Frontiers in Advanced Biotechnology: Integrating Ai, Nanomedicine, and Genetic Engineering - Zabala, Alia Jaber Khayoun; Atiyah, Nour Khalid Mohammed; Hamid, Zainab Zahir Naji; Hassan, Iman Ibrahim Abdul; Mukhlef, Mohammed Hamoud Nayef (American Journal of Biology and Natural Sciences, 2025)
The advancing cell visualization and recording techniques are enabling new opportunities to study the relationship between cell types and the emergent dynamics of mesoscale cellular organization. While we are at the beginning of cellular imaging, the advent of new technologies raises the hopeful prospect of long-term and large-scale visualization of cellular activity in this new regime of activity. This talk will introduce how deep learning methods can analyze cellular morphometry to predict cellular dynamics in terms of intracellular calcium rhythms, extend this work to spatiotemporal dynamics of gene expression, and also summarize future directions to unite conventional summary statistics with deep learning and applied learning methods with physics-based modeling to fully translate emerging imaging techniques into understanding of cellular self-organization and dynamics. Engineering approaches to modify or control gene expression in mammalian cells are hot topics in synthetic biology. An important sub-topic is to design and implement artificial transcription factors in order to achieve intentionally programmed control on gene activity. Computer-aided design methods allow for a wider range of artificial transcription factor effects than before, including tunable activity, user-defined expression profiles, or complex operation. Next-generation artificial transcription factors with native binding domains or cell-permeable designs have advanced the applicability of artificial transcription factors beyond transient life. Next-generation orthogonal delivery modules including RNA, DNA, and protein carriers have advanced collaboratively on both the engineering aspect and research feasibility. Synthetic gene circuits designed as layered networks can achieve complicated dynamic activities that are consistent with theoretical expectations and thus provide a calibrated platform for testing synthetic biology modules. Integration of genetic and biochemical circuits under the same framework allows for deeper processing of intracellular signals when purely genetic circuits are insufficient. In the meantime, synthesis of mass spectrometry-compatible proteomics circuits, inner-cell-surface-detectable glycoengineering circuits, and output-sensing circuits points to a promising future of synthetic biology with lived design-intent understanding of production by-products.