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.

Intensive chemotherapy regimens are not feasible in many adults with mantle cell lymphoma (MCL). We sought to build upon our previous experience with a non-intensive regimen, modified R-hyperCVAD chemotherapy (rituximab, cyclophosphamide, vincristine, doxorubicin, dexamethasone) with maintenance rituximab (MR), by the incorporation of bortezomib (VcR-CVAD) and the extension of MR beyond 2 years. Patients with previously untreated MCL received VcR-CVAD chemotherapy every 21 days for 6 cycles. Patients achieving at least a partial response to induction chemotherapy received rituximab consolidation (375 mg/m2 × 4 weekly doses) and MR (375 mg/m2 every 12 weeks × 20 doses). The primary end points were overall and complete response (CR), and secondary endpoints were progression-free (PFS) and overall survival (OS). Thirty patients were enrolled, with a median age of 61 years. All patients had advanced stage disease, and 60% had medium/high MCL International Prognostic Index risk factors. A CR or unconfirmed CR was achieved in 77% of patients. After a median follow-up of 42 months, the 3-year PFS and OS were 63% and 86%, respectively. The observed 3-year PFS and OS with VcR-CVAD in MCL were comparable to reported outcomes with more intensive regimens. A cooperative group trial (E1405) is attempting to replicate these promising results.

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Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

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