AWG Medicine and Bioinformatics
F. Cazals
INRIA

3D and Knowledge in Medicine and
Bioinformatics
Medicine
Geometric Modeling
Diagnostic
Therapy planning
Surgery
Radiotherapy
Legal medicine

3D and Knowledge in Medicine and
Bioinformatics
Biology
Docking
Molecular surfaces and volumes
Modeling molecular interfaces
Modeling pockets and channels
Databases (e.g. PDB), functional classifications
Folding
Conformers
Atomic environments
Folding styles

Geometry and Knowledge: Synergy
Model: geometry and knowledge
Geometry:
At organ level: tissues (volumes and
surfaces),
interfaces (surfaces), relative
positioning.
At molecule level: atoms (locations,
shape), bonds, secondary structure,
domains.
Knowledge:
Anatomy, physiological and
pathological parameters.
Chemical and interaction types, affinity
specificity of interactions.

Contacts
Medicine
#contacts: 20
#questionnaires: 12
Categories
Methodology
e.g., image segmentation, mesh generation,
simulation
Application
e.g., organ or pathology-specific
Integration
environment suppliers, medical teams

Contacts
Biology
#contacts: 20
#questionnaires: 13
Categories
Methodology
e.g., shape matching and comparison, shape
reconstruction
Applications
folding, docking, drug design
Integration
environment suppliers, pharma-companies, biological
teams

Example Key Players in Medicine
Methodology
Applied mathematicians: e.g., model design for
dynamic heart simulation.
Application
Electro-physiologist: computer aided diagnostic
and recovery after heart attack.
Integration
Medical team: optimization of pacemaker
placement, resection.

Example Key Players in Biology
Methodology
Biophysicist / applied mathematician: e.g.,
algorithms for electrostatic calculations (Poisson-
Boltzmann)‫‏‬
Application
Biophysicist into folding and misfolding (e.g.,
amyeloid diseases)‫‏‬
Integration
Structural Modeling group: from bio-physical
measurements to simulations

Scenario in Biomedicine
Simulating human articulations:
bone resection
Prosthesis placement
Parameters of interest:
contact pressure within the
cartilages
forces during motion
muscle biomechanics
Connection to physiology:
material properties
bone motion and geometry
Since the parameters are
stored in the ontology, the data
process is simplified.

Scenario in Biomedicine
Modeling anatomy
patient-specific
therapy planning
e.g., air flow
simulation
Parameters of interest: segmentation of upper respiratory tract
flow, pressure
deformations
Connection to physiology:
material properties
geometry of upper
respiratory tract
mesh generation
for simulation

Scenario in Bioinformatics
Docking two molecules involves:
Homology modeling
Rigid body docking
Side chain placement
Molecular dynamics simulations
Parameters of interest:
Homology between proteins
Known partners
Rigid domains within protein
Key residues
Correlated motions
Connections to experiments
Directed mutagenesis
Affinity and specificity
measurements (e.g. BIACORE)

Assessment
Case 1: The problem description is complete
Key parameters identified
Geometric models effective for description and prediction
Ex: articulations, zoo of protein folds
Ontologies crucial to integrate
Synergy needed between geometry, models and ontologies
Case 2: Problem partially described
Key parameters not yet identified
Incomplete knowledge
Hazardous extrapolation and prediction
Ex: brain modeling, protein folding process
Building blocks of models to be elaborated upon
Integration will come next