20.3 Systems virology and computational approaches

Systems virology combines virology, biology, and computation to study viral infections holistically. It uses high-tech tools to generate big datasets, aiming to understand complex virus-host interactions and emergent properties that can't be seen by looking at individual parts alone.

This approach uses network analysis to find key players in virus-host interactions and models infection dynamics over time. It helps predict viral behavior, host responses, and potential treatments based on integrated data analysis, pushing the boundaries of virology research.

Systems virology principles

Interdisciplinary approach and goals

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• Systems virology combines virology, systems biology, and computational biology to study viral infections holistically
• Aims to understand complex interactions between viruses and host cells, including dynamic changes in cellular networks during infection
• Employs high-throughput technologies to generate large-scale datasets (genomics, transcriptomics, proteomics, metabolomics)
• Studies emergent properties arising from complex virus-host interactions, which cannot be understood by examining individual components alone
  • Example: Synergistic effects of multiple viral proteins on host cell signaling
  • Example: Emergent patterns in viral evolution due to host immune pressure

Network analysis and temporal dynamics

• Network analysis identifies critical nodes and pathways in virus-host interactions
  • Example: Identifying hub proteins in host-pathogen protein-protein interaction networks
  • Example: Mapping viral protein interactions with host cellular machinery
• Temporal dynamics of viral infections studied using time-series experiments and mathematical modeling
  • Captures progression of infection from initial entry to viral replication and host response
  • Example: Modeling the kinetics of influenza virus replication in lung epithelial cells over 48 hours
• Predicts viral behavior, host responses, and potential intervention strategies based on integrated data analysis
  • Example: Forecasting the spread of a novel virus in a population based on its genomic sequence and initial transmission data

Computational modeling of virus-host interactions

Mathematical representations and simulation techniques

• Creates mathematical representations of viral infection processes and host immune responses
• Agent-based models simulate individual virus particles and host cells to study infection dynamics
  • Example: Modeling the spread of HIV in a population of CD4+ T cells
  • Example: Simulating the movement and interactions of viruses within a host cell
• Ordinary differential equation (ODE) models describe kinetics of viral replication and immune responses over time
  • Example: Modeling the dynamics of hepatitis C virus infection and clearance in the liver
  • $\frac{dV}{dt} = pI - cV$, where V is virus concentration, I is infected cell count, p is virus production rate, and c is clearance rate

Machine learning and bioinformatics applications

• Machine learning algorithms predict virus-host protein interactions and identify potential drug targets
  • Neural networks and support vector machines used for classification and prediction tasks
  • Example: Predicting binding sites of viral proteins on host cell receptors
• Bioinformatics tools analyze large-scale genomic and proteomic data to identify patterns in virus-host interactions
  • Example: Identifying conserved sequence motifs in viral genomes across multiple strains
• Network inference algorithms reconstruct regulatory networks involved in viral infections and host immune responses
  • Example: Inferring gene regulatory networks activated during interferon response to viral infection

In silico experimentation and hypothesis testing

• Enables in silico experiments to test hypotheses and guide wet-lab experiments
  • Reduces time and resources required for research
  • Example: Simulating the effects of different antiviral compounds on viral replication before conducting laboratory tests
• Facilitates parameter estimation and sensitivity analysis for biological models
  • Example: Determining the most critical factors influencing viral transmission rates in a population model

Systems biology for antiviral drug discovery

Network-based approaches and virtual screening

• Network pharmacology uses protein-protein interaction networks to identify novel drug targets
  • Aims to disrupt viral replication or enhance host immune responses
  • Example: Identifying host proteins that interact with multiple viral proteins as potential broad-spectrum antiviral targets
• Virtual screening techniques coupled with systems-level data predict efficacy of compound libraries
  • Example: Screening millions of compounds in silico for binding affinity to viral protease enzymes
• Pathway analysis understands impact of antiviral drugs on host cellular processes
  • Identifies potential off-target effects
  • Example: Analyzing the effect of a viral entry inhibitor on host cell membrane trafficking pathways

Drug repurposing and combination therapy strategies

• Systems-level drug repurposing analyzes existing drugs' effects on host-virus interaction networks
  • Identifies new antiviral applications for approved drugs
  • Example: Repurposing the malaria drug chloroquine for potential use against SARS-CoV-2
• Combination therapy design utilizes systems approaches to predict synergistic drug effects
  • Optimizes dosing strategies for multiple drugs
  • Example: Designing a triple-drug combination therapy for HIV treatment based on network analysis of viral resistance mechanisms
• Pharmacogenomics integrates genomic data with systems biology to predict individual treatment responses
  • Guides personalized medicine approaches in antiviral therapy
  • Example: Predicting hepatitis C treatment efficacy based on host genetic variants and viral genotype

Omics data integration in systems virology

Multi-omics integration techniques

• Combines data from genomics, transcriptomics, proteomics, and metabolomics for comprehensive view of virus-host interactions
• Data integration techniques identify patterns across different omics datasets
  • Multi-layer network analysis and tensor factorization used to integrate diverse data types
  • Example: Integrating transcriptomics and proteomics data to identify post-transcriptional regulation during viral infection
• Integration of host and viral omics data identifies key molecular events in viral pathogenesis and host immune responses
  • Leads to new hypotheses and potential therapeutic targets
  • Example: Identifying host factors required for viral replication by integrating viral-host protein interaction data with host transcriptomics

Specific omics approaches in virology

• Transcriptomics reveals host gene expression changes during viral infection
  • RNA-seq and microarray studies crucial for understanding cellular responses
  • Example: Profiling gene expression changes in lung epithelial cells during influenza virus infection
• Proteomics identifies changes in protein abundance and post-translational modifications
  • Mass spectrometry-based techniques used to study protein-level changes
  • Example: Quantifying changes in phosphorylation of host signaling proteins during herpesvirus infection
• Metabolomics provides insights into alterations in cellular metabolism during viral infections
  • Reveals potential metabolic targets for antiviral therapies
  • Example: Identifying metabolic pathways upregulated in hepatocytes during hepatitis B virus replication
• Epigenomics uncovers how viruses manipulate host gene regulation
  • DNA methylation and histone modification analyses used to study epigenetic changes
  • Example: Mapping changes in histone acetylation patterns in host cells infected with human papillomavirus