Name
Abstract | ||
|---|---|---|
Modern methods for the inference of cellular networks from experimental data often express nondeterminism through an ensemble of candidate models. To discriminate among these candidates new experiments need to be carried out. Theoretically, the number of possible experiments is exponential in the number of possible perturbations. In praxis, experiments are expensive and there exist several limiting constraints. Limiting factors exist on the combinations of perturbations that are technically possible, which components can be measured, and on the number of affordable experiments. Further, not all experiments are equally well suited to discriminate model candidates. The goal of optimal experiment design is to determine those experiments that discriminate most of the candidates while minimizing the costs. We present an approach for experiment planning with interaction graph models and sign consistency methods. This new approach can be used in combination with methods for network inference and consistency checking. We applied our method to study the Erythropoietin signal transduction in human kidney cells HEK293. We first used simulated experiment data from an ODE model to demonstrate in silico that our experimental design results in the inference of the gold standard model. Finally, we used the approach to plan in vivo experiments that discriminate model candidates for the Erythropoietin signal transduction in this cell line. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCBB.2018.2812184 | IEEE/ACM transactions on computational biology and bioinformatics |
Keywords | Field | DocType |
Perturbation methods,Data models,Predictive models,Computational modeling,Inhibitors,Biological system modeling,Optimized production technology | Data modeling,Exponential function,Experimental data,Computer science,Inference,Artificial intelligence,Cellular network,Ode,Machine learning,Design of experiments,In silico | Journal |
Volume | Issue | ISSN |
16 | 3 | 1557-9964 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sven Thiele | 1 | 383 | 17.94 |
| Sandra Heise | 2 | 9 | 1.61 |
| Wiebke Hessenkemper | 3 | 0 | 0.34 |
| Hannes Bongartz | 4 | 0 | 0.34 |
| Melissa Fensky | 5 | 0 | 0.34 |
| Fred Schaper | 6 | 2 | 1.07 |
| Steffen Klamt | 7 | 915 | 77.99 |