By Carolin Loos
Carolin bogs introduces novel techniques for the research of single-cell facts. either ways can be utilized to check mobile heterogeneity and accordingly improve a holistic realizing of organic approaches. the 1st strategy, ODE restricted blend modeling, allows the identity of subpopulation constructions and resources of variability in single-cell picture information. 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 information in addition to lineage info.
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Furthermore, as there are merely two assessments of ODE-MM, will provide additional evaluations on artiﬁcial as well as real experimental data. 2 Assessment of ODE-MMs Using Novel Data for NGF-Induced Erk Signaling Understanding intracellular signaling mechanisms that regulate pain sensitization is of great importance for pain research. Therefore, the underlying processes of NGF-induced Erk phosphorylation are studied. g. 1: Illustration of ODE constrained mixture modeling. The combination of mixture modeling of experimental data and pathway information allows us improved aquisition of subpopulation structures and mechanistics.
1 conc. 1 conc. 1 conc. 7: Artiﬁcial data for diﬀerent scenarios of a conversion process of A and B with two subpopulations that diﬀer in the response to a stimulus: Scenarios with (A) no parameter variability, (B) low parameter variability and (C) high parameter variability between individual cells. 3 Modeling Variability within a Subpopulation Moment Equations for The Conversion Process without Variability in Parameters The MEs for the conversion process are generated and simulated using the toolbox CERENA developed by Kazeroonian et al.
1. 2 Problem Statement Despite the successes achieved using ODE-MM, there are several open issues. So far, only the means of the subpopulations are described by ODEs and the variances are treated as additional parameters. If many time points and experimental conditions are observed, the unknown variances increase the dimension of the parameter space signiﬁcantly. As the predictive power of a model generally decreases with its complexity, it is desirable to reduce the number of its parameters. Moreover, RREs provide only a description of the averaged behavior of a subpopulation.