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Statistical Inference

Determining model parameters, and possibly model structure, from experimental data is a vital tool in both interpreting experimental data and developing/testing understanding of the biological system. A variety of techniques are available to perform model inference (fitting), with Bayesian methods offering the most versatile and powerful framework through a series of computational statistics algorithms, primarily Markov chain Monte Carlo algorithms. There are a wide variety of different projects within SBIDER that use inference methods on a variety of data types. This includes:

Chromosome dynamics during cell division (Burroughs). Inferring mechanical parameters from 3+1D fluorescence imaging data.

Gene regulatory mechanisms and networks (Rand, Finkelstadt). Inferring gene transcription characteristics from flourescence data.

Regulatory transcription network inference.

Epidemiology (Spencer). Inferring key epidemiological parameters from infection data.

Molecular epidemiology including phyogeny analysis. Xavier Didelot.