Numerical methods and modeling strategies that should be applied more in Quantitative Cell Biology

Discussion leaders for breakout sessions

Adriana Dawes

David Odde

Erkan Tuzel

Outcome from breakout sessions

The following is an unordered list of ideas that were recorded during breakout discussions in Workshops 2 and 3.  These will be considered as potential topics to be integrated into future workshops as  way to explore how these approaches may be more generally applied in quantitative cell biology

Dynamical non-linear time series analysis

Higher level image analysis (image understanding)

Computational fluid dynamics (DPD, smoothed particle dynamics (SPH)

Improved model selection approaches

Proposal:  Can we develop standard datasets to serve as gold standards for common cell biology problems (particle tracking, etc)

Bifurcation theory

Generative models

Model comparison methods

"Deep Learning"

Intelligent sampling / Enhanced sampling

Implementing methods using GPUs

Quantifying uncertainty in data and models

Computational advances in solving differential equations

Heteroscadasticity

Using big data approaches to understand simulations

Methods from computational meteorology

Methods to handle stochasticity versus parametric uncertainty

Using finite element analysis to mesh with continuum mechanics

Non-equilibrium thermodynamics

Moving boundary methods

Phase field theory

Complex fluids and Coloids