MOTIVATION: Understanding cell fate determination is crucial in developmental biology and regenerative medicine. Although theoretical frameworks such as epigenetic landscape and gene regulatory networks have been proposed for decades, traditional studies have often been limited by population-averaging and low-throughput techniques, which obscure the heterogeneity of individual cells and fail to provide a systematic view of cell fate control. Recent advances in single-cell technologies have provided unprecedented resolution, revealing the complexity of cell fate decisions and driving the need for more sophisticated computational methods. RESULTS: In this review, we first emphasize experimental advances, such as single-cell multi-omics, lineage tracing, and perturbation techniques, which produce novel data modalities and enable dynamic tracking of cell fate transitions. We then discuss the modeling paradigms for cell fate studies and further assess the role of emerging AI tools in perturbation modeling and discuss the potential of single-cell and spatial foundation models. Additionally, we highlight several case studies on predicting and manipulating cell fates, and discuss key challenges and future directions of the field. AVAILABILITY AND IMPLEMENTATION: This work generates no new software.

The rapid development of the Internet of Things (IoT) has brought profound changes in the cloud computing paradigm. One promising computing model in IoT-related applications is edge computing, which can decrease the request response time by deploying edge servers close to IoT devices. Two fundamental problems in edge computing include how to place a limited number of edge servers on the candidate locations (e.g., the Access Points) and how to configure the applications on each server. In this paper, we jointly study the edge server placement and the application configuration to minimize the weighted sum of the service cost and edge server opening cost. We propose a local-search based algorithm, named SPAC, to solve the problem efficiently with its approximation ratio analyzed. Extensive simulations on Google data traces demonstrate SPAC reduces the total cost by up to 60% compared with state-of-the-art methods, and outperforms the baselines consistently in different parameter settings.