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Xu S, Xu T, Yang Y, Chen X. Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network. mSystems 2024; 9:e0069724. [PMID: 39057922 PMCID: PMC11334518 DOI: 10.1128/msystems.00697-24] [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: 05/21/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
Modeling microbial metabolic dynamics is important for the rational optimization of both biosynthetic systems and industrial processes to facilitate green and efficient biomanufacturing. Classical approaches utilize explicit equation systems to represent metabolic networks, enabling the quantification of pathway fluxes to identify metabolic bottlenecks. However, these white-box models, despite their diverse applications, have limitations in simulating metabolic dynamics and are intrinsically inaccurate for industrial strains that lack information on network structures and kinetic parameters. On the other hand, black-box models do not rely on prior mechanistic knowledge of strains but are built upon observed time-series trajectories of biosynthetic systems in action. In practice, these observations are typically irregular, with discontinuously observed time points across multiple independent batches, each time point potentially containing missing measurements. Learning from such irregular data remains challenging for existing approaches. To address this issue, we present the Bidirectional Time-Series State Transfer Network (BTSTN) for modeling metabolic dynamics directly from irregular observations. Using evaluation data sets derived from both ideal dynamic systems and a real-world fermentation process, we demonstrate that BTSTN accurately reconstructs dynamic behaviors and predicts future trajectories. This approach exhibits enhanced robustness against missing measurements and noise, as compared to the state-of-the-art methods.IMPORTANCEIndustrial biosynthetic systems often involve strains with unclear genetic backgrounds, posing challenges in modeling their distinct metabolic dynamics. In such scenarios, white-box models, which commonly rely on inferred networks, are thereby of limited applicability and accuracy. In contrast, black-box models, such as statistical models and neural networks, are directly fitted or learned from observed time-series trajectories of biosynthetic systems in action. These methods typically assume regular observations without missing time points or measurements. If the observations are irregular, a pre-processing step becomes necessary to obtain a fully filled data set for subsequent model training, which, at the same time, inevitably introduces errors into the resulting models. BTSTN is a novel approach that natively learns from irregular observations. This distinctive feature makes it a unique addition to the current arsenal of technologies modeling metabolic dynamics.
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Affiliation(s)
- Shaohua Xu
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Microbial Biochemistry and Metabolic Engineering, Hangzhou, China
| | - Ting Xu
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuping Yang
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
| | - Xin Chen
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Microbial Biochemistry and Metabolic Engineering, Hangzhou, China
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Ma C, Gurkan-Cavusoglu E. A comprehensive review of computational cell cycle models in guiding cancer treatment strategies. NPJ Syst Biol Appl 2024; 10:71. [PMID: 38969664 PMCID: PMC11226463 DOI: 10.1038/s41540-024-00397-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.
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Affiliation(s)
- Chenhui Ma
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Evren Gurkan-Cavusoglu
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA
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Konstantyner T, Areco KCN, Bandiera-Paiva P, Marinonio ASS, Kawakami MD, Balda RDCX, Miyoshi MH, Sanudo A, Costa-Nobre DT, de Freitas RMV, Morais LCC, Teixeira MLP, Waldvogel BC, Kiffer CRV, de Almeida MFB, Guinsburg R. The burden of inappropriate birth weight on neonatal survival in term newborns: a population-based study in a middle-income setting. Front Pediatr 2023; 11:1147496. [PMID: 37360363 PMCID: PMC10285294 DOI: 10.3389/fped.2023.1147496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/10/2023] [Indexed: 06/28/2023] Open
Abstract
Introduction Premature birth, perinatal asphyxia, and infections are the main causes of neonatal death. Growth deviations at birth also affect neonatal survival according to week of gestation at birth, particularly in developing countries. The purpose of this study was to verify the association between inappropriate birth weight and neonatal death in term live births. Methods This is an observational follow-up study with all term live births from 2004 to 2013 in Sao Paulo State, Brazil. Data were retrieved with the deterministic linkage of death and birth certificates. The definition of very small for gestational age (VSGA) and very large for gestational age (VLGA) used the 10th percentile of 37 weeks and the 90th percentile of 41 weeks + 6 days, respectively, based on the Intergrowth-21st. We measured the outcome in terms of time to death and the status of each subject (death or censorship) in the neonatal period (0-27 days). Survival functions were calculated using the Kaplan-Meier method stratified according to the adequacy of birth weight into three groups (normal, very small, or very large). We used multivariate Cox regression to adjust for proportional hazard ratios (HRs). Results The neonatal death rate during the study period was 12.03/10,000 live births. We found 1.8% newborns with VSGA and 2.7% with VLGA. The adjusted analysis showed a significant increase in mortality risk for VSGA infants (HR = 4.25; 95% CI: 3.89-4.65), independent of sex, 1-min Apgar score, and five maternal factors. Discussion The risk of neonatal death in full-term live births was approximately four times greater in those with birth weight restriction. The development of strategies to control the factors that determine fetal growth restriction through planned and structured prenatal care can substantially reduce the risk of neonatal death in full-term live births, especially in developing countries such as Brazil.
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Affiliation(s)
- Tulio Konstantyner
- Departamento de Pediatria, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | - Kelsy Catherina Nema Areco
- Departamento de Pediatria, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
- Departamento de Informática em Saúde, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | - Paulo Bandiera-Paiva
- Departamento de Informática em Saúde, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | | | - Mandira Daripa Kawakami
- Departamento de Pediatria, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | - Rita de Cássia Xavier Balda
- Departamento de Pediatria, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | - Milton Harumi Miyoshi
- Departamento de Pediatria, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | - Adriana Sanudo
- Departamento de Medicina Preventiva, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | - Daniela Testoni Costa-Nobre
- Departamento de Pediatria, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | - Rosa Maria Vieira de Freitas
- Diretoria Adjunta de Produção e Análise de Dados, Fundação Sistema Estadual de Análise de Dados, São Paulo, São Paulo, Brazil
| | - Liliam Cristina Correia Morais
- Diretoria Adjunta de Produção e Análise de Dados, Fundação Sistema Estadual de Análise de Dados, São Paulo, São Paulo, Brazil
| | - Monica La Porte Teixeira
- Diretoria Adjunta de Produção e Análise de Dados, Fundação Sistema Estadual de Análise de Dados, São Paulo, São Paulo, Brazil
| | - Bernadette Cunha Waldvogel
- Diretoria Adjunta de Produção e Análise de Dados, Fundação Sistema Estadual de Análise de Dados, São Paulo, São Paulo, Brazil
| | - Carlos Roberto Veiga Kiffer
- Departamento de Medicina, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
| | | | - Ruth Guinsburg
- Departamento de Pediatria, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, São Paulo, Brazil
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Liu S, You Y, Tong Z, Zhang L. Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research. Front Genet 2021; 12:761629. [PMID: 34764986 PMCID: PMC8576451 DOI: 10.3389/fgene.2021.761629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.
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Affiliation(s)
- Suran Liu
- College of Computer Science, Sichuan University, Chengdu, China
| | - Yujie You
- College of Computer Science, Sichuan University, Chengdu, China
| | - Zhaoqi Tong
- College of Software Engineering, Sichuan University, Chengdu, China
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
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5
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Constantino CS, Carvalho AM, Vinga S. Coupling sparse Cox models with clustering of longitudinal transcriptomics data for trauma prognosis. BioData Min 2021; 14:25. [PMID: 33853663 PMCID: PMC8048345 DOI: 10.1186/s13040-021-00257-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 03/29/2021] [Indexed: 11/18/2022] Open
Abstract
Background Longitudinal gene expression analysis and survival modeling have been proved to add valuable biological and clinical knowledge. This study proposes a novel framework to discover gene signatures and patterns in a high-dimensional time series transcriptomics data and to assess their association with hospital length of stay. Methods We investigated a longitudinal and high-dimensional gene expression dataset from 168 blunt-force trauma patients followed during the first 28 days after injury. To model the length of stay, an initial dimensionality reduction step was performed by applying Cox regression with elastic net regularization using gene expression data from the first hospitalization days. Also, a novel methodology to impute missing values to the genes selected previously was proposed. We then applied multivariate time series (MTS) clustering to analyse gene expression over time and to stratify patients with similar trajectories. The validation of the patients’ partitions obtained by MTS clustering was performed using Kaplan-Meier curves and log-rank tests. Results We were able to unravel 22 genes strongly associated with hospital’s discharge. Their expression values in the first days after trauma showed to be good predictors of the length of stay. The proposed mixed imputation method allowed to achieve a complete dataset of short time series with a minimum loss of information for the 28 days of follow-up. MTS clustering enabled to group patients with similar genes trajectories and, notably, with similar discharge days from the hospital. Patients within each cluster have comparable genes’ trajectories and may have an analogous response to injury. Conclusion The proposed framework was able to tackle the joint analysis of time-to-event information with longitudinal multivariate high-dimensional data. The application to length of stay and transcriptomics data revealed a strong relationship between gene expression trajectory and patients’ recovery, which may improve trauma patient’s management by healthcare systems. The proposed methodology can be easily adapted to other medical data, towards more effective clinical decision support systems for health applications.
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Affiliation(s)
- Cláudia S Constantino
- INESC-ID, Instituto Superior Técnico, ULisboa, R. Alves Redol 9, Lisbon, 1000-029, Portugal
| | - Alexandra M Carvalho
- Instituto de Telecomunicações, Instituto Superior Técnico, ULisboa, Av. Rovisco Pais 1, Lisbon, 1049-001, Portugal
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, ULisboa, R. Alves Redol 9, Lisbon, 1000-029, Portugal. .,IDMEC, Instituto Superior Técnico, ULisboa, Av. Rovisco Pais 1, Lisbon, 1049-001, Portugal.
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Park RM. A Simple Toxicokinetic Model Exhibiting Complex Dynamics and Nonlinear Exposure Response. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2020; 40:2561-2571. [PMID: 32632964 PMCID: PMC7748990 DOI: 10.1111/risa.13547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 06/02/2020] [Accepted: 06/13/2020] [Indexed: 06/11/2023]
Abstract
Uncertainty in model predictions of exposure response at low exposures is a problem for risk assessment. A particular interest is the internal concentration of an agent in biological systems as a function of external exposure concentrations. Physiologically based pharmacokinetic (PBPK) models permit estimation of internal exposure concentrations in target tissues but most assume that model parameters are either fixed or instantaneously dose-dependent. Taking into account response times for biological regulatory mechanisms introduces new dynamic behaviors that have implications for low-dose exposure response in chronic exposure. A simple one-compartment simulation model is described in which internal concentrations summed over time exhibit significant nonlinearity and nonmonotonicity in relation to external concentrations due to delayed up- or downregulation of a metabolic pathway. These behaviors could be the mechanistic basis for homeostasis and for some apparent hormetic effects.
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Affiliation(s)
- Robert M. Park
- Division of Science Integration, National Institute for Occupational Safety and Health, 1090 Tusculum Ave, MS C-15, Cincinnati OH, USA
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7
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Ghulam A, Lei X, Guo M, Bian C. A Review of Pathway Databases and Related Methods Analysis. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191018162505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Pathway analysis integrates most of the computational tools for the investigation of
high-level and complex human diseases. In the field of bioinformatics research, biological pathways
analysis is an important part of systems biology. The molecular complexities of biological
pathways are difficult to understand in human diseases, which can be explored through pathway
analysis. In this review, we describe essential information related to pathway databases and their
mechanisms, algorithms and methods. In the pathway database analysis, we present a brief introduction
on how to gain knowledge from fundamental pathway data in regard to specific human
pathways and how to use pathway databases and pathway analysis to predict diseases during an
experiment. We also provide detailed information related to computational tools that are used in
complex pathway data analysis, the roles of these tools in the bioinformatics field and how to store
the pathway data. We illustrate various methodological difficulties that are faced during pathway
analysis. The main ideas and techniques for the pathway-based examination approaches are presented.
We provide the list of pathway databases and analytical tools. This review will serve as a
helpful manual for pathway analysis databases.
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Affiliation(s)
- Ali Ghulam
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Min Guo
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Chen Bian
- School of Computer Science, Shaanxi Normal University, Xian, China
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8
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Sun L, Jiang L, Grant CN, Wang HG, Gragnoli C, Liu Z, Wu R. Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk. Cancers (Basel) 2020; 12:cancers12082086. [PMID: 32731407 PMCID: PMC7465094 DOI: 10.3390/cancers12082086] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 07/24/2020] [Accepted: 07/25/2020] [Indexed: 01/03/2023] Open
Abstract
Neuroblastoma is a common cancer in children, affected by a number of genes that interact with each other through intricate but coordinated networks. Traditional approaches can only reconstruct a single regulatory network that is topologically not informative enough to explain the complexity of neuroblastoma risk. We implemented and modified an advanced model for recovering informative, omnidirectional, dynamic, and personalized networks (idopNetworks) from static gene expression data for neuroblastoma risk. We analyzed 3439 immune genes of neuroblastoma for 217 high-risk patients and 30 low-risk patients by which to reconstruct large patient-specific idopNetworks. By converting these networks into risk-specific representations, we found that the shift in patients from a low to high risk or from a high to low risk might be due to the reciprocal change of hub regulators. By altering the directions of regulation exerted by these hubs, it may be possible to reduce a high risk to a low risk. Results from a holistic, systems-oriented paradigm through idopNetworks can potentially enable oncologists to experimentally identify the biomarkers of neuroblastoma and other cancers.
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Affiliation(s)
- Lidan Sun
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China; (L.S.); (L.J.)
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China; (L.S.); (L.J.)
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Christa N. Grant
- Division of Pediatric Surgery, Department of Surgery, Penn State College of Medicine, Hershey, PA 17033, USA;
| | - Hong-Gang Wang
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, Penn State College of Medicine, Hershey, PA 17022, USA;
| | - Claudia Gragnoli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
- Division of Endocrinology, Diabetes, and Metabolic Disease, Translational Medicine, Department of Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19144, USA
- Molecular Biology Laboratory, Bios Biotech Multi Diagnostic Health Center, 00197 Rome, Italy
| | - Zhenqiu Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
- Division of Pediatric Hematology and Oncology, Department of Pediatrics, Penn State College of Medicine, Hershey, PA 17022, USA;
- Correspondence: (Z.L.); (R.W.); Tel.: +1-717-531-0003 (Z.L.); +1-717-531-2037 (R.W.)
| | - Rongling Wu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA;
- Correspondence: (Z.L.); (R.W.); Tel.: +1-717-531-0003 (Z.L.); +1-717-531-2037 (R.W.)
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Anguita-Ruiz A, Segura-Delgado A, Alcalá R, Aguilera CM, Alcalá-Fdez J. eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research. PLoS Comput Biol 2020; 16:e1007792. [PMID: 32275707 PMCID: PMC7176286 DOI: 10.1371/journal.pcbi.1007792] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 04/22/2020] [Accepted: 03/17/2020] [Indexed: 12/18/2022] Open
Abstract
Until date, several machine learning approaches have been proposed for the dynamic modeling of temporal omics data. Although they have yielded impressive results in terms of model accuracy and predictive ability, most of these applications are based on "Black-box" algorithms and more interpretable models have been claimed by the research community. The recent eXplainable Artificial Intelligence (XAI) revolution offers a solution for this issue, were rule-based approaches are highly suitable for explanatory purposes. The further integration of the data mining process along with functional-annotation and pathway analyses is an additional way towards more explanatory and biologically soundness models. In this paper, we present a novel rule-based XAI strategy (including pre-processing, knowledge-extraction and functional validation) for finding biologically relevant sequential patterns from longitudinal human gene expression data (GED). To illustrate the performance of our pipeline, we work on in vivo temporal GED collected within the course of a long-term dietary intervention in 57 subjects with obesity (GSE77962). As validation populations, we employ three independent datasets following the same experimental design. As a result, we validate primarily extracted gene patterns and prove the goodness of our strategy for the mining of biologically relevant gene-gene temporal relations. Our whole pipeline has been gathered under open-source software and could be easily extended to other human temporal GED applications.
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Affiliation(s)
- Augusto Anguita-Ruiz
- Department of Biochemistry and Molecular Biology II, Institute of Nutrition and Food Technology "José Mataix", Center of Biomedical Research, University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
- CIBEROBN (Physiopathology of Obesity and Nutrition), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Alberto Segura-Delgado
- Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - Rafael Alcalá
- Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
| | - Concepción M. Aguilera
- Department of Biochemistry and Molecular Biology II, Institute of Nutrition and Food Technology "José Mataix", Center of Biomedical Research, University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
- CIBEROBN (Physiopathology of Obesity and Nutrition), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Jesús Alcalá-Fdez
- Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
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10
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Li W, Zhang W, Zhang J. A Novel Model Integration Network Inference Algorithm with Clustering and Hub Genes Finding. Mol Inform 2020; 39:e1900075. [DOI: 10.1002/minf.201900075] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Accepted: 01/14/2020] [Indexed: 11/08/2022]
Affiliation(s)
- Wenchao Li
- State Key Laboratory of Industrial Control TechnologyInstitute of Cyber-Systems and Control of Zhejiang University Hangzhou China
| | - Wei Zhang
- State Key Laboratory of Industrial Control TechnologyInstitute of Cyber-Systems and Control of Zhejiang University Hangzhou China
| | - Jianming Zhang
- State Key Laboratory of Industrial Control TechnologyInstitute of Cyber-Systems and Control of Zhejiang University Hangzhou China
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11
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Sherman TD, Kagohara LT, Cao R, Cheng R, Satriano M, Considine M, Krigsfeld G, Ranaweera R, Tang Y, Jablonski SA, Stein-O'Brien G, Gaykalova DA, Weiner LM, Chung CH, Fertig EJ. CancerInSilico: An R/Bioconductor package for combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer. PLoS Comput Biol 2019; 14:e1006935. [PMID: 31002670 PMCID: PMC6504085 DOI: 10.1371/journal.pcbi.1006935] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 05/07/2019] [Accepted: 03/11/2019] [Indexed: 11/18/2022] Open
Abstract
Bioinformatics techniques to analyze time course bulk and single cell omics data
are advancing. The absence of a known ground truth of the dynamics of molecular
changes challenges benchmarking their performance on real data. Realistic
simulated time-course datasets are essential to assess the performance of time
course bioinformatics algorithms. We develop an R/Bioconductor package,
CancerInSilico, to simulate bulk and single cell
transcriptional data from a known ground truth obtained from mathematical models
of cellular systems. This package contains a general R infrastructure for
running cell-based models and simulating gene expression data based on the model
states. We show how to use this package to simulate a gene expression data set
and consequently benchmark analysis methods on this data set with a known ground
truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/
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Affiliation(s)
- Thomas D. Sherman
- Department of Oncology, Division of Biostatistics and Bioinformatics,
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore,
MD United States of America
- * E-mail:
(TDS); (EJF)
| | - Luciane T. Kagohara
- Department of Oncology, Division of Biostatistics and Bioinformatics,
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore,
MD United States of America
| | - Raymon Cao
- Department of Oncology, Division of Biostatistics and Bioinformatics,
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore,
MD United States of America
| | - Raymond Cheng
- Science, Math and Computer Science Magnet Program, Poolesville High
School, Poolesville, MD United States of America
| | - Matthew Satriano
- Department of Mathematics, University of Waterloo, Waterloo, Ontario,
Canada
| | - Michael Considine
- Department of Oncology, Division of Biostatistics and Bioinformatics,
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore,
MD United States of America
| | - Gabriel Krigsfeld
- Department of Oncology, Division of Biostatistics and Bioinformatics,
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore,
MD United States of America
| | | | - Yong Tang
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington,
DC United States of America
| | - Sandra A. Jablonski
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington,
DC United States of America
| | - Genevieve Stein-O'Brien
- Department of Oncology, Division of Biostatistics and Bioinformatics,
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore,
MD United States of America
- Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD
United States of America
| | - Daria A. Gaykalova
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins
University School of Medicine, Baltimore, MD United States of
America
| | - Louis M. Weiner
- Lombardi Comprehensive Cancer Center, Georgetown University, Washington,
DC United States of America
| | | | - Elana J. Fertig
- Department of Oncology, Division of Biostatistics and Bioinformatics,
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore,
MD United States of America
- Department of Applied Mathematics and Statistics, Johns Hopkins
University, Baltimore, MD United States of America
- Department of Biomedical Engineering, Johns Hopkins University,
Baltimore, MD United States of America
- * E-mail:
(TDS); (EJF)
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Computational methods for Gene Regulatory Networks reconstruction and analysis: A review. Artif Intell Med 2019; 95:133-145. [DOI: 10.1016/j.artmed.2018.10.006] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 10/23/2018] [Accepted: 10/23/2018] [Indexed: 01/14/2023]
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Maldonado EM, Taha F, Rahman J, Rahman S. Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases. Front Genet 2019; 10:19. [PMID: 30774647 PMCID: PMC6367241 DOI: 10.3389/fgene.2019.00019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 01/14/2019] [Indexed: 12/14/2022] Open
Abstract
Primary mitochondrial diseases form one of the most common and severe groups of genetic disease, with a birth prevalence of at least 1 in 5000. These disorders are multi-genic and multi-phenotypic (even within the same gene defect) and span the entire age range from prenatal to late adult onset. Mitochondrial disease typically affects one or multiple high-energy demanding organs, and is frequently fatal in early life. Unfortunately, to date there are no known curative therapies, mostly owing to the rarity and heterogeneity of individual mitochondrial diseases, leading to diagnostic odysseys and difficulties in clinical trial design. This review aims to discuss recent advances and challenges of systems approaches for the study of primary mitochondrial diseases. Although there has been an explosion in the generation of omics data, few studies have progressed toward the integration of multiple levels of omics. It is evident that the integration of different types of data to create a more complete representation of biology remains challenging, perhaps due to the scarcity of available integrative tools and the complexity inherent in their use. In addition, "bottom-up" systems approaches have been adopted for use in the iterative cycle of systems biology: from data generation to model prediction and validation. Primary mitochondrial diseases, owing to their complex nature, will most likely benefit from a multidisciplinary approach encompassing clinical, molecular and computational studies integrated together by systems biology to elucidate underlying pathomechanisms for better diagnostics and therapeutic discovery. Just as next generation sequencing has rapidly increased diagnostic rates from approximately 5% up to 60% over two decades, more recent advancing technologies are encouraging; the generation of multi-omics, the integration of multiple types of data, and the ability to predict perturbations will, ultimately, be translated into improved patient care.
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Affiliation(s)
- Elaina M. Maldonado
- Mitochondrial Research Group, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Fatma Taha
- Mitochondrial Research Group, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Joyeeta Rahman
- Mitochondrial Research Group, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Shamima Rahman
- Mitochondrial Research Group, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
- Metabolic Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom
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Stéphanou A, Fanchon E, Innominato PF, Ballesta A. Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why. Acta Biotheor 2018; 66:345-365. [PMID: 29744615 DOI: 10.1007/s10441-018-9330-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Accepted: 05/05/2018] [Indexed: 12/22/2022]
Abstract
Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.
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Affiliation(s)
- Angélique Stéphanou
- Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38000, Grenoble, France.
| | - Eric Fanchon
- Université Grenoble Alpes, CNRS, TIMC-IMAG/DyCTIM2, 38000, Grenoble, France
| | - Pasquale F Innominato
- North Wales Cancer Centre, Betsi Cadwaladr University Health Board, Bangor, Denbighshire, UK
- INSERM and Université Paris 11 Unit 935, Villejuif, France
- University of Warwick, Coventry, UK
| | - Annabelle Ballesta
- INSERM and Université Paris 11 Unit 935, Villejuif, France
- University of Warwick, Coventry, UK
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Deep Multi-OMICs and Multi-Tissue Characterization in a Pre- and Postprandial State in Human Volunteers: The GEMM Family Study Research Design. Genes (Basel) 2018; 9:genes9110532. [PMID: 30400254 PMCID: PMC6266773 DOI: 10.3390/genes9110532] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 10/18/2018] [Accepted: 10/29/2018] [Indexed: 12/16/2022] Open
Abstract
Cardiovascular disease (CVD) and type 2 diabetes (T2D) are increasing worldwide. This is mainly due to an unhealthy nutrition, implying that variation in CVD risk may be due to variation in the capacity to manage a nutritional load. We examined the genomic basis of postprandial metabolism. Our main purpose was to introduce the GEMM Family Study (Genetics of Metabolic Diseases in Mexico) as a multi-center study carrying out an ongoing recruitment of healthy urban adults. Each participant received a mixed meal challenge and provided a 5-hours’ time course series of blood, buffy coat specimens for DNA isolation, and adipose tissue (ADT)/skeletal muscle (SKM) biopsies at fasting and 3 h after the meal. A comprehensive profiling, including metabolomic signatures in blood and transcriptomic and proteomic profiling in SKM and ADT, was performed to describe tendencies for variation in postprandial response. Our data generation methods showed preliminary trends indicating that by characterizing the dynamic properties of biomarkers with metabolic activity and analyzing multi-OMICS data it could be possible, with this methodology and research design, to identify early trends for molecular biology systems and genes involved in the fasted and fed states.
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Cuperlovic-Culf M. Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling. Metabolites 2018; 8:E4. [PMID: 29324649 PMCID: PMC5875994 DOI: 10.3390/metabo8010004] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 01/08/2018] [Accepted: 01/09/2018] [Indexed: 01/15/2023] Open
Abstract
Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.
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Affiliation(s)
- Miroslava Cuperlovic-Culf
- Digital Technologies Research Center, National Research Council of Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada.
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