Designing genetic perturbation experiments for model selection under uncertainty
| Autor: | Eve Tasiudi, Hans-Michael Kaltenbach, Claude Lormeau, Jörg Stelling |
|---|---|
| Přispěvatelé: | Findeisen, Rolf, Hirche, Sandra, Janschek, Klaus, Mönnigmann, Martin |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science Model selection Gene networks 020208 electrical & electronic engineering Bayesian probability Posterior probability Systems biology Experimental design Uncertainty quantification 02 engineering and technology Function (mathematics) Network topology Identification (information) 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Algorithm Biological network Topology (chemistry) |
| Zdroj: | IFAC-PapersOnLine, 53 (2) 21th IFAC World Congress. Proceedings |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2020.12.242 |
| Popis: | Deterministic dynamic models play a crucial role in elucidating the function of biological networks. However, the underlying biological mechanisms are often only partially known, and different biological hypotheses on the unknown molecular mechanisms lead to multiple potential network topologies for the model. Limitations in generating comprehensive quantitative data often prevent identification of the correct model topology and additionally leave substantial uncertainty about a model's parameter values. Here, we introduce an experiment design method for model discrimination under parameter uncertainty. We focus on genetic perturbations, such as gene deletions, as our possible experimental interventions. We start from an initial dataset and a single model whose topology includes all different hypotheses. We obtain the set of models compatible with the initial dataset, their posterior probabilities, and the distribution of compatible parameter values using our previously published topological filtering approach. We then employ a fully Bayesian approach to identify the genetic perturbation that yields the maximal expected information gain in a subsequent experiment. This approach explicitly accounts for parameter uncertainty; it also naturally allows comparing an arbitrary number of candidate models simultaneously. In contrast to previous approaches, our intervention alters the topology of the dynamic system rather than selecting optimal inputs, observables, or time-points for measurements. We demonstrate its applicability with an in-silico study based on a published real-world biological example. IFAC-PapersOnLine, 53 (2) ISSN:2405-8963 21th IFAC World Congress. Proceedings |
| Databáze: | OpenAIRE |
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