Abstract
Despite remarkable advances in microbial biomanufacturing, progress continues to be driven predominantly by the optimization of individual process variables. Yet microbial production systems operate as integrated biological networks, in which interactions among cultivation factors often influence system behavior more profoundly than the individual variables themselves. Consequently, optimization alone may improve production performance without necessarily advancing our understanding of the biological relationships that govern system-level responses.
This presentation proposes a complementary perspective in which biological interactions are viewed not only as components of optimization models, but also as sources of transferable biological insight. Rather than simply identifying optimal cultivation conditions, interpreting interaction effects can reveal how microbial production systems behave as integrated biological networks whose responses cannot be fully explained by isolated cultivation variables.
To explore this perspective, interaction effects among FeSO₄, NaCl, and NaNO₃ on phycobiliprotein production in Arthrospira platensis were systematically investigated using a multivariable response surface framework. Rather than serving solely as an optimization tool, the experimental design was interpreted as a framework for revealing biologically informative interaction patterns. In this way, interaction effects were considered not merely as statistical descriptors, but as biological clues to system behavior under combined cultivation conditions.
The interaction-driven experimental framework revealed robust interaction patterns, with statistically significant interaction effects demonstrating that microbial production behavior could not be adequately explained by the independent effects of individual cultivation variables alone. Instead, interaction patterns exposed biological dependencies that emerged only when environmental factors were considered collectively, highlighting the inherently context-dependent nature of production optimization. Collectively, these findings suggest that interaction effects should be interpreted not merely as statistical descriptors within optimization models, but as biologically informative clues capable of generating transferable insight into system behavior beyond the identification of optimal cultivation conditions.
Although illustrated through phycobiliprotein production, the perspective presented here extends well beyond a single product or cultivation system. The broader value of biological interactions lies not merely in identifying optimal cultivation conditions, but in deepening our understanding of how microbial production systems behave under combined process conditions. By reframing interaction effects as sources of transferable biological insight rather than solely as optimization outcomes, this perspective invites us to rethink microbial biomanufacturing—not simply as a process of finding better conditions, but as a process of learning how biological systems behave.
Biography
Dr. Elnaz Kyavar is a plant biologist whose research focuses on microbial biomanufacturing, cyanobacterial physiology, and the development of sustainable production strategies for high-value biomolecules. She received her Ph.D. in Plant Biology from the Science and Research Branch, Islamic Azad University, Tehran, Iran. Her research integrates microbial physiology, cultivation optimization, and systems-level interpretation of biological responses to improve phycobiliprotein production and advance microbial cell factory development.
Her recent work emphasizes understanding biological interactions underlying microbial production systems rather than viewing optimization solely as a process engineering objective. She is particularly interested in translating complex biological phenomena into practical frameworks that support robust, scalable, and application-oriented biomanufacturing. Her research interests include microbial biotechnology, phycobiliproteins, cyanobacterial physiology, process optimization, systems biology, and biological interaction-driven bioprocess design.
Dr. Kyavar also serves as an international peer reviewer for several scientific journals. Through both her research and scientific presentations, she aims to bridge experimental evidence with conceptual understanding, promoting scientific storytelling as a means of making complex biological systems more understandable while maintaining scientific rigor.