🧬Systems Biology Unit 15 – Disease Modeling & Network Drug Discovery

Key Concepts

• Systems biology applies a holistic approach to studying biological systems by integrating data from various levels (molecular, cellular, tissue, organ, and organism)
• Disease modeling aims to understand the complex mechanisms underlying human diseases using computational and mathematical techniques
  • Involves the integration of multi-omics data (genomics, transcriptomics, proteomics, and metabolomics)
• Network biology focuses on the interactions between biological components (genes, proteins, and metabolites) and how these interactions contribute to disease pathogenesis
• Drug discovery benefits from systems biology approaches by identifying novel drug targets and predicting potential side effects
  • Network-based drug discovery strategies include drug repositioning and combination therapy
• Challenges in disease modeling and network drug discovery include data heterogeneity, model validation, and translating findings into clinical practice

Disease Modeling Basics

• Disease modeling involves the development of computational and mathematical models to simulate the progression and mechanisms of human diseases
• Models can be based on various data types, such as gene expression, protein interactions, and clinical data
• The goal of disease modeling is to identify key drivers and pathways involved in disease pathogenesis
  • Key drivers are genes or proteins that play a central role in disease progression
• Disease models can be used to predict disease outcomes, stratify patients, and guide treatment decisions
• Common types of disease models include network-based models, agent-based models, and ordinary differential equation (ODE) models
  • Network-based models represent the interactions between biological components (genes, proteins) as a graph
  • Agent-based models simulate the behavior of individual cells or molecules in a complex system
• Disease modeling requires the integration of multi-omics data to capture the complexity of biological systems

Network Biology Fundamentals

• Network biology studies the interactions between biological components (genes, proteins, and metabolites) and how these interactions give rise to complex phenotypes
• Biological networks are represented as graphs, with nodes representing biological entities (genes, proteins) and edges representing interactions (physical, functional, or regulatory)
• Network topology refers to the arrangement of nodes and edges in a network
  • Common topological features include hubs (highly connected nodes), modules (densely connected subnetworks), and motifs (recurring patterns of interactions)
• Network centrality measures, such as degree centrality and betweenness centrality, are used to identify important nodes in a network
• Network perturbation analysis involves simulating the effect of node or edge removal on network structure and function
  • Can be used to identify essential genes or drug targets
• Network-based approaches have been applied to study various biological processes, such as gene regulation, protein-protein interactions, and metabolic pathways

Data Sources and Integration

• Disease modeling and network drug discovery rely on the integration of multi-omics data from various sources
• Genomic data, such as DNA sequencing and gene expression profiles, provide information on genetic variations and gene regulation
  • Databases: The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO)
• Proteomic data, such as protein-protein interaction networks and mass spectrometry, reveal the structure and function of proteins
  • Databases: STRING, BioGRID, Human Protein Reference Database (HPRD)
• Metabolomic data, such as metabolite profiles and flux analysis, provide insights into cellular metabolism
• Clinical data, including electronic health records (EHRs) and patient-reported outcomes, are essential for validating disease models and assessing treatment efficacy
• Data integration techniques, such as network integration and multi-view learning, are used to combine heterogeneous data types
  • Challenges in data integration include data quality, standardization, and batch effects

Computational Methods

• Various computational methods are employed in disease modeling and network drug discovery
• Machine learning techniques, such as support vector machines (SVMs) and random forests, are used for feature selection and classification tasks
  • Can be applied to predict disease outcomes or identify biomarkers
• Deep learning approaches, including convolutional neural networks (CNNs) and graph neural networks (GNNs), are increasingly used for analyzing complex biological data
  • CNNs are effective for image-based tasks, such as histopathology analysis
  • GNNs are suitable for modeling graph-structured data, such as biological networks
• Network analysis tools, such as Cytoscape and NetworkX, enable the visualization and analysis of biological networks
• Pathway enrichment analysis methods, such as Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA), identify overrepresented biological pathways in a given gene set
• Mathematical modeling techniques, including ordinary differential equations (ODEs) and Boolean networks, are used to simulate the dynamics of biological systems
  • ODEs describe the continuous change of variables over time
  • Boolean networks represent the state of biological entities as binary variables (on/off)

Drug Discovery Applications

• Network-based approaches have been applied to various stages of the drug discovery pipeline
• Drug target identification involves identifying key nodes (genes, proteins) in disease-associated networks that can be targeted by drugs
  • Network centrality measures and perturbation analysis are used to prioritize potential drug targets
• Drug repositioning aims to find new indications for existing drugs by leveraging network-based similarities between diseases and drugs
  • Can reduce the time and cost of drug development compared to de novo drug discovery
• Combination therapy involves identifying synergistic drug combinations based on network topology and pathway analysis
  • Aims to improve treatment efficacy and overcome drug resistance
• Side effect prediction uses network-based methods to anticipate potential adverse drug reactions
  • Can be based on drug-target interactions, drug-drug interactions, or drug-disease associations
• Personalized medicine leverages patient-specific network models to tailor treatment strategies
  • Incorporates genetic, clinical, and lifestyle factors to optimize drug selection and dosing

Challenges and Limitations

• Disease modeling and network drug discovery face several challenges and limitations
• Data heterogeneity and quality issues can affect the accuracy and reproducibility of computational models
  • Requires standardized data collection, processing, and integration methods
• Model validation is essential to ensure the reliability and clinical relevance of disease models
  • Requires extensive experimental and clinical data for benchmarking
• Translating computational findings into clinical practice remains a significant challenge
  • Requires close collaboration between computational biologists, experimental scientists, and clinicians
• Biological complexity, including context-dependent interactions and dynamic processes, can limit the predictive power of network-based models
• Computational efficiency and scalability become important considerations when dealing with large-scale biological networks and high-dimensional data
• Ethical and legal issues, such as data privacy and intellectual property, need to be addressed when working with patient data and developing new drugs

Future Directions

• Integration of multi-scale data, from molecular to population levels, to build more comprehensive disease models
  • Incorporating environmental factors, lifestyle data, and social determinants of health
• Development of explainable AI methods to improve the interpretability of complex disease models
  • Enhancing trust and adoption of computational models in clinical decision-making
• Expansion of network-based approaches to study rare diseases and drug repurposing opportunities
  • Leveraging existing knowledge from well-studied diseases to inform rare disease research
• Integration of real-world evidence, such as electronic health records and patient registries, to validate and refine disease models
  • Enabling continuous learning and adaptation of models based on real-world data
• Advancement of single-cell technologies and spatial omics to capture cellular heterogeneity and spatial context in disease modeling
  • Providing a more granular understanding of disease mechanisms and drug responses
• Collaborative efforts to establish standardized benchmarks, data sharing platforms, and best practices in disease modeling and network drug discovery
  • Fostering reproducibility, transparency, and cross-disciplinary collaboration