Jack Snoeyink & Matt O’Meara Dept. Computer Science UNC Chapel Hill

Collaborators  Brian Kuhlman, UNC Biochem  Many other members of the RosettaCommons  Richardson lab, Duke Biochem Funding  NIH  NSF

 Scientific Models, esp. for Structural Molecular Biology  Models are the lens through which we view data  Models are predominantly geometric  Computational models are complex  Models evolve, so testing becomes crucial  Focus on statistical/computational models with  a sample source, observable local features, chosen functional form, fit parameters, & visualization/testing methods  Capture assumptions and date used to build models to:  Visualize for making design decisions while building  Fit parameters to ensure best performance  Record as scientific benchmarks Case Study: Rosetta protein structure prediction software [B]

 Scientific Models, esp. for Struct. Molec. Bio.  Why: Model as Lens  Who: Scientists as model builders  What: Geometric models  Which: Conceptual vs. Computational  Where: Evaluative vs. Predictive  When: Model evolution  How: Roles of models

 Physical and Conceptual models  Kept simple to aid understanding  Statistical and Computational models  Evolve by combining simple models  Even when complex can still be effective at Validation (Molprobity) or Prediction (Rosetta)

Spiral development, much like software  Discover problematic features in some data  Create an energy function to adjust them  Fit parameters to improve results  Check into the software as a new option  Make default option if everyone likes it  Occasionally refactor and rewrite, removing outdated or unused models But less support for testing…

Our goal: Capture data and assumptions from model building for use in model visualization and testing.

Abstraction: A simple component of a complex computational model consists of:  One or more sample sources giving  Pdb files from native or decoys  Observable local features having a  Hydrogen bond distances and angles  Chosen functional form that  Energy from distances and angles  Depends on fitting parameters  Weights for combining terms KMB’03

data set A data set B data set Z... SQL query ggplot2 spec plots statistics gather features filtertransform Tool schematic

Implemented tools  Compare distributions from sample sources  Tufte’s small multiples via ggplot  Kernel density estimation  Normalization Opportunities for  Statistical analysis  Dimension reduction …

[KMB’03] Histogram of Hbond A-H distances in natives

Scientific unit tests native, HEAD, ^HEAD run on continuously testing server Knowledge-base score term creation native, release, experimental turn exploration into living benchmarks Test design hypotheses native, protocol, designs how strange is the this geometry?

Rotamer recovery

 Scientific Models, esp. for Structural Molecular Biology  Models are the lens through which we view data  Models are predominantly geometric  Computational models are complex  Models evolve, so testing becomes crucial  Focus on statistical/computational models with  a sample source, observable local features, chosen functional form, fit parameters, & visualization/testing methods  Capture assumptions and date used to build models to:  Visualize for making design decisions while building  Fit parameters to ensure best performance  Record as scientific benchmarks Case Study: Rosetta protein structure prediction software [B]

Visualization plays important roles in all stages of the life cycle of computational models