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The field of systems metabolic engineering is rapidly expanding, thanks to impressive improvements in both ‘omics measurements and computational frameworks. This general systems‐level paradigm is enabling new applications beyond just chemical overproduction. This special issue—inspired in part by the Metabolic Engineering 12 conference held in Munich, Germany, in July 2018—showcases major advances and paradigms in the field. We are grateful that leading experts in the field contributed their expertise to make this issue most thought‐provoking, inspiring, and timely for the field. Over the past few months, this has enabled the collection of a series of authoritative review and research articles to highlight the advances and future of the field. A review by Youngjoon Lee et al. from Sang Y. Lee’s laboratory highlights how systems metabolic engineering can be used to solve the World’s needs with renewable bioplastics (https://doi. org/10.1002/biot.201800426). Specifically, this review covers the topic of polyhydroxyalkanoate production through a systems metabolic engineering approach and extends the coverage to include non‐natural polyesters. A contribution from Jens Nielsen’s laboratory covers advances in the engineering of an important model system—Saccharomyces cerevisiae (S. cerevisiae) for a myriad of products including fuels, chemicals, food ingredients, and pharmaceuticals (https://doi.org/10.1002/biot. 201800421). Systems‐based approaches for this host are discussed. Guo‐Qiang (George) Chen and co‐workers provide an overview on biotechnology concepts, which rely on low‐cost mixed substrates, reduced freshwater and energy consumption, and long‐lasting continuous processing (https://doi.org/10.1002/ biot.201800437). In particular, they discuss the potential of more contamination‐resistant extremophilic microbes. Julia Frunzke’s group reviews the emerging field of evolutionary engineering for Corynebacterium glutamicum (C. glutamicum), an industrial platform organism (https://doi.org/10.1002/biot.201800444). The authors summarize experimental strategies of evolutionary engineering of this important microbe and highlight the potential to improve growth or stress resistance, implement the utilization of alternative carbon sources, or improve small‐ molecule production. The laboratory of Pablo I. Nikel presents state‐of‐the‐art techniques to manipulate genetic variation in bacterial populations and to construct combinatorial libraries of strains, which can be screened and selected for cell factories displaying enhanced phenotypes (https://doi.org/10.1002/biot. 201800439). Several reviews deal with modeling and simulation aspects in the biotechnology area. A review by Pedro A. Saa et al. with the Eduardo Agosin laboratory provides an overview of constraint‐ based models for metabolic pathway prediction (https://doi. org/10.1002/biot.201800734). In particular, they find that despite the power of these rational approaches, their implementation is limited and they discuss challenges for the field. Hal S. Alper’s laboratory reviews the integration of machine learning with systems metabolic engineering (https://doi.org/ 10.1002/biot.201800734). The contribution illustrates data‐ driven modeling methods, which make use of large multiomics data sets and become increasingly valuable to metabolic strain design. Matthew W. Chang’s group covers the likewise rapidly developing field to use metaomics and metabolic modeling to decipher the metabolism of the human microbiota, a complex community of commensal, symbiotic, and pathogenic microbes that play a crucial role in maintaining the homeostasis of human health (https://doi.org/10.1002/biot.201800734). Moreover, this special issue has a series of innovative papers dedicated to applying systems‐level approaches for metabolic engineering. In a contribution led by Stephan Noack (https:// doi.org/10.1002/biot.201800428), the authors describe a scale‐ down, miniaturization model leading to the rational engineering of heterologous cutinase secretion in C. glutamicum. Their BioLector‐enabled approach enabled measurements of metabolites and enzyme activity leading to process models for rate, DOI: 10.1002/biot.201900312 Prof. H. S. Alper McKetta Department of Chemical Engineering The University of Texas at Austin Austin, TX 78713, USA