진학프로석·박사 채용 접수 플랫폼

Post-doc position available in the Institute of Physical Chemistry PAS within National Science Center (NSC) SONATA BIS 12 Project No. 2022/46/E/ST8/00284 “Przełomowa metoda biodruku wspomagana technikami mikroprzepływowymi oraz sztuczną inteligencją: na drodze do stworzenia atlasu modelowania in vitro tkanek ludzkich“/ Empowering microfluidic-assisted bioprinting with artificial intelligence tools: towards an atlas for modelling in vitro human biology.
Leader dr hab. Marco Costantini.

In vitro tissue models are regarded as a critical component of future biomedical research. Using them, researchers have demonstrated a high capacity in mimicking both tissue-specific complex phenomena and revealing the interplay between physiological tissue structures and functions over the last two decades. Current in vitro tissue models, on the other hand, are mostly created using low-throughput approaches based on prior experience, intuition, and trial-and-error methodology. Such constraints result in the creation of valuable in vitro models, albeit far from optimal. The goal of this project is to address these limitations by creating a datadriven, high-throughput workflow that includes an innovative microfluidic-assisted extrusion bioprinting (μ-eBP) system supported by advanced image analysis tools for capturing key information about in vitro neo-tissue formation dynamics. The core scientific and technical objectives of the MYO-PATH project are: i) the progress towards a deeper understanding and control of cellular dynamic processes involved in the manufacturing of artificial tissues – specifically of skeletal muscle tissue; ii) the establishment of blueprints to obtain the highest tissue functionality and scalability potential and prioritize the best cost-effective experimental conditions; iii) the generation of a developmental atlas of artificial muscle morphogenesis in vitro. We intend to achieve this goal by developing new tools - hardware and software - for all stages of μ-eBP. A multiplexed and iterative screening and optimization approach will be used to obtain blueprint information. We plan to develop microfluidic modules that will allow us to address critical, currently unmet challenges in eBP, such as i) cell sedimentation mitigation in printer cartridges and ii) bioink multiplexing. Cellular dynamics will be captured using a variety of techniques such as bright-field, confocal, and optical coherence tomography. Advanced image analysis tools, including machine learning (ML)-based tools, will be used to analyse the resulting datasets. Notably, we propose developing quasi-live and offline image analysis tools to reliably assess the best performing conditions based on two indexes: an order parameter and a myo-index.