Integration of biological and computational disciplines is critical for interpreting the expanding diversity and scale of data in modern biology and for addressing emerging questions concerning living systems and their environments. Studies of team science development and outcomes for integrative biology research centers remain limited (Bader et al., 2025; Dueck et al., 2021; Hall et al., 2018; Int. Brain Lab, 2026), noting a lack of longitudinal network data for individual centers and a need to compare alternative organizational designs. Federal funding enabled formation of a unique research institute intentionally structured to integrate computational and experimental biology labs for specific research and training purposes.

To examine the effectiveness of the Institute’s integrative framework in fostering persistent and successful interdisciplinary (ID) collaborations, members were tracked over four years at four annual time points. Data documented research productivity outcomes and the value of training-related activity contributed by and accruing to individuals and the institute. Three primary data categories included: A) Professional Development Activities, B) Relationship-Centered Activity, and C) Research Productivity.

To explain observed structural changes, we applied standard Social Network Analysis metrics to characterize network development and compare productivity over time. We used the STERGM method (Krivitsky & Handcock, 2014) to model longitudinal dynamics of formation, persistence, and strength of disciplinary (D) and interdisciplinary (ID) collaborations, examining effects of 1) career stage, and 2) leadership changes, including formation of a cross-systems postdoctoral team, on network development and resilience. Results include:

Network Dynamics: Whole-institute and research-focused network density increased across four periods relative to baseline. D collaborations generated foundational insights, while ID collaborations enabled broader objectives. Both grew steadily, with ID ties expanding at roughly twice the rate. Early-career members had fewer within-domain connections than mid- and senior-career members, but ID connectivity did not differ by career stage.

Research Productivity: Productivity rose in Y2–Y3, peaked at Y4, and declined at Y5. A post–Y3 shift from lab-based to integrative project-based seed funding likely promoted ID collaboration and increased productivity. Faculty and research staff showed no significant output differences, though staff productivity was higher at Y4 (not significant due to small sample size). Across faculty and staff, productivity did not differ significantly over time or by career stage, though mid-career researchers trended higher.

Network Resilience: Leadership restructuring (18–36 months), including expanded roles and a co-directorship model, broadened decision-making. A cross-systems team improved communication and integration. By the final period, the network was highly robust: removal of the two most central leaders did not reduce effectiveness due to emergent integrators and increased connectivity among early-career leaders and research staff.

Future Directions: Effectiveness in ID collaborations is not fully captured with traditional research metrics; ID competence includes not only technical expertise but also behavioral and social traits (e.g., openness and adaptability). Future work will map the ID Competency Framework developed within the Institute (Mukherjee et al., 2024) to network positions and assess relationships within SNA metrics including integration, brokerage, and cross-systems collaboration.