Computational analysis of viable parameter regions in models of synthetic biological systems
| Autor: | Miha Moškon, Miha Mraz, Nikolaj Zimic, Žiga Pušnik |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Mathematical optimization Environmental Engineering Computer science Biomedical Engineering Parameter space Repressilator 03 medical and health sciences 0302 clinical medicine Robustness (computer science) Sensitivity (control systems) AC-DC circuit Cluster analysis Viable parameter regions Robustness Computational analysis Molecular Biology lcsh:QH301-705.5 Network model Principal components Dimensionality reduction Methodology Cell Biology Function (mathematics) Genetic algorithms 030104 developmental biology lcsh:Biology (General) Biological model Biological D flip-flop 030217 neurology & neurosurgery |
| Zdroj: | Journal of Biological Engineering, Vol 13, Iss 1, Pp 1-21 (2019) Journal of Biological Engineering |
| ISSN: | 1754-1611 |
| DOI: | 10.1186/s13036-019-0205-0 |
| Popis: | BackgroundGene regulatory networks with different topological and/or dynamical properties might exhibit similar behavior. System that is less perceptive for the perturbations of its internal and external factors should be preferred. Methods for sensitivity and robustness assessment have already been developed and can be roughly divided into local and global approaches. Local methods focus only on the local area around nominal parameter values. This can be problematic when parameters exhibits the desired behavior over a large range of parameter perturbations or when parameter values are unknown. Global methods, on the other hand, investigate the whole space of parameter values and mostly rely on different sampling techniques. This can be computationally inefficient. To address these shortcomings ’glocal’ approaches were developed that apply global and local approaches in an effective and rigorous manner.ResultsHerein, we present a computational approach for ’glocal’ analysis of viable parameter regions in biological models. The methodology is based on the exploration of high-dimensional viable parameter spaces with global and local sampling, clustering and dimensionality reduction techniques. The proposed methodology allows us to efficiently investigate the viable parameter space regions, evaluate the regions which exhibit the largest robustness, and to gather new insights regarding the size and connectivity of the viable parameter regions. We evaluate the proposed methodology on three different synthetic gene regulatory network models, i.e. the repressilator model, the model of the AC-DC circuit and the model of the edge-triggered master-slave D flip-flop.ConclusionsThe proposed methodology provides a rigorous assessment of the shape and size of viable parameter regions based on (1) the mathematical description of the biological system of interest, (2) constraints that define feasible parameter regions and (3) cost function that defines the desired or observed behavior of the system. These insights can be used to assess the robustness of biological systems, even in the case when parameter values are unknown and more importantly, even when there are multiple poorly connected viable parameter regions in the solution space. Moreover, the methodology can be efficiently applied to the analysis of biological systems that exhibit multiple modes of the targeted behavior. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |