Arianna Miano, PhD

Principal Investigator | NIH NIA Grant Managed the scientific research, budget, and personnel for a two-year, $500K NIH NIA-funded project. Leading experimental planning, execution, and oversight to push forward the creation of engineered probiotic therapeutics for Alzheimer. Leading research collaboration on characterizing engineering non-model gut commensals with external partners.

Driving innovation in technology and platform development by integrating CRISPR-Cas9 genome editing, homologous recombination with selection and counter-selection markers, bioreactor-based gut microbiome simulation, and next-generation sequencing (Oxford Nanopore) to accelerate the development of microbiome-targeted therapeutics.

Selected to participate as a technology inventor to the first edition of the Nucleate Activator Program in San Diego.

- Designed and engineered an inducible quorum sensing genetic circuit to fine tune spatiotemporal control of drug release from bacterial carriers. Developed a custom microfluidic device to characterize the circuit dynamics under 10 different inducer concentrations simultaneously, reducing the experiments time and cost tenfold. Used Matlab to implement a deterministic ODE model which accurately predicted the circuit behavior over time as a function of the inducer concentration. This study resulted in a publication in the journal Nature Communications, an international patent and a blog post for the Nature - Biomedical Engineering community. - Co-lead a collaborative project which uncovered new mechanisms that drive coexistence and maintenance of biodiversity using an engineered bacterial ecology in combination with a 2D stochastic lattice model. Designed an innovative protocol to precisely array bacterial population in a grid format by repurposing an high-throughout liquid handling robot. This study was published in the journal Nature Communications and mentioned in more than 10 press releases. - Lead a collaborative project across two labs and three researchers to analyze bacterial transcriptomics data using unsupervised machine learning (ICA- Independent Component Analysis) techniques. The transcriptomics data was generated using an in-house technology consisting of a microfluidic device which can track simultaneously 4000+ unique bacterial strains with a time resolution of 10 minutes. A bioinformatic pipeline was implemented in python to analyze this unique dataset and lead to the discovery of previously unknown gene activation patterns linked to bacterial exposure to heavy-metal. -Lead a selection of lectures, planned and lead weekly discussion sessions, graded all tests and homework for the class Biomedical Signals and Systems with 40+ senior students.

Designed a novel microfluidic device to study live gene expression of bacterial populations which has been adopted by several post-doc and graduate students in the lab and contributed to a top-tier journal publication.

Lead a team of five analysts to consult the start-up Mindful Care in planning their market entry strategy. Planned project deliverables, guided team members and lead weekly presentations to update the client on key findings.

I worked as a consulting analyst for the digital pathology company KeenEye based in France. I conduced primary and secondary research to help the client get an understanding of the market size and needs, as well as investigating the competitive landscape.

Research project based on the realization of a computational whole cell model based on finite resources availability in Guy-Bart Stan lab.

Worked with a group of five students to create a protocol for data storage in the DNA of living cells.

Teaching assistant for Matlab based laboratory sessions in the course "Signals and Systems"

Ten-week project in collaboration with ULIS research group, supervised by Dr. Mengxing Tang. The project was based on the implementation of beamforming code for Ultrasound imaging with diverging waves in MEX, C++ and GPU CUDA languages.

Group project for the design and realization of a throwing frame and an electronic feedback for the paralympic athlete Ellie Simpsons.