3.7.1 0.9

Life is about Connections

Friends of the Foundary & Biomaths Colloquium


John "Scooter" Morris, Ph.D.
Swansea University
19 June 2019

Slides: https://rbvi.github.io/chimera-tutorials/presentations/FriendsOfTheFoundary-2019.html/

Connections Big Data Dealing with complexity What are we doing about it? Challenge

Connections

Connections in life

How did I get here?

Connections

Connections in biology

Connections

Connections in biology

Connections

Connections in biology

Connections

Connections in biology

Connections

Connections in biology

  • Cell-cell interactions
    • Gap junctions
    • Tight junctions
    • Desmosomes
    • Cell signaling

Connections - why do we care?

“...empirically derived networks are necessary for describing the molecular mechanisms and biological processes that drive disease under the influences of inherited risk factors (genetic markers) and environmental risk factors.”

Schadt EE, Björkegren JL. Science translational medicine. 2012

Connections - why do we care?

Citations: 83,549 papers published in the last decade about biological networks

Finding connections

  • Molecular interactions
  • Cell-cell interactions
    • Imaging techniques
    • Microfluidic approaches
    • Biochemical measurements
  • Interspecies interactions
    • Host-pathogen proteomics (BioID, AP/MS)
Connections Big Data Dealing with complexity What are we doing about it? Challenge

(Really) Big Data

Back of the envelope calculations for # of interactions in a human

# of proteins in a human cell~1x1010
# of lipids in a human cell~1x1010
# of metabolites in a human cell~1x108
# of RNA molecules in a human cell~1x108
# of miRNA molecules in a human cell~1x104
# of molecular entities* = ~1x1040
# of cells in a human~3.7x1013
Potential interactions** = <<1.3x10107

* Doesn't include carbohydrates, other small RNAs

** Actual interactions are much, much less due to cellular compartments, tissue specificity, etc., etc.

(Really) Big Data

But wait, there's more!

  • Connections aren't static. They vary based on:
    • time (development, aging, time of day, physiological changes)
    • environmental conditions
    • disease (e.g. infection, somatic mutation)
  • Organisms connect also
    • Host/pathogen interactions
    • Symbiosis
    • Competition
    • Predation
Connections Big Data Dealing with complexity What are we doing about it? Challenge

Dealing with complexity

Simplification: pathways

Dealing with complexity

Simplification: module detection

  • Topology-driven modules
  • Attribute-driven modules
  • Hybrid clusters

Dealing with complexity

Machine Learning

Pai, et al. (2019)

Dealing with complexity

Interaction

  • Overview+detail
  • Focus+context
  • Linked views
  • Brushing and linking
Connections Big Data Dealing with complexity What are we doing about it? Challenge

Our Projects

Tool: ChimeraX

Our Projects

Tool: Cytoscape

Our Projects

Tool: scNetViz

  • Problem: putting single-cell RNA seq results in a broader biological context
  • Challenge: 100,000 cells, 27,000 genes
  • Solution: aggregate within clusters and do network analysis between clusters

Our Projects

Tool: SPOKE

  • Problem: lots of public databases relevant to human disease
  • Challenge: they aren't linked and there's no easy way to navigate through them
  • Solution: create a large "knowledge network" that combines multiple databases

Our Projects

Node types
  • Anatomy
  • BiologicalProcess
  • CellularComponent
  • Compound
  • Disease
  • Gene
  • MolecularFunction
  • Pathway
  • PharmacologicClass
  • Protein
  • SideEffect
  • Symptom
Edge types
  • Anatomy→contains→Anatomy
  • Anatomy-expresses-Gene
  • Anatomy→isa→Anatomy
  • Anatomy→partof→Anatomy
  • Compound-affects-(mutant)Gene
  • Compound-binds-Protein
  • Compound-causes-SideEffect
  • Compound-contraindicates-Disease
  • Compound-resembles-Compound
  • Compound-treats-Disease
  • Disease-associates-Gene
  • Disease→contains→Disease
  • Disease→isa→Disease
  • Disease-localizes-Anatomy
  • Disease-presents-Symptom
  • Disease-resembles-Disease
  • Gene-covaries-Gene
  • Gene-participates-BiologicalProcess
  • Gene-participates-CellularComponent
  • Gene-participates-MolecularFunction
  • Gene-participates-Pathway
  • PharmacologicClass-includes-Compound
  • Protein-interacts-Protein
  • Protein-translatedfrom-Gene

Our Projects

Tool: Future

  • Differential networks
  • Dynamic networks
Connections Big Data Dealing with complexity What are we doing about it? Challenge

Challenge

Understanding biological connections is critical to:

  • understanding biological processes
  • understanding how species adapt
  • understanding evolution
  • understanding (and combating) disease

Challenge

Understanding biological connections will take:

    • new computational algorithms and approaches to deal with massive scale
    • new approaches to visualization to support in the interpretation of data
    • new collaborations between biology, medical science, computer science, engineering, and mathematics

Challenge

Observations

  • Swansea University has:
    • Amazing biology
    • Exciting medical sciences
    • Talented mathemeticians and computer scientists
  • Swansea University doesn't have:
    • Computational Biology program to bring all of this together

Challenge

Some ideas...

  • a computational biology colloquium series, in collaboration with the biomaths colloquia?
  • more cross-campus events to bring researchers together?
  • an open-door policy to encourage collaboration?

Connections

Discussion

Thank you for your attention!