@inproceedings{kantner2019hybrid,

title = {Hybrid modeling of quantum light emitting diodes: Self-consistent coupling of drift-diffusion, Schrödinger-Poisson, and quantum master equations},

author = {Markus Kantner},

year  = {2019},

date = {2019-01-01},

booktitle = {Proc. Photonics West 2019 (Physics and Simulation of Optoelectronic Devices XXVII)},

pages = {109120U},

organization = {SPIE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nowicka_etal_2019,

title = {Designing Distributed Cell Classifier Circuits Using a Genetic Algorithm},

author = {Melania Nowicka and Heike Siebert},

editor = {Luca Bortolussi and Guido Sanguinetti},

isbn = {978-3-030-31304-3},

year  = {2019},

date = {2019-01-01},

booktitle = {Computational Methods in Systems Biology},

pages = {96–119},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Cell classifiers are decision-making synthetic circuits that allow in vivo cell-type classification. Their design is based on finding a relationship between differential expression of miRNAs and the cell condition. Such biological devices have shown potential to become a valuable tool in cancer treatment as a new type-specific cell targeting approach. So far, only single-circuit classifiers were designed in this context. However, reliable designs come with high complexity, making them difficult to assemble in the lab. Here, we apply so-called Distributed Classifiers (DC) consisting of simple single circuits, that decide collectively according to a threshold function. Such architecture potentially simplifies the assembly process and provides design flexibility. We present a genetic algorithm that allows the design and optimization of DCs. Breast cancer case studies show that DCs perform with high accuracy on real-world data. Optimized classifiers capture biologically relevant miRNAs that are cancer-type specific. The comparison to a single-circuit classifier design approach shows that DCs perform with significantly higher accuracy than individual circuits. The algorithm is implemented as an open source tool.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}