Selective inhibition of KDAC isoforms while maintaining potency remains a challenge. Using the largazole macrocyclic depsipeptide structure as a starting point for developing new KDACIs with increased selectivity, a combination of four different simplified largazole analogue (SLA) scaffolds with diverse zinc-binding groups (for a total of 60 compounds) were designed, synthesized, and evaluated against class I KDACs 1, 3, and 8, and class II KDAC6. Experimental evidence as well as molecular docking poses converged to establish the cyclic tetrapeptides (CTPs) as the primary determinant of both potency and selectivity by influencing the correct alignment of the zinc-binding group in the KDAC active site, providing a further basis for developing new KDACIs of higher isoform selectivity and potency.

For more than the last 20 decades, multi-agent simulations have been highlighted to model mega-scale social or biological agents and to simulate their emergent collective behavior that may be difficult only with mathematical and macroscopic approaches. A successful key for simulating megascale agents is to speed up the execution with parallelization. Although many parallelization attempts have been made to multiagent simulations, most work has been done on shared-memory programming environments such as OpenMP, CUDA, and Global Array, or still has left several programming problems specific to distributed-memory systems, such as machine unawareness, ghost space management, and cross-processor agent management (including migration, propagation, and termination). To address these parallelization challenges, we have been developing MASS, a new parallel-computing library for multi-agent and spatial simulation over a cluster of computing nodes. MASS composes a user application of distributed arrays and multi-agents, each representing an individual simulation place or an active entity. All computation is enclosed in each array element or agent; all communication is scheduled as periodic data exchanges among those entities, using machine-independent identifiers; and agents migrate to a remote array element for rendezvousing with each other. This paper presents the programming model, implementation, and evaluation of the MASS library.

Integrating sensor networks in cloud computing gives new opportunities of using as many cloud-compute nodes as necessary to analyze real-time sensor data on the fly. However, most cloud services for parallelization such as OpenMP, MPI, and MapReduce are not always fitted to on-the-fly sensor-data analyses that are implemented as model-based entity-based, and multi-agent simulations. To address this semantic gap between analyzing algorithms and their actual implementations, we are developing MASS: a library for multi-agent spatial simulation that composes a user application of distributed array elements and multi-agents, each representing an individual simulation place or an active entity. All computation is enclosed in each of elements and agents that are automatically distributed over different computing nodes. Their communication is then scheduled as periodical data exchanges among those entities using their logical indices. We are currently implementing a multi-process and a multi-threaded version of the MASS library, both to be combined in a single version in the near future. This paper focuses on an implementation and preliminary performance of the multi-process version.