Computational Systems Biology in Medicine and Biotechnology: Methods and Protocols (Methods in Molecular Biology, 2399)

Dedication
Preface
Contents
Contributors
Chapter 1: Computational Systems Biology and Artificial Intelligence
1 Introduction
2 Is There a Place for AI in CSB? Or for CSB in AI?
3 Understanding Through Simulation, Explanation, and Prediction
4 A Way Ahead for Computational Systems Biology
References
Part I: Systems Biology of the Genome, Epigenome, and Redox Proteome
Chapter 2: Bioinformatic Analysis of CircRNA from RNA-seq Datasets
1 Introduction
2 Materials
3 Methods
3.1 Identify RNA-seq Datasets to Analyze
3.2 Obtain the FASTQ Files from These Datasets, Containing Unprocessed RNA-seq Reads
3.3 Align the FASTQ Files to the Human Genome
3.4 Use a circRNA-Identifying Software Such as CIRCexplorer2 to Generate Annotated circRNA Junction Reads
3.5 Construct Bioinformatically the Body of the circRNAs
3.6 Analyze Bioinformatically the Levels of circRNAs
3.7 Propose Functions for circRNAs Differentially Abundant
4 Example to Illustrate Workflow
5 Notes
References
Chapter 3: Single-Cell Analysis of the Transcriptome and Epigenome
1 Introduction
1.1 Single-Cell Transcriptomic Approaches
1.2 Single-Cell Epigenomic Approaches
1.3 Single-Cell Multiomics Approaches
1.4 Single-Cell Multiplexing Approaches
1.5 Single-Cell Functional Genomics Approaches
2 Methods
2.1 Computational Methods to Analyze Single-Cell Transcriptomics Data
2.1.1 Data Preprocessing
2.1.2 Quality Control
2.1.3 Batch Correction
2.1.4 Data Normalization
2.1.5 Dimensionality Reduction
2.1.6 Cell Clustering, Find Marker Genes
2.1.7 Trajectory Analysis
2.1.8 Splice-Variant Analysis Using SMART-Seq
2.1.9 CITE-seq and Cell-Hashing
2.2 Computational Methods to Analyze Single-Cell ATAC-seq Data
2.2.1 Data Preprocessing
2.2.2 Quality Control
2.2.3 Batch Correction
2.2.4 Data Normalization
2.2.5 Dimensionality Reduction Visuals
2.2.6 Cell Clustering, Find Marker Genes
2.2.7 Trajectory Analysis
2.2.8 Chromatin Variation Across Regions
2.2.9 Enhancer-Promoter Looping Predictions by Cicero
2.3 Computational Methods to Analyze Single-Cell DNA Multiomics Data
2.4 Conclusions
3 Notes
References
Chapter 4: Automating Assignment, Quantitation, and Biological Annotation of Redox Proteomics Datasets with ProteoSushi
1 Introduction
2 Materials
2.1 Sample Preparation and Mass Spectrometry Analysis
2.2 Mass Spectrometry Data Availability
2.3 Computational Resources and Required Software
3 Methods
3.1 Redox Proteomics Sample Preparation
3.1.1 Overview of Liquid Chromatography-Mass Spectrometry (LC-MS): Data-Dependent Acquisitions (DDA) and Data-Independent Acqu...
3.1.2 Liquid Chromatography-Mass Spectrometry (LC-MS)
3.2 MS2 Database Searches to Generate Peptide Spectral Matches (PSMs)
3.3 Label Free Data-Independent Acquisition (DIA) Quantitation Using Skyline
3.4 Processing Peptide-Centric, PTM-Focused Proteomic Results Using ProteoSushi
3.4.1 ProteoSushi Data Requirements
3.4.2 ProteoSushi Installation and Data Analysis
3.5 Statistical Analysis of Redox Regulated Cysteine Sites: Multiple Hypothesis Correction
3.5.1 Analyses of Variance (ANOVA)
3.6 Statistical Analysis of Biological Annotations
3.6.1 Peptide Annotation Enrichment Analysis: Fisher Exact Test
3.6.2 Monte Carlo Simulation
3.7 Conclusions
4 Notes
References
Part II: Systems Biology of Metabolic Networks
5: A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling
1 Introduction
2 Materials
2.1 Data Mining in Biomedicine
2.2 Constraint-Based Reconstruction and Modeling
2.3 Machine Learning for Multi-Omic Data Integration
2.4 Multimodal Machine Learning
2.5 Multi-Omic Data Integration with Survival Analysis
2.5.1 Evaluation Metrics for Survival Analysis
C-Index
Brier Score
Mean Absolute Error
2.6 Multi-Omic Analysis Using Deep Neural Networks
2.7 Multimodal GSMMs-Merging Metabolic Analyses with Machine Learning
3 Methods
3.1 Integrating Gene Expression Data into Flux Balance Analysis
3.1.1 System Requirements
3.1.2 Flux Balance Analysis
3.2 Survival Analysis
3.3 Multi-Omic Data Integration and Machine Learning Analyses
3.3.1 System Requirements
3.3.2 Classification Task with Early Data Integration
3.3.3 Regression Task with Late Data Integration
3.4 Conclusions
4 Notes
References
Chapter 6: MITODYN: An Open Source Software for Quantitative Modeling of Mitochondrial and Cellular Energy Metabolic Flux Dyna...
Abbreviations
1 Introduction
2 Materials
3 Methods
3.1 Mitodyn Performance: A Detailed Respiratory Chain Model
3.1.1 Respiratory Complex I
3.1.2 Respiratory Complex II
3.1.3 Respiratory Complex III
3.1.4 Reactive Oxygen Species (ROS) Generation as Implemented in the Model
3.2 Model Implementation
3.3 Conclusions
4 Notes
References
Chapter 7: Integrated Multiomics, Bioinformatics, and Computational Modeling Approaches to Central Metabolism in Organs
1 Introduction
2 Materials
2.1 Metabolite Profiling and Bioinformatic Analyses
2.2 Computational Tools
3 Methods
3.1 Metabolomics Analysis
3.2 Computing the Fluxome Through Central Metabolism
3.3 Reduction in the Dimension of the Algebraic Problem for Optimizing vmax
3.4 Representative Results
4 Notes
5 Conclusion
References
Part III: Systems Biology of Aging and Longevity
Chapter 8: Understanding the Human Aging Proteome Using Epidemiological Models
1 Introduction
2 Materials
2.1 Modeling Methods Used in Epidemiology
2.2 Adjustment for Confounding Factors and Outcomes
2.3 Sample Collection
2.4 Phenotypic Information of the Sample
3 Methods
3.1 Sample Preparation for SOMAscan Based Plasma Analysis
3.2 Sample Preparation for Mass Spectrometry (MS) Based Skeletal Muscle Analysis
3.3 Bioinformatics Analysis of the SOMAscan Plasma Data
3.4 Bioinformatics Analysis of the MS Skeletal Muscle Data
3.5 Plasma Proteome Data Interpretation and Data Visualization
3.6 Skeletal Muscle Proteome Data Interpretation and Data Visualization
3.7 Integrations of Epidemiological Models and Proteomic Analysis Results
3.8 Advantages and Limitations of the Epidemiological Models in Proteomic Analysis
3.9 Conclusion
4 Notes
References
Chapter 9: Unraveling Pathways of Health and Lifespan with Integrated Multiomics Approaches
1 Introduction
2 Materials
3 Methods
3.1 Sample Preparation for Liver Transcriptomics
3.2 Sample Preparation for Liver Metabolomics
3.3 Pathways of Lifespan
3.4 Pathways of Health Span
3.5 The Impact of Diet on Health Preservation
3.6 Validation of the Integrated Multiomics Analyses
3.7 Conclusions
4 Notes
References
Part IV: Systems Biology of Disease
Chapter 10: UT-Heart: A Finite Element Model Designed for the Multiscale and Multiphysics Integration of our Knowledge on the ...
1 Introduction
2 Methods
2.1 Mesh Generation
2.2 Electrophysiology
2.2.1 Cell Model of Electrophysiology
2.2.2 Propagation of Excitation
2.2.3 Personalization of Electrophysiology
2.3 Mechanics
2.3.1 Sarcomere Model
2.3.2 Heart Mechanics
2.3.3 Circulatory Model
2.3.4 Personalization of the Circulatory Model
2.4 Integrated Model
2.4.1 Prediction of the Therapeutic Effect of Cardiac Resynchronization Therapy (CRT)
2.4.2 In Silico Surgery
3 Notes
4 Conclusion
References
Chapter 11: Multiscale Modeling of the Mitochondrial Origin of Cardiac Reentrant and Fibrillatory Arrhythmias
1 Introduction
2 Materials
3 Methods
4 Notes
References
Chapter 12: Automated Quantification and Network Analysis of Redox Dynamics in Neuronal Mitochondria
1 Introduction
2 Materials
2.1 Experimental Agents
2.2 Image Analysis Software
3 Methods
3.1 Experimental Methods
3.1.1 Imaging Mitochondrial Redox Dynamics in Cell Culture In Vitro
3.1.2 Imaging Neuronal Mitochondrial Redox Dynamics in Ex Vivo Preparations
3.2 Analytical Methods
3.2.1 Image Analysis
3.2.2 Extract Individual Mitochondrial Fluorescence Traces
3.2.3 Mitochondrial Signal Events
3.2.4 Mitochondrial Intensity Trace Wavelet Analysis
3.2.5 Mitochondrial Morphological Properties
3.2.6 Mitochondrial Clusters
3.2.7 Mitochondrial Signal Propagation
4 Notes
5 Conclusions
References
Part V: Systems Biology of Rhythms, Morphogenesis, and Complex Dynamics
Chapter 13: Computational Approaches and Tools as Applied to the Study of Rhythms and Chaos in Biology
1 Introduction
1.1 Acknowledging the Importance of Time-Dependent Fluctuations in Complex Biological Systems
1.2 Clocks, Chaos, and a Wide Range of Dynamic Regimes
1.2.1 Biological Circadian and Ultradian Rhythms
1.2.2 Calcium Dynamics as an Example of the Diversity of Possible Dynamic States
1.3 Combining Experimental Design with Appropriate Mathematical Tools to Investigate Temporal Patterns in Time Series
2 Methods
2.1 Informative Metrics in Time Series Analysis
2.1.1 Actograms
2.1.2 Smoothing Data: Binning, Moving Average, and Detrending
2.1.3 Discretization of Raw Data into Events
2.1.4 Histograms and Probability Distribution of Raw Data and Events
2.1.5 Autocorrelation Estimation and the Correlogram
2.1.6 Harmonic Analysis
2.1.7 Power Spectrum Analysis for the Analysis of Rhythms
2.1.8 Lagged Phase Space Plots, Embedding, and Attractor Reconstruction
Box 1 What is an Attractor?
2.1.9 Lyapunov Exponent
2.1.10 Wavelet Analysis
2.1.11 Synchrosqueezing
2.1.12 Correlations Between Time Series and Wavelet Coherence
2.2 Two Cases Studies for Investigating Biological Time Series
2.2.1 Wheel Running and Food Intake Behavioral Rhythms in Mice Subjected to Caloric Restriction
2.2.2 Chaos in Calcium Dynamics in a Mitochondrial Model
3 Notes
References
Chapter 14: Computational Systems Biology of Morphogenesis
1 Introduction
2 Materials
2.1 Computational Modeling
2.2 Machine Learning
2.3 Validation
3 Methods
3.1 Computational Modeling at the Systems Level
Box 1 MATLAB code to simulate a Turing reaction-diffusion system
3.2 Computational Systems Biology of Whole Embryos
3.3 Machine Learning of Computational Systems Biology Models
Box 2 Evolutionary algorithm pseudocode for the inference of systems biology models
3.4 Validating Systems-Level Models with Computational Predictions
Box 3 Example execution of MoCha to find genes with particular regulatory interactions. The user input command is in blue and ...
3.5 Conclusions
4 Notes
References
Chapter 15: Agent-Based Modeling of Complex Molecular Systems
1 Introduction
2 Materials
3 Methods
3.1 Modeling the NF-κB Regulatory Network
Model Development
Model Build-up
Model Expansion
Model Validation
3.2 Further Applications for Using Agent-Based Modeling in Biology
3.2.0 The Dynamics of Tissue Growth and Repair
3.2.0 The Metabolic Basis of Bacterial Dynamics
3.2.0 The Impact of Compartmentalization and Kinetics on Signal Specificity
3.2.0 The Dynamics of Blood Flow
3.3 Conclusion
4 Notes
References
Part VI: Systems Biology in Biotechnology
Chapter 16: Metabolic Modeling of Wine Fermentation at Genome Scale
1 Introduction
2 Materials
2.1 Metabolic Model
2.2 Software
3 Methods
3.1 Phenotype Prediction Using Experimental Data
3.1.1 Calculation of Flux Distributions Using Specific Uptake/Production Rates
Calculation of Flux Distributions in Continuous Cultures
Calculation of Flux Distributions in Batch Cultures
3.1.2 Sensitivity Analysis
3.2 Determination of Nutritional Requirements and Comparison with Experimental Data
3.2.1 Minimal Media Determination
3.2.2 Omission Simulations and Comparison with Experimental Data
3.2.3 Addition of Alternative Carbon Sources and Comparison with Experimental Data
3.3 Prediction of Flavor Compounds Production
3.4 Conclusions
4 Notes
Appendix
Tutorial 1
Tutorial 2
Tutorial 3
Tutorial 4
Tutorial 5
References
Chapter 17: Modeling Approaches to Microbial Metabolism
1 Introduction
2 Knowledge Representation and Types of Mathematical Models
3 Mass Conservation on Biochemical Networks
4 Examples for Models for Bacterial Systems
4.1 Coarse-Grained Model
4.2 Stoichiometric Model
4.3 Kinetic Model
4.4 Conclusion
5 Notes
6 Glossary
6.1 Bio-based Economy
6.2 Intervention Strategy
6.3 Iterative Cycle of Experimental Investigation and Model Based Analysis
6.4 Mathematical Model
6.5 Network Reconstruction
6.6 Network Representation (Stoichiometric or Incidence Matrix)
References
Correction to: Multiscale Modeling of the Mitochondrial Origin of Cardiac Reentrant and Fibrillatory Arrhythmias
Index