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Data, models & computation
for stochastic dynamic cellular networks
in systems biology
Mike West
Department of Statistical Science
Duke University
INI Cambridge, June 24th 2008
Single cell studies - dynamic data
Much intra-cellular behaviour (including gene
expression) is intrinsically stochastic
Cellular systems cannot be properly understood
(hence predicted and controlled) unless
appropriate stochastic components are
incorporated into dynamic cellular network models
INI Cambridge, June 24th 2008
Synthetic bacterial gene circuits “emulate” gene networks
key to mammalian cell proliferation (and cancer)
c.f. Studies on mammalian cells
Mammalian Rb/E2f pathway:
Mammalian cell
development & fate
network
Feed-Forward
(Cancer Systems
Biology)
Stochastic models: States=RNA levels over time
Data - movies: multiple genes over time
Positive Feedback
Fit, assess, refine models:
- evaluate cell-specific stochasticity
- multiple cancer cell lines
- predict network responses to interventions
INI Cambridge, June 24th 2008
Synthetic circuit
T7
Partial data over time on
elements of yt
INI Cambridge, June 24th 2008
Aspects of inference & computation
Many (#cells): stochastic cell-specific effects, experimental noise
Parameters (rate constants)
Unobserved (latent) time series of (1,2,..) RNAs
Fine time scale model: crude time scale data
Imputation of uncertain state variables
Model fitting, assessment, comparison
Simulation-based Bayesian analysis: parameters and latent states
Markov chain Monte Carlo methods for dynamic, non-linear systems
Integration of time course, single cell data with “marginal” data from flow cytometry
- “snapshots in time on 105+ cells
INI Cambridge, June 24th 2008
Stochastic imputation of latent processes
HMM: Forward filtering backward sampling
(FFBS)
yt+k
yt
Latent process
xt
xt+1
t
t+1
xt+k-1
xt+k
t+k
Filtering:
Sampling:
Latent “missing” states imputed
INI Cambridge, June 24th 2008
Mixture modelling Metropolis MCMC
mixture
INI Cambridge, June 24th 2008
Mixture modelling Metropolis MCMC
mixture
INI Cambridge, June 24th 2008
Imputed trajectories
+ data
Posterior for parameters
Information content:
prior
posterior
INI Cambridge, June 24th 2008
Data extraction: single cell dynamic imaging
Novel hybrid-image-based segmentation algorithms &
neighborhood-based cell tracking
• E-coli
• Budding yeast
• Mammalian cells
Open source software
Cell lineage reconstruction
INI Cambridge, June 24th 2008
People, papers, software etc
Jarad Niemi
Quanli Wang
www.stat.duke.edu/~mw
NSF-NIH
Duke (NCI) Systems Cancer Biology Center
Statistical Science
Lingchong You
Chee-Meng Tan
Bioengineering
NIH Duke (NIH) Systems Biology Center
INI Cambridge, June 24th 2008
Stochastic imputation of latent processes
INI Cambridge, June 24th 2008
Raw single cell data – snapshot images
Frame 11
Frame 17
Frame 26
10 mins between frames - technical limit of time resolution
INI Cambridge, June 24th 2008