Available online at www.sciencedirect.com Combinatorial engineering of microbes for optimizing cellular phenotype Christine Nicole S Santos and Gregory Stephanopoulos Although random mutagenesis and screening and evolutionary engineering have long been the gold standards for strain improvement in industry, the development of more sophisticated recombinant DNA tools has led to the introduction of alternate methods for engineering strain diversity. Here, we summarize several combinatorial cell optimization methods developed in recent years, many of which are more amenable to phenotypic transfer and more efficient in probing greater dimensions of the available phenotypic space. They include tools that enable the fine- tuning of pathway expression (synthetic promoter libraries, tunable intergenic regions (TIGRs)), methods for generating randomized knockout and overexpression libraries, and more global techniques (artificial transcription factor engineering, global transcription machinery engineering, ribosome engineering, and genome shuffling) for eliciting complex, multigenic cellular properties. Addresses Department of Chemical Engineering, Massachusetts Institute of Technology, Room 56-469, Cambridge, MA 02139, United States Corresponding author: Stephanopoulos, Gregory (gregstep@mit.edu) Current Opinion in Chemical Biology 2008, 12:168–176 This review comes from a themed issue on Biocatalysis and Biotransformation Edited by Stephen G. Withers and Lindsay Eltis Available online 29th February 2008 1367-5931/$ – see front matter # 2008 Elsevier Ltd. All rights reserved. DOI 10.1016/j.cbpa.2008.01.017 Introduction In the past 15 years, metabolic engineering has emerged as the discipline that utilizes modern genetic tools for the construction of organisms capable of fuel and chemical production. It was formalized partly from the advent of more sophisticated recombinant DNA techniques that allowed for the targeted genetic manipulation of microbes, either for the modification of existing bio- chemical reactions or the introduction of completely heterologous pathways. As such, the earliest examples in the field focus on engineering cellular phenotype using rational modifications (typically gene deletions/overex- pressions and pathway deregulation) based on existing stoichiometric, kinetic, and regulatory knowledge of a system [1,2]. Although this ‘rational design’ approach has been successful in many applications, it was established early on that the interconnectivity and sheer complexity of biological networks often preclude the recognition of simple genotype–phenotype relationships to guide these modifications. Indeed, a single genetic perturbation often has a variety of unpredictable secondary responses within the cell. In a similar vein, the performance of biosynthetic pathways frequently depends on distal genes through kinetic and regulatory interactions whose origins are poorly understood [3  ,4]. Finally, as an added compli- cation, engineering a complex phenotype may call for the simultaneous modulation of several of these potentially unknown factors [5]. Such challenges led to the development of a new concept called ‘inverse metabolic engineering’ (IME) for cell optimization. This methodology involves three main steps: (1) the construction or identification (by selection) of strains possessing a desired cellular phenotype, (2) the evaluation and determination of genetic and/or environ- mental factors that confer the phenotype, and (3) the transfer of this phenotype to another strain through direct modifications of the identified genetic and/or environ- mental factors [6]. Various ‘-omics’ approaches estab- lished in the past several years have greatly facilitated the analysis of identified strains and have been the subject of other excellent reviews [7,8]. Here, we focus on recently developed techniques for the generation of strains possessing a phenotype of interest, which, in many cases, remains a significant bottleneck of the IME approach. Owing to the difficulty of predicting these complex genotype–phenotype relationships, many of these methods are combinatorial in nature, that is, they are based on generating genetic (and hence, phenotypic) diversity in a population followed by screening and selec- tion for improved phenotypes. Fine-tuning expression levels of pathway components It is now broadly accepted that most metabolic pathways are not limited by a single rate-limiting step and that optimized pathways require the balanced expression of several enzymes [9,10]. Without such coordination, meta- bolic imbalance can lead to the accumulation of gene products or intermediate metabolites with potentially cytotoxic effects or, in some cases, may result in the depletion of a metabolite needed for normal cell growth. Furthermore, the overexpression of genes/proteins often results in an undue metabolic burden on the cell [11]. Thus, many recent metabolic engineering efforts have focused on the development of tools for fine-tuning Current Opinion in Chemical Biology 2008, 12:168–176 www.sciencedirect.com