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Stochastic Dynamic Cellular Networks in Systems Biology

Explore stochastic models and computational methods for analyzing dynamic cellular networks in systems biology, focusing on single-cell studies, gene expression, and synthetic gene circuits. Incorporate stochastic components for accurate predictions and control of cellular behavior. Utilize Bayesian analysis, Markov chain Monte Carlo methods, and mixture modeling for integrating time-course data and imputing latent processes. Experiment with single-cell imaging and cell lineage reconstruction in various organisms. Access open-source software and collaborate with experts in the field.

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Stochastic Dynamic Cellular Networks in Systems Biology

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  1. Data, models & computation for stochastic dynamic cellular networks in systems biology Mike West Department of Statistical Science Duke University

  2. 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

  3. Synthetic bacterial gene circuits “emulate” gene networks key to mammalian cell proliferation (and cancer) c.f. Studies on mammalian cells Mammalian Rb/E2f pathway: Feed-Forward Positive Feedback Mammalian cell development & fate network (Cancer Systems Biology) • Stochastic models: States=RNA levels over time • Data - movies: multiple genes over time • Fit, assess, refine models: • evaluate cell-specific stochasticity • multiple cancer cell lines • predict network responses to interventions

  4. T7 Partial data over time on elements of yt Synthetic circuit

  5. 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

  6. yt yt+k Latent process xt+1 xt+k xt+k-1 xt t+1 t+k t Filtering: Sampling: Stochastic imputation of latent processes HMM: Forward filtering backward sampling (FFBS) Latent “missing” states imputed

  7. mixture Mixture modelling Metropolis MCMC

  8. mixture Mixture modelling Metropolis MCMC

  9. Imputed trajectories + data Posterior for parameters Information content: prior posterior

  10. Data extraction: single cell dynamic imaging Novel hybrid-image-based segmentation algorithms & neighborhood-based cell tracking Open source software Cell lineage reconstruction • E-coli • Budding yeast • Mammalian cells

  11. People, papers, software etc Jarad Niemi Quanli Wang Statistical Science www.stat.duke.edu/~mw Lingchong You Chee-Meng Tan Bioengineering NSF-NIH Duke (NCI) Systems Cancer Biology Center NIH Duke (NIH) Systems Biology Center

  12. Stochastic imputation of latent processes

  13. Raw single cell data – snapshot images Frame 26 Frame 11 Frame 17 10 mins between frames - technical limit of time resolution

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