https://www.selleckchem.com/products/cathepsin-g-inhibitor-i.html Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.Triple-negative breast cancer (TNBC) has high rate of metastasis, which is associated with breast cancer stem-like cells (CSCs). Although Taxol (micelle formulation of paclitaxel) is the first line chemotherapy to treat TNBC, it increases CSCs in residual tumors. Abraxane, albumin nanoparticle of paclitaxel, showed lower plasma concentration compared to Taxol in both human and animal models, but it is not clear why Abraxane showed superior efficacy to Taxol in treatment of metastatic breast cancer in human. In this study, we intend to investigate if Abraxane decreases CSCs for its better efficacy. Th