Essential Components for Collaborative Networks in Translational Research
Inclusive of characterizing the problem, defining its cause, clarifying a solution, and sharing that solution with wider society, the classical arc of translational research is often prolonged and dependent on financial and intellectual champions. The need for scalable translational research is acutely apparent, and creating an informatics tool fit for this work becomes absolutely necessary.
Historically, humans have solved complex problems such as those of translational research through the distribution of cognitive processes across environments and social groups. Modern technology can distribute such cognition to virtual social groups. Whether physical or virtual, social groups represent a structured, participatory, transparent, and communal space for the exchange of knowledge. Recognizing that virtual social groups are applicable not only for scaling translational research but also for creating a successfully collaborative work environment, the National Institutes of Health Undiagnosed Diseases Program (NIH UDP) developed the Undiagnosed Diseases Program Integrated Collaboration System (UDPICS).
UDPICS is founded on the idea that knowledge is shared among translational researchers and their various experiences and environments, rather than merely confined to one individual. For this reason, the supporting informatics infrastructure creates a culture of collaborative information sharing and interaction. Despite knowledge, skill, and cultural differences among different users, UDPICS bridges these differences through 1) clearly delineated social relationships, 2) shared foundational knowledge, 3) facile communication in a common nomenclature, 4) accountability and maintenance of quality standards, and 5) intuitive usability. These characteristics, which create a social network founded on trust, respect, and appreciation, enable distributed cognition for problem solving. By uniting globally dispersed experts into virtual collaborative networks under a common goal, UDPICS enables scalable translational research.
Echoing the attributes of a physical social group, UDPICS forms virtual social networks modeling traditional social hierarchy. Privileges conceptually similar to those defining relationships in other social groups are provided by permissions in UDPICS and determine access to data, functions, and requests. These are assigned according to the role or needs of the user; each successive role is associated with more permissions. These roles reflect those of social groups; namely, initiator-contributor, procedural technician, orienter, integrator, opinion giver and opinion seeker, and information giver and information seeker. This integration of defined social hierarchy within UDPICS promotes a transparent relationship among users that leads to scaling of virtual collaborative networks.
The centralization of information related to a patient within UDPICS provides a foundation that encourages discussion of perspectives and fosters creativity and innovation. To this end, the UDPICS ecosystem contains an inventory system for biospecimens, an inventory system for model organisms, an electronic laboratory notebook, a human phenotyping tool, and a next-generation sequencing analysis tool. Clinical research teams, laboratory researchers, and bioinformatics analysts alike use this system collaboratively to record, track, and manage patient-centric research data with standardized nomenclature and terms.
Virtual collaboration within UDPICS facilitates participatory modes of study and research. Furthermore, the organization of data and knowledge within UDPICS makes it an archive for knowledge and skills from past experiments as well as for established social networks of prior collaborators. This enables future problem solving through application of experience and nonproprietary physical and intellectual assets created in prior collaborative processes.
Integrating distinct social groups requires a common language for understanding, communication, and productivity. Clinicians and laboratory scientists speak different languages, particularly regarding disease phenotype; therefore, to facilitate communication, UDPICS includes tools for ontology and collaboration. These integrated systems describe human features and model organism features, while another integrated application then translates features recorded in ontologies to traits expected in other organisms, facilitating decisions on the best animal model to generate for a disease.
A social network with shared data and language achieves effective coherence and agency within an organization if there are robust communication tools. UDPICS has several communication functions, including chat, a to-do list of tasks with associated time frames, an Activities List customized to a user’s interests, and email notifications for urgent items.
Productive organizational cultures frequently have a clearly defined mission shared by participants who understand their roles and strive to achieve the goals of the organization. Within UDPICS, workflows are designed to achieve the goals of the NIH UDP and tasks are assigned according to a user’s role or responsibility. Additionally, given that open and negotiable infrastructure development reflecting user practices and perceptions is more successful at getting user adoption, UDPICS has multiple functions enabling facile evolution of interfaces and workflows to satisfy users’ needs.
In summary, achieving the enhanced level of collaboration required for scalable translational research is impossible if participants operate independently using separate systems to manage disparate repositories of information. As problem solving throughout the greater scientific community increasingly depends on partnerships, support of distributed cognition through virtual social networks becomes mandatory, and UDPICS is a solution.