Costa, L. d. F. | bioRxiv Science

bioRxiv2022

Discovering Patterns in Bipartite Networks

Bipartite networks constitute an especially important type of network that can be widely applied to model and study several intricate systems in many scientific areas, being also theoretically related to several network models and concepts. However, given a bipartite network the identification of specific recurrent patterns of interest often represents a substantial challenge. In the present work, we apply the coincidence methodology for translating data into networks as a means for automatically identifying repeating interconnection patterns in given bipartite networks. The important issue of normalization of the links strength is also addressed. Though the method is illustrated with respect to foodweb networks, its application is general and encompasses many other subjects and areas.

ecology↗

bioRxiv2020

Accelerated Simulation of Large Reaction Systems Using a Constraint-Based Algorithm

Simulation of reaction systems has been employed along decades for a better understanding of such systems. However, the ever-growing gathering of biological data implied in larger and more complex models that are computationally challenging for current discrete-stochastic simulation methods. In this work, we propose a constraint-based algorithm to simulate such reaction systems, called the Constraint-Based Simulation Algorithm (CBSA). The main advantage of the proposed method is that it is intrinsically parallelizable, thus being able to be implemented in GPGPU architectures. We show through examples that our method can provide valid solutions when compared to the well-known Stochastic Simulation Algorithm (SSA). An analysis of computational efficiency showed that the CBSA tend to outperform other considered methods when dealing with a high number of molecules and reaction channels. Therefore, we believe that the proposed method constitutes an interesting alternative when simulating large chemical reaction systems.

systems biology↗