Name
Title | ||
|---|---|---|
Decomposing the Parameter Space of Biological Networks via a Numerical Discriminant Approach. |
Abstract | ||
|---|---|---|
Many systems in biology, physics and engineering can be described by systems of ordinary differential equation containing many parameters. When studying the dynamic behavior of these large, nonlinear systems, it is useful to identify and characterize the steady-state solutions as the model parameters vary, a technically challenging problem in a high-dimensional parameter landscape. Rather than simply determining the number and stability of steady-states at distinct points in parameter space, we decompose the parameter space into finitely many regions, the steady-state solutions being consistent within each distinct region. From a computational algebraic viewpoint, the boundary of these regions is contained in the discriminant locus. We develop global and local numerical algorithms for constructing the discriminant locus and classifying the parameter landscape. We showcase our numerical approaches by applying them to molecular and cell-network models. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-41258-6_9 | MC |
Field | DocType | Volume |
Applied mathematics,Mathematical optimization,Algebraic number,Nonlinear system,Ordinary differential equation,Discriminant,Biological network,Parameter space,Artificial intelligence,Machine learning,Mathematics | Conference | 1125 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heather A. Harrington | 1 | 3 | 1.83 |
| Dhagash Mehta | 2 | 15 | 8.26 |
| Helen M. Byrne | 3 | 81 | 14.74 |
| Jonathan D. Hauenstein | 4 | 269 | 37.65 |