We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked.
This paper investigates the foundational differences and the impacts on the analysis when using models with discrete time and models with dense time.
Soft Agents: Exploring Soft Constraints to Model Robust Adaptive Distributed Cyber-Physical Agent Systems
We are interested in principles for designing and building open distributed systems consisting of multiple cyber-physical agents, specifically, where a coherent global view is unattainable and timely consensus is impossible. Such agents attempt to contribute to a system goal by making local decisions to sense and effect their environment based on local information. In this paper we propose a model, formalized in the Maude rewriting logic system, that allows experimenting with and reasoning about designs of such systems. Features of the model include communication via sharing of partially ordered knowledge, making explicit the physical state as well as the cyber perception of this state, and the use of a notion of soft constraints developed by Martin Wirsing and his team to specify agent behavior. The paper begins with a discussion of desiderata for such models and concludes with a small case study to illustrate the use of the modeling framework.
Inspired by a new programming paradigm based on partially ordered knowledge sharing model for loosely coupled distributed computing and its implementation in our cyber-application framework, this paper studies how to program an NCPS and exploit the capabilities provided by the underlying framework.
We have compiled software tools and databases that are typically used for target identification through in silico analyses. We have also identified enzyme- and broad-spectrum metabolite-based drug targets that have emerged through in silico systems microbiology.
We design and implement a “what-if” analysis methodology using formal methods. Our methodology analyzes the impact of failures and changes in heterogeneous networks on QoS of flows.