PhD Proposal: Vikash Kumar

Vikash Kumar, PhD Student

Location: TRC 206 & WEBEX

Advisor: 

Dr. Govind Rao

TITLE

ABSTRACT

Shaking cell culture vessels, like shake flasks, constitute the cornerstone of bioprocessing research and development, with over a million shake flask cultures performed annually. Their simple yet effective design facilitates the parallel execution of multiple culture experiments, for a range of studies like strain screening, media optimization, hydrodynamics, transfer process studies, process optimization, and scale-up investigations. Remarkably, over 90% of culture studies in biotechnology laboratories rely on this shaking bioreactor. Despite their prevalence, these small-scale shaking vessels encounter inherent challenges, primarily stemming from oxygen limitations and carbon dioxide accumulation, which adversely affect culture outcomes. Furthermore, the absence of non-invasive sensors for critical process parameters (CPPs) such as dissolved oxygen (DO) and dissolved carbon dioxide (dCO₂) hampers the ability to perform real-time process monitoring during culture. The limited gas exchange and absence of process information contribute to inefficiencies in most cell culture studies. Addressing these limitations is crucial for enhancing the effectiveness of such studies.

A breathable shaking flask will be developed to address the limitations of contemporary shaking bioreactors. The breathable shake flask has permeable walls facilitating the exchange of O₂ and CO₂ through the walls, thus improving gas exchange. A set of non-invasive sensors for DO, dCO₂, and pH will be developed, exploiting the permeability characteristics. A DO and dCO₂ control algorithm will be developed using real time process data. The breathable flask and the sensors will be validated against the contemporary standard. The effect of improved gas transfer on cellular metabolomics will be studied by monitoring real-time ATP levels, glucose uptake rate, lactate, and acetate generation rate. Metabolomics will be performed to further elucidate the differences in the cells resulting from the culture environment. Hydrodynamic environments inside the bioreactor will be modeled to obtain shear and turbulence information experienced by the cells. Using suitable machine learning tools on real-time process monitoring data, a correlation between CPPs like DO, dCO₂, pH, cellular health, and final recombinant product quality will be established.

Agenda

• 12:55 pm: Meeting room will open
• 1:00 pm: 45-min presentation will be open to the public with Q&A.
• Followed by a closed session with the committee and PhD Student.