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Long-range intercellular communication is essential for multicellular biological systems to regulate multiscale cell–cell interactions and maintain life. Growing evidence suggests that intercellular calcium waves (ICWs) act as a class of long-range signals that influence a broad spectrum of cellular functions and behaviors. Importantly, mechanical signals, ranging from single-molecule-scale to tissue-scale in vivo, can initiate and modulate ICWs in addition to relatively well-appreciated biochemical and bioelectrical signals. Despite these recent conceptual and experimental advances, the full nature of underpinning mechanotransduction mechanisms by which cells convert mechanical signals into ICW dynamics remains poorly understood. This review provides a systematic analysis of quantitative ICW dynamics around three main stages: initiation, propagation, and regeneration/relay. We highlight the landscape of upstream molecules and organelles that sense and respond to mechanical stimuli, including mechanosensitive membrane proteins and cytoskeletal machinery. We clarify the roles of downstream molecular networks that mediate signal release, spread, and amplification, including adenosine triphosphate (ATP) release, purinergic receptor activation, and gap junction (GJ) communication. Furthermore, we discuss the broad pathophysiological implications of ICWs, covering pathophysiological processes such as cancer metastasis, tissue repair, and developmental patterning. Finally, we summarize recent advances in optical imaging and artificial intelligence (AI)/machine learning (ML) technologies that reveal the precise spatial-temporal-functional dynamics of ICWs and ATP waves. By synthesizing these insights, we offer a comprehensive framework of ICW mechanobiology and propose new directions for mechano-therapeutic strategies in disease diagnosis, cancer immunotherapies, and drug discovery. 

Drug resistance is one of the fundamental challenges in modern medicine. Using combinations of drugs is an effective solution to counter drug resistance as is harder to develop resistance to multiple drugs simultaneously. Finding the correct dosage for each drug in the combination remains to be a challenging task. Testing all possible drug-drug combinations on various cell lines for different dosages in wet-lab experiments is infeasible since there are many combinations of drugs as well as their dosages yet the drugs and the cell lines are limited in availability and each wet-lab test is costly and time-consuming. Efficient and accurate in silico prediction methods are surely needed. Here we present a novel computational method, PartialFibers to address this challenge. Unlike existing prediction methods PartialFibers takes advantage of the distribution of the missing drug-drug interactions and effectively predicts the dosage of a drug in the combination. Our results on real datasets demonstrate that PartialFibers is more flexible, scalable, and achieves higher accuracy in less time than the state of the art algorithms.