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Automatic relevance determination for hyperparameter inference of chemical reaction rates


Wadehn, Frederico

Semester Thesis, FS13 (10240)

One main task in stochastic analysis of bio-chemical reaction networks is to infer the reaction rates from noisy and incomplete measurements. Advanced measurement techniques like fluorescence microscopy allow single cell measurements of specific molecules and hence reveal both intrinsic as well as extrinsic contributions to the populationís heterogeneity. Intrinsic noise arises due to the stochastic nature of chemical reactions happening within a single cell. On the other hand the variability in the micro-environment -commonly referred to as extrinsic noise, modeled by heterogeneous reaction rates across cell populations- also contributes to the totally observed variability. The main goal of my semester thesis was to develop empirical Bayesian algorithms to infer if a reaction rate was heterogenous or homogeneous across an isogenic cell population. We developed and tested several inference schemes based on the expectation-maximization algorithm and on the automatic relevance determination. In future, these algorithms which were tested with synthetic data, will be applied to experimental single-cell measurements in order to determine which bio-chemical reactions are affected by extrinsic factors.


Type of Publication:

(13)Semester/Bachelor Thesis

C. Zechner

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% Autogenerated BibTeX entry
@PhdThesis { WadFre:2013:IFA_4506
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