| Full text | |
| Author(s): |
Tovar, Carlos R. P.
;
Martins-, David C., Jr.
;
Rozante, Luiz C. S.
;
Araujo, Eloi
;
IEEE Comp Soc
Total Authors: 5
|
| Document type: | Journal article |
| Source: | 2022 IEEE 22ND INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE 2022); v. N/A, p. 6-pg., 2022-01-01. |
| Abstract | |
The processes of stability and synchronization in networks of interacting dynamical entities perform very relevant roles in many biological contexts, and specially in gene regulatory networks. Coupled Boolean Networks (CBN) present a wide spectrum of potential applications, mainly in Systems Biology. Despite its importance, there are relatively few studies focused on stability involving a specific class of models. Attractor fields of CBNs consist in a constrained class of globally stable states of the system, in which the dynamics of each interacting entity remains "locally confined" in the same local attractor. The main goal of this paper is to present a computationally efficient method that, given a CBN as input, identify all its attractor fields. Experimental results show that our method is capable of recovering all attractor fields in a feasible time (in the order of minutes or at most a couple of hours in a single desktop), even for CBNs containing several thousands of attractor fields, suggesting that the proposed method is suitable to capture the dynamics structure of large scale CBNs. (AU) | |
| FAPESP's process: | 18/18560-6 - Data integration to identify biological markers of neurodevelopmental disorders |
| Grantee: | Helena Paula Brentani |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/21934-5 - Network statistics: theory, methods, and applications |
| Grantee: | André Fujita |
| Support Opportunities: | Research Projects - Thematic Grants |