By Carolin Loos
Carolin bathrooms introduces novel techniques for the research of single-cell information. either techniques can be utilized to review mobile heterogeneity and for this reason enhance a holistic realizing of organic methods. the 1st approach, ODE limited combination modeling, allows the identity of subpopulation buildings and assets of variability in single-cell image facts. the second one strategy estimates parameters of single-cell time-lapse facts utilizing approximate Bayesian computation and is ready to take advantage of the temporal cross-correlation of the knowledge in addition to lineage info.
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Extra resources for Analysis of Single-Cell Data : ODE Constrained Mixture Modeling and Approximate Bayesian Computation
1: Hypothesis testing for two experimental condition based on pooled data of three biological replicates. Both criteria, AIC (lower table) and BIC (upper table), select hypotheses H3 and H4. The last colums show the results for the model selection based on the single replicates R1, R2 and R3. ✓ indicates that the model is not rejected and ✗ that it has been rejected using AIC or BIC for ΔBIC or ΔAIC > 10. The maximum likelihood estimate is denoted by θ ML . 5: Fit for the optimal model MH4 based on pooled data of three biological replicates.
Intrinsic noise, arising due to the inherent stochasticity of the underlying biological processes, and extrinsic noise, which emerges, for example, from diﬀerences in parameters of the cells in a subpopulation, can now be taken into account. Therefore, not only differences between supopulations, but also heterogeneity of the individual subpopulations can be studied. In addition, measurement noise can now be treated separately from the variability of the subpopulations. Moreover, the evolution of the variability of a subpopulation can be predicted, as variances at time points for which no measurements exist can be simulated by the MEs.
We discussed diﬀerent approaches to solve the CME, ranging from exact solutions obtained with the SSA to approximations with MEs and showed the link to deterministic modeling by RREs. Moreover, this chapter contains an introduction to parameter inference, including parameter estimation, identiﬁability and uncertainty analysis, and model selection. We presented the approach of maximum likelihood estimation using multi-start local optimization, and deﬁned the posterior distribution that is used in a Bayesian context.
Analysis of Single-Cell Data : ODE Constrained Mixture Modeling and Approximate Bayesian Computation by Carolin Loos