Biodesign plays a critical role in developing innovative solutions, such as carbon capturing with living materials that incorporate photosynthetic organisms. Team effectiveness is a crucial aspect of successful collaboration and has been extensively studied in various fields. However, research on team effectiveness in the emerging field of Biodesign remains limited. This study aims to fill this gap by examining the impact of transdisciplinary collaboration on team effectiveness and proposing strategies for its enhancement in the future.
In scientific teams, factors such as individual expertise, disciplinary composition, and social dynamics significantly influence team effectiveness. Issues related to team leadership, coordination, and communication have been identified as major contributors to problems in many industries.
This study focuses on exploring how the convergence of different scientific backgrounds and individual perspectives within transdisciplinary Biodesign teams influences team effectiveness. By gaining valuable insights into the dynamics at play, this research aims to lay the foundation for the development of a tool that can enhance team effectiveness across Biodesign projects.
To address the research objective, several sub-questions are proposed. The first sub-question explores themes such as team processes, social networks, hierarchy, disciplinarity, and participatory design that impact team effectiveness in scientific teams. A comprehensive literature study will shed light on this sub-question. The second sub-question investigates the specific themes that affect overall team effectiveness in the Biodesign field. An exploratory case study, involving six expert interviews, will provide insights into this sub-question. Finally, the third sub-question explores the potential of leveraging these identified themes to enhance the team effectiveness of a Biodesign research group. This sub-question will be answered by connecting the findings from the literature study to the data obtained from the case study.
In the final phase of this thesis, a tool called the Dual Feedback System (DFS) was developed with the purpose of enhancing team effectiveness in Biodesign and other academic teams. DFS comprises two components: the Satisfaction Survey and the Feedback Meeting. It involves BEP, MEP, or PhD students and their supervisors who complete the survey to provide feedback on different aspects of the ongoing project, including project satisfaction, teamwork, and supervision. The survey results serve as the foundation for the subsequent Feedback Meeting, which aims to facilitate open communication and bridge the existing hierarchical gap between participants, fostering a collaborative and constructive environment.
Overall, this thesis contributes to our understanding of team effectiveness in the Biodesign field and provides valuable insights in how collaborations can be improved and team effectiveness in transdisciplinary research teams can be enhanced. The findings serve as the basis for the development of DFS, designed to enhance team effectiveness in Biodesign and other academic teams. The implementation of this tool within teams at TU Delft will further amplify its impact, particularly in important areas such as sustainability.

The microscope is an essential tool for biologists. Since the late 16th century, it has given researchers a better understanding of cell processes and greatly advanced healthcare. In this century, Single molecule localization microscopy (SMLM) has revolutionized optical microscopy by breaking the optical diffraction limit. Sparsely activating emitters in a sample labeled with fluorophores, the object can be reconstructed by estimating their positions using the system point spread function (PSF). These localization algorithms are the state of the art
in optical imaging, using unbiased estimators to reach the theoretical minimum uncertainty, or Cramér-Rao lower bound (CRLB).
While SMLM works well when emitters are sparsely activated, overlap of the emitter images is inevitable for thick or densely labeled samples. When SMLM is used on such images, the estimates become biased and the algorithm cannot find the correct number of emitters. Most techniques also make a deterministic estimate and are incapable of representing the uncertainty of estimates for dense samples.
A three-dimensional, Bayesian multiple emitter fitting algorithm is constructed using reversible jump Markov chain Monte Carlo (RJMCMC). While following the structure of Bayesian multiple-emitter fitting (BAMF), novel RJMCMC moves are designed to sample the parameters. The algorithm also jumps through models, estimating the number of emitters. It asymptotically samples from the posterior, revealing uncertainties in three-dimensional imaging that other techniques are incapable of imaging.
The algorithm was tested with astigmatic and biplane imaging. It has proven capable of consistently finding the correct model when a prior on emitter intensity is used. When separating two emitters, posterior density reconstruction revealed non-Gaussian emitter position uncertainties. Upon further investigation, the posterior density was found to be multimodal, with
both modes representative of the data and indistinguishable in terms of likelihood. This shows the algorithm can quantify three-dimensional PSF degeneracy and can become a vital tool for researchers to analyze their imaging setup. We also expect it to be especially effective when combined with modulation-enhanced localization microscopy (meLM) techniques.