SHF: Small: Molecular Classifier Circuits for Disease Diagnostics

点击翻译按钮获取中文摘要

Changes in the levels of RNA and protein molecules are associated with a large number of human diseases. Monitoring such changes enables clinicians to perform diagnosis, evaluate therapeutic efficacy and predict disease recurrence. Sometimes, detection of just a single molecular marker can be indicative of a disease state, but more commonly it is necessary to interpret a combination of markers via complex algorithms to obtain a reliable diagnosis. In a traditional diagnostic workflow, markers of interest are first detected and quantitated using tools such as RNA sequencing or microarrays. A computer is then used to make a diagnosis, for example by comparing the measurement results to a previously established benchmark. Despite their widespread use in medical research, these methods remain cost-prohibitive for a large number of medical applications where recurrent monitoring or regular screenings are necessary. To overcome these limitations, this work introduces a novel type of diagnostic tool where the computation and diagnosis is performed by a "molecular computer", minimizing the need for complex instrumentation.This research is tightly integrated with an outreach program that has two main goals. The first goal is to develop an educational program dedicated to teaching the interdisciplinary skills that are necessary to be successful in molecular programming. A second and longer term goal is to increase the enrollment of women in engineering research. A key aim is to motivate students with backgrounds in electrical engineering and computer science to engage in molecular programming research by demonstrating that molecular systems can be "programmed" just as we program electronic systems. To achieve these goals the PI is participating in engineering outreach programs and systematically pushes research results into the classroom, both through specialized classes (e.g. synthetic biology) and by incorporating molecular programming modules in core electrical engineering and computer science classes.The goal of this proposal is to demonstrate that molecular computation could become practically useful for disease diagnosis. The proposed approach integrates computation in silico with computation in the test tube. The workflow begins with the training of a computational classifier --- a support vector machine (SVM) --- on publicly available gene expression data. Then, the in silico classifier is mapped onto a set of DNA strands and complexes that realize the same classifier at the molecular level, resulting in a novel kind of molecular computation architecture. Finally, the molecular classifier is tested on different types of molecular data. In preliminary work, PI has constructed a molecular SVM that can, in principle, be used to distinguish between bacterial and viral infections based on analysis of seven host transcripts. The goal of the current proposal is to optimize and automate classifier design and testing and to bring such technology closer to practical applications.