Developing a Patient- and Family-Centered Research Agenda for Hospital Medicine: The Improving Hospital Outcomes through Patient Engagement (i-HOPE) Study.

Background: Patient, caregiver, and other stakeholder priorities have not been robustly incorporated into directing hospital-based research and improvement efforts.

Objective: To systematically engage stakeholders to identify important questions of adult hospitalized patients and to create a prioritized research agenda for improving the care of adult hospitalized patients.

Design: A collaborative approach to stakeholder engagement and research question prioritization.

An adaptive coarse graining method for signal transduction in three dimensions.

The spatio-temporal landscape of the plasma membrane regulates activation and signal transduction of membrane bound receptors by restricting their two-dimensional mobility and by inducing receptor clustering. This regulation also extends to complex formation between receptors and adaptor proteins, which are the intermediate signaling molecules involved in cellular signaling that relay the received cues from cell surface to cytoplasm and eventually to the nucleus. Although their investigation poses challenging technical difficulties, there is a crucial need to understand the impact of the receptor diffusivity, clustering, and spatial heterogeneity, and of receptor-adaptor protein complex formation on the cellular signal transduction patterns.

Controlling the response: predictive modeling of a highly central, pathogen-targeted core response module in macrophage activation.

We have investigated macrophage activation using computational analyses of a compendium of transcriptomic data covering responses to agonists of the TLR pathway, Salmonella infection, and manufactured amorphous silica nanoparticle exposure. We inferred regulatory relationship networks using this compendium and discovered that genes with high betweenness centrality, so-called bottlenecks, code for proteins targeted by pathogens. Furthermore, combining a novel set of bioinformatics tools, topological analysis with analysis of differentially expressed genes under the different stimuli, we identified a conserved core response module that is differentially expressed in response to all studied conditions.

Defining the players in higher-order networks: predictive modeling for reverse engineering functional influence networks.

Determining biological network dependencies that can help predict the behavior of a system given prior observations from high-throughput data is a very valuable but difficult task, especially in the light of the ever-increasing volume of experimental data. Such an endeavor can be greatly enhanced by considering regulatory influences on co-expressed groups of genes representing functional modules, thus constraining the number of parameters in the system. This allows development of network models that are predictive of system dynamics.