Bio-ModelChecker: Using Bounded Constraint Satisfaction to Seamlessly Integrate Observed Behavior With Prior Knowledge of Biological Networks
| العنوان: | Bio-ModelChecker: Using Bounded Constraint Satisfaction to Seamlessly Integrate Observed Behavior With Prior Knowledge of Biological Networks |
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| المؤلفون: | Hooman Sedghamiz, Matthew Morris, Travis J. A Craddock, Darrell Whitley, Gordon Broderick |
| المصدر: | Frontiers in Bioengineering and Biotechnology Frontiers in Bioengineering and Biotechnology, Vol 7 (2019) |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | 0301 basic medicine, Model checking, Histology, Theoretical computer science, Optimization problem, multi-valued discrete logic, path robustness, Computer science, lcsh:Biotechnology, Biomedical Engineering, Bioengineering, data compliance, 02 engineering and technology, multi-objective, 03 medical and health sciences, regulatory networks, Robustness (computer science), lcsh:TP248.13-248.65, Network model, Original Research, constraint satisfaction, System identification, Bioengineering and Biotechnology, Constraint satisfaction, 021001 nanoscience & nanotechnology, 030104 developmental biology, Biological plausibility, transition efficiency, 0210 nano-technology, Biological network, Biotechnology |
| الوصف: | The in silico study and reverse engineering of regulatory networks has gained in recognition as an insightful tool for the qualitative study of biological mechanisms that underlie a broad range of complex illness. In the creation of reliable network models, the integration of prior mechanistic knowledge with experimentally observed behavior is hampered by the disparate nature and widespread sparsity of such measurements. The former challenges conventional regression-based parameter fitting while the latter leads to large sets of highly variable network models that are equally compliant with the data. In this paper, we propose a bounded Constraint Satisfaction (CS) based model checking framework for parameter set identification that readily accommodates partial records and the exponential complexity of this problem. We introduce specific criteria to describe the biological plausibility of competing multi-valued regulatory networks that satisfy all the constraints and formulate model identification as a multi-objective optimization problem. Optimization is directed at maximizing structural parsimony of the regulatory network by mitigating excessive control action selectivity while also favoring increased state transition efficiency and robustness of the network's dynamic response. The framework's scalability, computational time and validity is demonstrated on several well-established and well-studied biological networks. |
| تدمد: | 2296-4185 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebdc185c811bf18237ad7aa47ea4c68a https://pubmed.ncbi.nlm.nih.gov/30972331 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....ebdc185c811bf18237ad7aa47ea4c68a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22964185 |
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