Parameter estimation of stochastic biochemical reaction network models

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Dnr:

NAISS 2024/22-1142

Type:

NAISS Small Compute

Principal Investigator:

Federica Milinanni

Affiliation:

Kungliga Tekniska högskolan

Start Date:

2024-09-10

End Date:

2025-10-01

Primary Classification:

10203: Bioinformatics (Computational Biology) (applications to be 10610)

Webpage:

Allocation

Klemming at PDC: 500 GiB
Dardel at PDC: 10 x 1000 core-h/month

Abstract

We develop methodology for modelling dynamical biochemical reaction networks, e.g. for subcellular signalling pathways of nerve cells. The methodology includes parameter estimation, uncertainty quantification and sensitivity analysis (UQSA, see https://doi.org/10.48550/arXiv.2308.05527). The model that we are currently considering use stochastic simulation and require likelihood-free methods for uncertainty quantification.