Multi-agent-based bio-network for systems biology: protein–protein interaction network as an example

Distributed Broadcast Control of Multi-Agent Systems Using Hierarchical Coordination

Broadcast control (BC) is a bio-inspired coordination technique for a swarm of agents in which a single coordinator broadcasts an identical scalar signal to all performing agents without discrimination, and the agents make appropriate moves towards the agents' collective optimal state without communicating with one another. The BC technique aims to accomplish a globally assigned task for which BC utilizes a stochastic optimization algorithm to coordinate a group of agents. However, the challenge intensifies as the system becomes larger: it requires a larger number of agents, which protracts the converging time for a single coordinator-based BC model. This paper proposes a revamped version of BC model, which assimilates distributed multiple coordinators to control a larger multi-agent system efficiently in a pragmatic manner. Precisely, in this hierarchical BC scheme, the distributed multiple sub-coordinators broadcast the identical feedback signal to the agents, which they receive from the global coordinator to accomplish the coverage control task of the ordinary agents. The dual role of sub-coordinators is manipulated by introducing weighted averaging of the gradient estimation under the stochastic optimization mechanism. The potency of the proposed model is analyzed with numerical simulation for a coverage control task, and various performance aspects are compared with the typical BC schemes to demonstrate its practicability and performance improvement. Particularly, the proposed scheme shows the same convergence with about 30% less traveling costs, and the near convergence is reached by only about one-third of iteration steps compared to the typical BC.

Mean-square bounded consensus of nonlinear multi-agent systems under deception attack

This paper researches mean-square bounded consensus for a nonlinear multi-agent system subjected to randomly occurring deception attack, process and measurement noises. Considering the measurement tampered by the attacker, an estimator is presented to obtain relative accurate state estimation, where the gain is acquired by a recursive algorithm. On this basis, a type of centralized controller is designed combined with cloud computing system. Moreover, from perspective of the defender, a detector is proposed at the side of agent to detect whether the current actuator input is attacked. Using linear matrix inequality, sufficient conditions are given for achieving mean-square bounded consensus and an upper boundary is derived. Finally, validity of the proposed method is illustrated via two simulation examples.

Prediction and characterization of protein-protein interaction networks in swine

Background

Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.

Results

We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.

Conclusion

The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).

Internet resources for agent-based modelling

The use of agent-based models (ABMs) is steadily increasing in all the disciplines including environmental chemistry and toxicology. This growth is mainly driven by their ability to address problems that conventional modelling techniques cannot, such as the change of scale or the emergence of unanticipated phenomena resulting from interactions between their constitutive goal-directed agents. After a brief introduction on the basic principles of agent-based modelling and the presentation of selected case studies, the main software resources available on the Internet are presented. An attempt is made to estimate the complexity of these tools versus their potentialities and flexibility.