China
- Longmen Laboratory, Luoyang, 471003, Henan, China
| | - Peiluan Li
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000, China.
- Longmen Laboratory, Luoyang, 471003, Henan, China.
| | - Jinling Yan
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
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2
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Zheng D, Grandgenett PM, Zhang Q, Baine M, Shi Y, Du Q, Liang X, Wong J, Iqbal S, Preuss K, Kamal A, Yu H, Du H, Hollingsworth MA, Zhang C. radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. Sci Rep 2024; 14:12316. [PMID: 38811597 PMCID: PMC11137018 DOI: 10.1038/s41598-024-62741-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 05/21/2024] [Indexed: 05/31/2024] Open
Abstract
Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, medical images, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection of patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomic feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomic features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomic features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomic feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the association analysis has revealed potential gene mutations and radiomic feature candidates that warrant further investigation in future research endeavors.
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Affiliation(s)
- Dandan Zheng
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA.
| | - Paul M Grandgenett
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
| | - Qi Zhang
- Department of Mathematics and Statistics, University of New Hampshire, Durham, NH, USA
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Yu Shi
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Qian Du
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL, USA
| | - Jeffrey Wong
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Subhan Iqbal
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA
| | - Kiersten Preuss
- Department of Nutrition and Health Sciences, University of Nebraska, Lincoln, NE, USA
| | - Ahsan Kamal
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Hongfeng Yu
- School of Computing, University of Nebraska, Lincoln, NE, USA
| | - Huijing Du
- Department of Mathematics, University of Nebraska, Lincoln, NE, USA
| | - Michael A Hollingsworth
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA.
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska, Lincoln, NE, USA.
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3
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Xie Y, Peng X, Li P. MIWE: detecting the critical states of complex biological systems by the mutual information weighted entropy. BMC Bioinformatics 2024; 25:44. [PMID: 38280998 PMCID: PMC10822190 DOI: 10.1186/s12859-024-05667-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 01/22/2024] [Indexed: 01/29/2024] Open
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.
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Affiliation(s)
- Yuke Xie
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
| | - Xueqing Peng
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000, China
| | - Peiluan Li
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000, China.
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4
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Zheng D, Grandgenett PM, Zhang Q, Baine M, Shi Y, Du Q, Liang X, Wong J, Iqbal S, Preuss K, Kamal A, Yu H, Du H, Hollingsworth MA, Zhang C. radioGWAS: link radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.02.23297995. [PMID: 37961101 PMCID: PMC10635263 DOI: 10.1101/2023.11.02.23297995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Addressing the significant level of variability exhibited by pancreatic cancer necessitates the adoption of a systems biology approach that integrates molecular data, biological properties of the tumors, and clinical features of the patients. In this study, a comprehensive multi-omics methodology was employed to examine a distinctive collection patient dataset containing rapid autopsy tumor and normal tissue samples as well as longitudinal imaging with a focus on pancreatic cancer. By performing a whole exome sequencing analysis on tumor and normal tissues to identify somatic gene variants and a radiomics feature analysis to tumor CT images, the genome-wide association approach established a connection between pancreatic cancer driver genes and relevant radiomics features, enabling a thorough and quantitative assessment of the heterogeneity of pancreatic tumors. The significant association between sets of genes and radiomics features revealed the involvement of genes in shaping tumor morphological heterogeneity. Some results of the association established a connection between the molecular level mechanism and their outcomes at the level of tumor structural heterogeneity. Because tumor structure and tumor structural heterogeneity are related to the patients' overall survival, patients who had pancreatic cancer driver gene mutations with an association to a certain radiomics feature have been observed to experience worse survival rates than cases without these somatic mutations. Furthermore, the outcome of the association analysis has revealed potential gene mutations and radiomics feature candidates that warrant further investigation in future research endeavors.
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5
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Smith J, Josef C, Xie Y, Kamaleswaran R. Online Critical-State Detection of Sepsis Among ICU Patients using Jensen-Shannon Divergence. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2023; 2022:982-991. [PMID: 37128380 PMCID: PMC10148343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Sepsis is a severe medical condition caused by a dysregulated host response to infection that has a high incidence and mortality rate. Even with such a high-level occurrence rate, the detection and diagnosis of sepsis continues to pose a challenge. There is a crucial need to accurately forecast the onset of sepsis promptly while also identifying the specific physiologic anomalies that contribute to this prediction in an interpretable fashion. This study proposes a novel approach to quantitatively measure the difference between patients and a reference group using non-parametric probability distribution estimates and highlight when abnormalities emerge using a Jensen-Shannon divergence- based single sample analysis approach. We show that we can quantitatively distinguish between these two groups and offer a measurement of divergence in real time while simultaneously identifying specific physiologic factors contributing to patient outcomes. We demonstrate our approach on a real-world dataset of patients admitted to Atlanta, Georgia's Grady Hospital.
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Affiliation(s)
- Jeffrey Smith
- Georgia Institute of Technology, Atlanta, Georgia, USA
| | | | - Yao Xie
- Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Rishikesan Kamaleswaran
- Georgia Institute of Technology, Atlanta, Georgia, USA
- Emory University School of Medicine Atlanta, Georgia, USA
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6
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Zhong J, Ding D, Liu J, Liu R, Chen P. SPNE: sample-perturbed network entropy for revealing critical states of complex biological systems. Brief Bioinform 2023; 24:7007928. [PMID: 36705581 DOI: 10.1093/bib/bbad028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/25/2022] [Accepted: 01/11/2023] [Indexed: 01/28/2023] Open
Abstract
Complex biological systems do not always develop smoothly but occasionally undergo a sharp transition; i.e. there exists a critical transition or tipping point at which a drastic qualitative shift occurs. Hunting for such a critical transition is important to prevent or delay the occurrence of catastrophic consequences, such as disease deterioration. However, the identification of the critical state for complex biological systems is still a challenging problem when using high-dimensional small sample data, especially where only a certain sample is available, which often leads to the failure of most traditional statistical approaches. In this study, a novel quantitative method, sample-perturbed network entropy (SPNE), is developed based on the sample-perturbed directed network to reveal the critical state of complex biological systems at the single-sample level. Specifically, the SPNE approach effectively quantifies the perturbation effect caused by a specific sample on the directed network in terms of network entropy and thus captures the criticality of biological systems. This model-free method was applied to both bulk and single-cell expression data. Our approach was validated by successfully detecting the early warning signals of the critical states for six real datasets, including four tumor datasets from The Cancer Genome Atlas (TCGA) and two single-cell datasets of cell differentiation. In addition, the functional analyses of signaling biomarkers demonstrated the effectiveness of the analytical and computational results.
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Affiliation(s)
- Jiayuan Zhong
- School of Mathematics and Big Data, Foshan University, Foshan 528000, China
- School of Mathematics, South China University of technology, Guangzhou 510640, China
| | - Dandan Ding
- Department of Thoracic Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou 510095, China
| | - Juntan Liu
- School of Mathematics, South China University of technology, Guangzhou 510640, China
| | - Rui Liu
- School of Mathematics, South China University of technology, Guangzhou 510640, China
- Pazhou Lab, Guangzhou 510330, China
| | - Pei Chen
- School of Mathematics, South China University of technology, Guangzhou 510640, China
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7
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Huo Y, Li C, Li Y, Li X, Xu P, Bao Z, Liu W. Detecting early-warning signals for influenza by dysregulated dynamic network biomarkers. Brief Funct Genomics 2023:7036269. [PMID: 36787234 DOI: 10.1093/bfgp/elad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/15/2023] [Accepted: 01/25/2023] [Indexed: 02/15/2023] Open
Abstract
As a dynamical system, complex disease always has a sudden state transition at the tipping point, which is the result of the long-term accumulation of abnormal regulations. This paper proposes a novel approach to detect the early-warning signals of influenza A (H3N2 and H1N1) outbreaks by dysregulated dynamic network biomarkers (dysregulated DNBs) for individuals. The results of cross-validation show that our approach can detect early-warning signals before the symptom appears successfully. Unlike the traditional DNBs, our dysregulated DNBs are anchored and very few, which is essential for disease early diagnosis in clinical practice. Moreover, the genes of dysregulated DNBs are significantly enriched in the influenza-related pathways. The source code of this paper can be freely downloaded from https://github.com/YanhaoHuo/dysregulated-DNBs.git.
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Affiliation(s)
- Yanhao Huo
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Chuchu Li
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Yujie Li
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Xianbin Li
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China.,School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Peng Xu
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China.,School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Zhenshen Bao
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China.,School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Wenbin Liu
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
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8
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Huang X, Su B, Wang X, Zhou Y, He X, Liu B. A network-based dynamic criterion for identifying prediction and early diagnosis biomarkers of complex diseases. J Bioinform Comput Biol 2022; 20:2250027. [PMID: 36573886 DOI: 10.1142/s0219720022500275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Lung adenocarcinoma (LUAD) seriously threatens human health and generally results from dysfunction of relevant module molecules, which dynamically change with time and conditions, rather than that of an individual molecule. In this study, a novel network construction algorithm for identifying early warning network signals (IEWNS) is proposed for improving the performance of LUAD early diagnosis. To this end, we theoretically derived a dynamic criterion, namely, the relationship of variation (RV), to construct dynamic networks. RV infers correlation [Formula: see text] statistics to measure dynamic changes in molecular relationships during the process of disease development. Based on the dynamic networks constructed by IEWNS, network warning signals used to represent the occurrence of LUAD deterioration can be defined without human intervention. IEWNS was employed to perform a comprehensive analysis of gene expression profiles of LUAD from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The experimental results suggest that the potential biomarkers selected by IEWNS can facilitate a better understanding of pathogenetic mechanisms and help to achieve effective early diagnosis of LUAD. In conclusion, IEWNS provides novel insight into the initiation and progression of LUAD and helps to define prospective biomarkers for assessing disease deterioration.
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Affiliation(s)
- Xin Huang
- School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning 114007, P. R. China
| | - Benzhe Su
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, P. R. China
| | - Xingyu Wang
- School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning 114007, P. R. China
| | - Yang Zhou
- Liaoning Clinical Research Center for Lung Cancer, The Second Hospital of Dalian Medical University Dalian, Liaoning 116023, P. R. China
| | - Xinyu He
- School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning 116029, P. R. China
| | - Bing Liu
- School of Mathematics and Information Science, Anshan Normal University, Anshan, Liaoning 114007, P. R. China
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9
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Yang Y, Tian Z, Song M, Ma C, Ge Z, Li P. Detecting the Critical States of Type 2 Diabetes Mellitus Based on Degree Matrix Network Entropy by Cross-Tissue Analysis. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1249. [PMID: 36141135 PMCID: PMC9498060 DOI: 10.3390/e24091249] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 08/18/2022] [Accepted: 09/02/2022] [Indexed: 06/16/2023]
Abstract
Type 2 diabetes mellitus (T2DM) is a metabolic disease caused by multiple etiologies, the development of which can be divided into three states: normal state, critical state/pre-disease state, and disease state. To avoid irreversible development, it is important to detect the early warning signals before the onset of T2DM. However, detecting critical states of complex diseases based on high-throughput and strongly noisy data remains a challenging task. In this study, we developed a new method, i.e., degree matrix network entropy (DMNE), to detect the critical states of T2DM based on a sample-specific network (SSN). By applying the method to the datasets of three different tissues for experiments involving T2DM in rats, the critical states were detected, and the dynamic network biomarkers (DNBs) were successfully identified. Specifically, for liver and muscle, the critical transitions occur at 4 and 16 weeks. For adipose, the critical transition is at 8 weeks. In addition, we found some "dark genes" that did not exhibit differential expression but displayed sensitivity in terms of their DMNE score, which is closely related to the progression of T2DM. The information uncovered in our study not only provides further evidence regarding the molecular mechanisms of T2DM but may also assist in the development of strategies to prevent this disease.
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Affiliation(s)
- Yingke Yang
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
| | - Zhuanghe Tian
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
| | - Mengyao Song
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
| | - Chenxin Ma
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
| | - Zhenyang Ge
- College of Agriculture, Henan University of Science and Technology, Luoyang 471023, China
| | - Peiluan Li
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
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10
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Gao R, Yan J, Li P, Chen L. Detecting the critical states during disease development based on temporal network flow entropy. Brief Bioinform 2022; 23:6587172. [PMID: 35580862 DOI: 10.1093/bib/bbac164] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/23/2022] [Accepted: 04/12/2022] [Indexed: 12/12/2022] Open
Abstract
Complex diseases progression can be generally divided into three states, which are normal state, predisease/critical state and disease state. The sudden deterioration of diseases can be viewed as a bifurcation or a critical transition. Therefore, hunting for the tipping point or critical state is of great importance to prevent the disease deterioration. However, it is still a challenging task to detect the critical states of complex diseases with high-dimensional data, especially based on an individual. In this study, we develop a new method based on network fluctuation of molecules, temporal network flow entropy (TNFE) or temporal differential network flow entropy, to detect the critical states of complex diseases on the basis of each individual. By applying this method to a simulated dataset and six real diseases, including respiratory viral infections and tumors with four time-course and two stage-course high-dimensional omics datasets, the critical states before deterioration were detected and their dynamic network biomarkers were identified successfully. The results on the simulated dataset indicate that the TNFE method is robust under different noise strengths, and is also superior to the existing methods on detecting the critical states. Moreover, the analysis on the real datasets demonstrated the effectiveness of TNFE for providing early-warning signals on various diseases. In addition, we also predicted disease deterioration risk and identified drug targets for cancers based on stage-wise data.
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Affiliation(s)
- Rong Gao
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
| | - Jinling Yan
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
| | - Peiluan Li
- School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China
| | - Luonan Chen
- Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.,Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.,Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong 519031, China.,International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
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11
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Bose I. Tipping the Balance: A Criticality Perspective. ENTROPY 2022; 24:e24030405. [PMID: 35327916 PMCID: PMC8947304 DOI: 10.3390/e24030405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/10/2022] [Accepted: 03/12/2022] [Indexed: 01/02/2023]
Abstract
Cell populations are often characterised by phenotypic heterogeneity in the form of two distinct subpopulations. We consider a model of tumour cells consisting of two subpopulations: non-cancer promoting (NCP) and cancer-promoting (CP). Under steady state conditions, the model has similarities with a well-known model of population genetics which exhibits a purely noise-induced transition from unimodality to bimodality at a critical value of the noise intensity σ2. The noise is associated with the parameter λ representing the system-environment coupling. In the case of the tumour model, λ has a natural interpretation in terms of the tissue microenvironment which has considerable influence on the phenotypic composition of the tumour. Oncogenic transformations give rise to considerable fluctuations in the parameter. We compute the λ−σ2 phase diagram in a stochastic setting, drawing analogies between bifurcations and phase transitions. In the region of bimodality, a transition from a state of balance to a state of dominance, in terms of the competing subpopulations, occurs at λ = 0. Away from this point, the NCP (CP) subpopulation becomes dominant as λ changes towards positive (negative) values. The variance of the steady state probability density function as well as two entropic measures provide characteristic signatures at the transition point.
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Affiliation(s)
- Indrani Bose
- Department of Physics, Bose Institute, 93/1, A. P. C. Road, Kolkata 700009, India
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