Prof. Dr. Mircea Teodorescu and Kateryna Voitiuk 

MaxWell Webinar with Prof. Dr. Mircea Teodorescu and Kateryna Voitiuk

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Prof. Dr. Mircea Teodorescu
Teodorescu Lab, UC Santa Cruz, USA


Kateryna Voitiuk 
Teodorescu Lab, UC Santa Cruz, USA


Dr. Marie Obien
MaxWell Biosystems, Switzerland

This webinar will highlight:

A combination of different modalities for an automated and remotely controlled laboratory for organoid experiments

An integration of microscopy, fluidics, and HD-MEA systems to study functional neural networks in cerebral organoids

An optogenetic platform for in depth-analysis of single neurons responding to light


Open Source Hardware & Software Modules for Multimodal Electrophysiology Experiments


High-density recordings allow monitoring of neural network activity over space and time but require extensive manual labor during long-term experiments. We propose an automated, remote-controlled experimentation platform that provides consistent media feeding and fuses complementary information acquired through several sensing and stimulation modalities (using MaxWell HD-CMOS MEAs, imaging, and optogenetics). For reproducibility, we use off-the-shelf and open-source 3D printed components as well as our open-source ‘Braingeneers’ Python library.

Speaker Bio’s

Prof. Dr. Mircea Teodorescu | Mircea Teodorescu is an Associate Professor in Electrical and Computer Engineering. His lab focuses on research in mechanical, electrical and computer engineering with applications in bio-molecular engineering, biomechanics and assistive technology. His lab interfaces biology and engineering through the use of mathematical modeling, rapid manufacturing, embedded electronic systems, and sensing.

Kateryna Voitiuk | Kateryna Voitiuk is a Ph.D. candidate in Bioinformatics. She is curious about how the brain processes information from the bottom up. Kate engineers hardware, software, and microfluidic devices to study neural activity in 3D stem-cell-derived organoid and connectoid models. She is honored to be supported as an NIH NHGRI T-32 Trainee and ARCS Foundation Agilent Technologies Scholar.

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