Purification and characterization of heparan sulfate from human primary osteoblasts

Heparan sulfate (HS) is a linear, highly variable, highly sulfated glycosaminoglycan sugar whose biological activity largely depends on internal sulfated domains that mediate specific binding to an extensive range of proteins. In this study we employed anion exchange chromatography, molecular sieving and enzymatic cleavage on HS fractions purified from three compartments of cultured osteoblasts-soluble conditioned media, cell surface, and extracellular matrix (ECM). We demonstrate that the composition of HS chains purified from the different compartments is structurally non-identical by a number of parameters, and that these differences have significant ramifications for their ligand-binding properties. The HS chains purified of conditioned medium had twice the binding affinity for FGF2 when compared with either cell surface or ECM HS. In contrast, similar binding of BMP2 to the three types of HS was observed. These results suggest that different biological compartments of cultured cells have structurally and functionally distinct HS species that help to modulate the flow of HS-dependent factors between the ECM and the cell surface.

Resource allocation and scheduling of multiple composite web services in cloud computing using cooperative coevolution genetic algorithm

In cloud computing, resource allocation and scheduling of multiple composite web services is an important and challenging problem. This is especially so in a hybrid cloud where there may be some low-cost resources available from private clouds and some high-cost resources from public clouds. Meeting this challenge involves two classical computational problems: one is assigning resources to each of the tasks in the composite web services; the other is scheduling the allocated resources when each resource may be used by multiple tasks at different points of time. In addition, Quality-of-Service (QoS) issues, such as execution time and running costs, must be considered in the resource allocation and scheduling problem. Here we present a Cooperative Coevolutionary Genetic Algorithm (CCGA) to solve the deadline-constrained resource allocation and scheduling problem for multiple composite web services. Experimental results show that our CCGA is both efficient and scalable.