Intelligent computation in cancer gene therapy.

In recent years, the use of gene therapy for the treatment of disease has gained substantial interest, both in academic research and in the biomedical industry. Initial experimentation in gene therapy has generated positive results, as well as questions regarding safety. However, lessons have been learned from these first investigations, among them a realization that such treatments require a method to fine-tune the expression of therapeutic genes in real-time. A logical solution to this problem arose through the field of synthetic biology in the form of synthetic gene circuits. Thus, the synthetic biology community today aims to create "smart cells" for a variety of gene therapy applications, in an attempt to precisely target malignant cells while avoiding harming healthy ones. To generate safer and more effective gene therapies, new approaches with emerging computational abilities are necessary. In this review, we present several computational approaches which allow demonstrating artificial intelligence in living cells. Specifically, we will focus on implementing artificial neural networks using synthetic gene regulatory networks for cancer therapy and discuss the state-of-the-art computational developments.

Stimulating Extracellular Vesicles Production from Engineered Tissues by Mechanical Forces.

Extracellular vesicles (EVs) have emerged as a promising strategy to promote tissue regeneration. However, overcoming the low EV production yield remains a big challenge in translating EV-based therapies to the clinical practice. Current EV production relies heavily on 2D cell culture, which is not only less physiologically relevant to cells but also requires substantial medium and space. In this study, we engineered tissues seeded with stem cells from dental pulp or adipose tissues, or skeletal muscle cells, and significantly enhanced the EV production yield by applying mechanical stimuli, including flow and stretching, in bioreactors. Further mechanistic investigation revealed that this process was mediated by yes-associated protein (YAP) mechanosensitivity. EVs from mechanically stimulated dental pulp stem cells on 3D scaffolds displayed superior capability in inducing axonal sprouting than the 2D counterparts. Our results demonstrate the promise of this strategy to boost EV production and optimize their functional performance toward clinical translation.