Network Pharmacology: Harnessing STRING, STITCH, and Clustering for Cellular Biology

network-pharma-string-stitch-clustering.md

Slide 1: Title & Introduction

Title:

Network Pharmacology: Harnessing STRING, STITCH, and Clustering for Cellular Biology

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Introduction:

• Our Goal:

  • Make complex biological data more accessible to cellular biologists.

• Initial Challenge:

  • Finding tools to simplify handling of complex datasets.

• Key Point:

  • Ensuring biologists can easily access and work with data to derive actionable insights.

• Importance of Tools like STRING and STITCH:

  • These tools help visualize complex molecular interactions.

• Inna's Role:

  • She will explain clustering techniques later in the talk, and how it can be used to provide deeper insight.

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Slide 2: Exploring STRING & STITCH

Title:

Powerful Tools for Network Pharmacology: STRING & STITCH

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Content:

• STRING:

  • Visualizes protein-protein interactions.

• STITCH:

  • Maps compound-target networks.

• Visualization of Complex Interactions:

  • Both tools help biologists visualize complex molecular interactions.

• Exploring Enriched Pathways and Compound-Target Relationships:

  • Useful for exploring enriched pathways and compound-target relationships.

• Key Point:

  • These tools make biological data more intuitive for biologists.

• Insights into Disease Mechanisms and Therapeutic Targets:

  • Provide insights into disease mechanisms and therapeutic targets.

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Slide 3: Case Study - Applying STRING to NSCLC

Title:

Case Study: Exploring Non-Small Cell Lung Cancer (NSCLC) Pathways

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Content:

• Real-World Application:

  • Walk through an example of applying STRING/STITCH to NSCLC.

• Focus on Key Genes:

  • Genes like KRAS, EGFR, and TP53.

• Exploring Activation and Inactivation Behavior:

  • Demonstrate how to explore gene activation/inactivation.

• Mapping Genes to Known Pathways:

  • Show how to map genes to known pathways.

• Enrichment Uncovers Central Pathways:

  • Discuss how enrichment reveals pathways central to NSCLC progression.

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Slide 4: Tackling Granularity in Gene-Isoform Relationships

Title:

Addressing Granularity: Genes vs. Isoforms

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Content:

• Challenge with Isoforms:

  • BioGrid often focuses on isoforms rather than base genes.

• Solution with Clustering Algorithms:

  • Use clustering algorithms like K-means and MCL.

• How Clustering Helps:

  • Groups isoforms with their base genes.

• Key Point:

  • Advanced analytical methods simplify data while retaining biological relevance.

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Slide 5: Transition to Clustering

Title:

Clustering to Make Sense of Complex Data

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Content:

• Importance of Clustering:

  • Extracts patterns from large datasets.

• K-means and MCL Algorithms:

  • K-means Clustering: Partitions data into k clusters.
  • Markov Cluster Algorithm (MCL): Detects clusters in network data.

• Understanding Isoform and Gene Relationships:

  • Clustering helps understand these relationships.

• Benefits with Enriched Data:

  • Pairing clustering with enriched data enhances insights.

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Slide 6: Inna's Technical Segment

Title:

Clustering in Action: K-means and MCL for Biological Data

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Content:

• Detailed Explanation of Clustering Algorithms:

  • In-depth look at K-means and MCL.

• Demonstration with Research Examples:

  • Examples from NSCLC research.

• Influence of Enriched Data on Clustering Results:

  • How enriched data affects clustering outcomes.

• Uncovering Hidden Patterns:

  • Clustering reveals hidden patterns in biological datasets.

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Slide 7: Conclusion

Recap of Key Points:

• STRING and STITCH Enhance Accessibility:

  • Simplify complex data for biologists.

• Real-World Application in NSCLC Research:

  • Provides valuable insights into disease pathways.

• Addressing Granularity with Clustering:

  • Simplifies data without losing critical information.

• Value of Clustering in Data Interpretation:

  • Essential for discovering hidden patterns and relationships.

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Future Directions:

• Continued Integration of Tools:

  • Further combine STRING, STITCH, and clustering algorithms.

• Potential for New Discoveries:

  • Aim for breakthroughs in NSCLC and other diseases.

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Slide 8: Thank You

Closing Remarks:

• Appreciation:

  • Thank the audience for their attention.

• Q & A:

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Note: You can enhance this presentation by adding relevant images, charts, or graphs where appropriate. If you're using software like PowerPoint or Google Slides, consider incorporating:

• Visual Aids:

  • Diagrams of protein-protein interactions from STRING.
  • Network maps from STITCH showing compound-target relationships.
  • Flowcharts illustrating the clustering process.

• Case Study Visuals:

  • Pathway diagrams for NSCLC.
  • Graphs showing gene activation/inactivation.

• Clustering Examples:

  • Visual representations of clusters formed by K-means and MCL.

Tips for Creating the Presentation:

• Consistency:

  • Use a consistent theme and color scheme throughout the slides.

• Clarity:

  • Keep text concise and use bullet points for easy reading.

• Engagement:

  • Include questions or prompts to engage the audience.

• References:

  • Cite any sources or tools used, such as STRING and STITCH websites.