(368af) Somesh Mishra: Cabbi, Darpa, University of Illinois Urbana-Champaign (UIUC), IL, Usapostdoc Research Associate at Agricultural and Biological Engineering (UIUC)

Much of current research is driven toward sustainability through precision fermentation for manufacturing food-analogous. The second wave of synthetic biology enables the faster manufacturing of sustainable products (food, proteins, and chemicals). The invented expression system is often conceptually viewed as optimum. However, before ascertaining its commercial potential, there is a need to identify KPPs for process optimization, cost reduction, and DSP . The factors affecting and are not limited to growth media, cell density control, temperature, and agitation. Usually, the cellular media applied during cell line establishment studies is loaded with various supplements, growth factors, and antibiotics, which are unsuitable for commercialization and forbidden for regulatory reasons. To avoid this media development studies for commercially viable titer and product yield is required. Likewise, identifying KPPs (pH, temperature, agitation, aeration, DO), and fermentation mode at the lab scale that could influence the product titer, yield, quality, purity, and stability, and analytics is crucial for scale-up, DSP, and economics. Looping lab and pre-commercial scale for testing, learning, improving, and validating (TLIV) is essential to achieve the desired outcome.

I, therefore, propose a rationalized, unified, multidimensional approach to meet the goal of commercially viable precision fermentation involving (i) growth media selection, decision on titer, yield, and productivity on inputs of economics; (ii) applying experiments based on statistical approaches to identify and optimize fermentation KPPs, in parallel development of DSP. The DSP information is applied to improve fermentation (feedback approach for better product quality at reduced cost); (iii) apply TLIV between lab and pre-commercial scale. All the results will be supported and interpreted utilizing hybrid modeling approaches.

Teaching Philosophy:

The philosophy of equal education through active learning inspires my teaching approach. Surely, traditional teaching approaches are a substantial part of any classroom. However, it is seen that by supplementing it with active learning magnifies learning outcomes for all students, in particularly, for women and underrepresented minorities.

To get insight into active learning and how to adapt and implement it to the course I teach, I attended and completed the foundation certificate program offered by The Center for Innovation in Teaching & Learning (CITL) at the University of Illinois at Urbana-Champaign (UIUC), IL, USA. Also, I engaged as a Teaching Assistant for two semesters in course ABE 488- Bioprocessing Biomass for Fuel and ABE 226-ABE principles: Bioprocessing at Agricultural and Biological Engineering at UIUC, IL, USA. This training I received has acquainted me with the broad spectrum of active learning strategies that are to be implemented in the classroom I teach. For instance, while introducing students to Superpro Designer and allotting them to group project, in dedicated office hours, I listened to students as a group and individual and discussed the process flow sheet, pros-cons of unit operations. This exercise makes it clear that some students have a better understanding of the subject material than others. This is due to the multidisciplinary and diverse nature of the class. However, this group project give a chance to more comfortable students to teach their fellow classmates. Due to this the students gain confidence and comfort to work on what they don’t know. I received good evaluation grades from the students.

Selected Publications:

VG Tran, S Mishra, SS Bhagwat et al., 2023. An end-to-end pipeline for succinic acid production at an industrially relevant scale using Issatchenkia orientalis. Nature Communications 14 (1), 6152

S Mishra, V Kumar, J Sarkar, AS Rathore. 2021. CFD-based mass transfer modeling of a single-use bioreactor for production of monoclonal antibody biotherapeutics. Chemical Engineering Journal 267, 118323

S Mishra, A S.Rathore, N Saxena, GThakur. 2022. Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends in Biotechnology