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Assistant Professor of Biomedical Engineering
The Reker lab tightly integrates biomedical data science and wet-lab experiments for the analysis and design of therapeutic opportunities. Automated experimentation can be guided by active machine learning to generate knowledge-rich datasets. A key aspect of our research is improving our understanding of the most effective active machine learning workflows to enable the broad deployment of adaptive machine learning and automated experimentation.
We focus our adaptive model development on critical drug properties such as efficacy, biodistribution, metabolism, toxicity, and side-effects. Prospective applications of these predictions enable us to better understand limitations of currently approved medications as well as design new drug candidates, nanoparticles, and pharmaceutical formulations. By integrating clinical data analysis, we can rapidly validate the translational relevance of our predictions and conceive big data-driven protocols for precision medicine and personalized drug delivery.
Appointments and Affiliations
- Assistant Professor of Biomedical Engineering
- Member of the Duke Cancer Institute
- Sc.D. Swiss Federal Institute of Technology-ETH Zurich (Switzerland), 2016
Integration of active machine learning, biomedical data science, and biochemical experiments for the analysis and design of personalized therapeutic opportunities.
- BME 221L: Biomaterials
- BME 590L: Special Topics with Lab
- BME 791: Graduate Independent Study
- BME 792: Continuation of Graduate Independent Study
- Abramson, A; Kirtane, AR; Shi, Y; Zhong, G; Collins, JE; Tamang, S; Ishida, K; Hayward, A; Wainer, J; Rajesh, NU; Lu, X; Gao, Y; Karandikar, P; Tang, C; Lopes, A; Wahane, A; Reker, D; Frederiksen, MR; Jensen, B; Langer, R; Traverso, G, Oral mRNA delivery using capsule-mediated gastrointestinal tissue injections, Matter, vol 5 no. 3 (2022), pp. 975-987 [10.1016/j.matt.2021.12.022] [abs].
- Wollborn, J; Hassenzahl, LO; Reker, D; Staehle, HF; Omlor, AM; Baar, W; Kaufmann, KB; Ulbrich, F; Wunder, C; Utzolino, S; Buerkle, H; Kalbhenn, J; Heinrich, S; Goebel, U, Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection., Annals of Intensive Care, vol 11 no. 1 (2021) [10.1186/s13613-021-00965-8] [abs].
- Steiger, C; Phan, NV; Huang, H-W; Sun, H; Chu, JN; Reker, D; Gwynne, D; Collins, J; Tamang, S; McManus, R; Lopes, A; Hayward, A; Baron, RM; Kim, EY; Traverso, G, Dynamic Monitoring of Systemic Biomarkers with Gastric Sensors., Advanced Science (Weinheim, Baden Wurttemberg, Germany), vol 8 no. 24 (2021) [10.1002/advs.202102861] [abs].
- Lee, K; Yang, A; Lin, YC; Reker, D; Bernardes, GJL; Rodrigues, T, Combating small-molecule aggregation with machine learning, Cell Reports Physical Science, vol 2 no. 9 (2021) [10.1016/j.xcrp.2021.100573] [abs].
- Reker, D; Rybakova, Y; Kirtane, AR; Cao, R; Yang, JW; Navamajiti, N; Gardner, A; Zhang, RM; Esfandiary, T; L'Heureux, J; von Erlach, T; Smekalova, EM; Leboeuf, D; Hess, K; Lopes, A; Rogner, J; Collins, J; Tamang, SM; Ishida, K; Chamberlain, P; Yun, D; Lytton-Jean, A; Soule, CK; Cheah, JH; Hayward, AM; Langer, R; Traverso, G, Computationally guided high-throughput design of self-assembling drug nanoparticles., Nature Nanotechnology, vol 16 no. 6 (2021), pp. 725-733 [10.1038/s41565-021-00870-y] [abs].