Engineering transcription factors with novel DNA-binding specificity using comparative genomics

Comparative Analysis of the IclR-Family of Bacterial Transcription Factors and Their DNA-Binding Motifs: Structure, Positioning, Co-Evolution, Regulon Content

The IclR-family is a large group of transcription factors (TFs) regulating various biological processes in diverse bacteria. Using comparative genomics techniques, we have identified binding motifs of IclR-family TFs, reconstructed regulons and analyzed their content, finding co-occurrences between the regulated COGs (clusters of orthologous genes), useful for future functional characterizations of TFs and their regulated genes. We describe two main types of IclR-family motifs, similar in sequence but different in the arrangement of the half-sites (boxes), with GKTYCRYW_3-4RYGRAMC and TGRAACAN_1-2TGTTYCA consensuses, and also predict that TFs in 32 orthologous groups have binding sites comprised of three boxes with alternating direction, which implies two possible alternative modes of dimerization of TFs. We identified trends in site positioning relative to the translational gene start, and show that TFs in 94 orthologous groups bind tandem sites with 18-22 nucleotides between their centers. We predict protein-DNA contacts via the correlation analysis of nucleotides in binding sites and amino acids of the DNA-binding domain of TFs, and show that the majority of interacting positions and predicted contacts are similar for both types of motifs and conform well both to available experimental data and to general protein-DNA interaction trends.

Prokaryotic sigma factors and their transcriptional counterparts in Archaea and Eukarya

RNA polymerases (RNAPs) carry out transcription in the three domains of life, Bacteria, Archaea, and Eukarya. Transcription initiation is highly regulated by a variety of transcription factors, whose number and subunit complexity increase during evolution. This process is regulated in Bacteria by the σ factor, while the three eukaryotic RNAPs require a complex set of transcription factors (TFs) and a TATA-binding protein (TBP). The archaeal transcription system appears to be an ancestral version of the eukaryotic RNAPII, requiring transcription factor B (TFB), TBP, and transcription factor E (TFE). The function of the bacterial sigma (σ) factor has been correlated to the roles played by the eukaryotic RNAP II and the archaeal RNAP. In addition, σ factors, TFB, and TFIIB all contain multiple DNA binding helix-turn-helix (HTH) structural motifs; although TFIIB and TFB display two HTH domains, while the bacterial σ factor spans 4 HTH motifs. The sequence similarities and structure alignments of the bacterial σ factor, eukaryotic TFIIB, and archaeal TFB evidence that these three proteins are homologs.Key Points• Transcription initiation is highly regulated by TFs.• Transcription is finely regulated in all domains of life by different sets of TFs.• Specific TFs in Bacteria, Eukarya and Archaea are homologs.

Scaling up genetic circuit design for cellular computing: advances and prospects

Synthetic biology aims to engineer and redesign biological systems for useful real-world applications in biomanufacturing, biosensing and biotherapy following a typical design-build-test cycle. Inspired from computer science and electronics, synthetic gene circuits have been designed to exhibit control over the flow of information in biological systems. Two types are Boolean logic inspired TRUE or FALSE digital logic and graded analog computation. Key principles for gene circuit engineering include modularity, orthogonality, predictability and reliability. Initial circuits in the field were small and hampered by a lack of modular and orthogonal components, however in recent years the library of available parts has increased vastly. New tools for high throughput DNA assembly and characterization have been developed enabling rapid prototyping, systematic in situ characterization, as well as automated design and assembly of circuits. Recently implemented computing paradigms in circuit memory and distributed computing using cell consortia will also be discussed. Finally, we will examine existing challenges in building predictable large-scale circuits including modularity, context dependency and metabolic burden as well as tools and methods used to resolve them. These new trends and techniques have the potential to accelerate design of larger gene circuits and result in an increase in our basic understanding of circuit and host behaviour.

Tailor-made transcriptional biosensors for optimizing microbial cell factories

Monitoring cellular behavior and eventually properly adapting cellular processes is key to handle the enormous complexity of today's metabolic engineering questions. Hence, transcriptional biosensors bear the potential to augment and accelerate current metabolic engineering strategies, catalyzing vital advances in industrial biotechnology. The development of such transcriptional biosensors typically starts with exploring nature's richness. Hence, in a first part, the transcriptional biosensor architecture and the various modi operandi are briefly discussed, as well as experimental and computational methods and relevant ontologies to search for natural transcription factors and their corresponding binding sites. In the second part of this review, various engineering approaches are reviewed to tune the main characteristics of these (natural) transcriptional biosensors, i.e., the response curve and ligand specificity, in view of specific industrial biotechnology applications, which is illustrated using success stories of transcriptional biosensor engineering.

Identification of Position-Specific Correlations between DNA-Binding Domains and Their Binding Sites. Application to the MerR Family of Transcription Factors

The large and increasing volume of genomic data analyzed by comparative methods provides information about transcription factors and their binding sites that, in turn, enables statistical analysis of correlations between factors and sites, uncovering mechanisms and evolution of specific protein-DNA recognition. Here we present an online tool, Prot-DNA-Korr, designed to identify and analyze crucial protein-DNA pairs of positions in a family of transcription factors. Correlations are identified by analysis of mutual information between columns of protein and DNA alignments. The algorithm reduces the effects of common phylogenetic history and of abundance of closely related proteins and binding sites. We apply it to five closely related subfamilies of the MerR family of bacterial transcription factors that regulate heavy metal resistance systems. We validate the approach using known 3D structures of MerR-family proteins in complexes with their cognate DNA binding sites and demonstrate that a significant fraction of correlated positions indeed form specific side-chain-to-base contacts. The joint distribution of amino acids and nucleotides hence may be used to predict changes of specificity for point mutations in transcription factors.

Engineering Transcriptional Regulator Effector Specificity Using Computational Design and In Vitro Rapid Prototyping: Developing a Vanillin Sensor

The pursuit of circuits and metabolic pathways of increasing complexity and robustness in synthetic biology will require engineering new regulatory tools. Feedback control based on relevant molecules, including toxic intermediates and environmental signals, would enable genetic circuits to react appropriately to changing conditions. In this work, variants of qacR, a tetR family repressor, were generated by computational protein design and screened in a cell-free transcription-translation (TX-TL) system for responsiveness to a new targeted effector. The modified repressors target vanillin, a growth-inhibiting small molecule found in lignocellulosic hydrolysates and other industrial processes. Promising candidates from the in vitro screen were further characterized in vitro and in vivo in a gene circuit. The screen yielded two qacR mutants that respond to vanillin both in vitro and in vivo. While the mutants exhibit some toxicity to cells, presumably due to off-target effects, they are prime starting points for directed evolution toward vanillin sensors with the specifications required for use in a dynamic control loop. We believe this process, a combination of the generation of variants coupled with in vitro screening, can serve as a framework for designing new sensors for other target compounds.

GntR Family of Bacterial Transcription Factors and Their DNA Binding Motifs: Structure, Positioning and Co-Evolution

The GntR family of transcription factors (TFs) is a large group of proteins present in diverse bacteria and regulating various biological processes. Here we use the comparative genomics approach to reconstruct regulons and identify binding motifs of regulators from three subfamilies of the GntR family, FadR, HutC, and YtrA. Using these data, we attempt to predict DNA-protein contacts by analyzing correlations between binding motifs in DNA and amino acid sequences of TFs. We identify pairs of positions with high correlation between amino acids and nucleotides for FadR, HutC, and YtrA subfamilies and show that the most predicted DNA-protein interactions are quite similar in all subfamilies and conform well to the experimentally identified contacts formed by FadR from E. coli and AraR from B. subtilis. The most frequent predicted contacts in the analyzed subfamilies are Arg-G, Asn-A, Asp-C. We also analyze the divergon structure and preferred site positions relative to regulated genes in the FadR and HutC subfamilies. A single site in a divergon usually regulates both operons and is approximately in the middle of the intergenic area. Double sites are either involved in the co-operative regulation of both operons and then are in the center of the intergenic area, or each site in the pair independently regulates its own operon and tends to be near it. We also identify additional candidate TF-binding boxes near palindromic binding sites of TFs from the FadR, HutC, and YtrA subfamilies, which may play role in the binding of additional TF-subunits.

Improved production of a heterologous amylase in Saccharomyces cerevisiae by inverse metabolic engineering

The increasing demand for industrial enzymes and biopharmaceutical proteins relies on robust production hosts with high protein yield and productivity. Being one of the best-studied model organisms and capable of performing posttranslational modifications, the yeast Saccharomyces cerevisiae is widely used as a cell factory for recombinant protein production. However, many recombinant proteins are produced at only 1% (or less) of the theoretical capacity due to the complexity of the secretory pathway, which has not been fully exploited. In this study, we applied the concept of inverse metabolic engineering to identify novel targets for improving protein secretion. Screening that combined UV-random mutagenesis and selection for growth on starch was performed to find mutant strains producing heterologous amylase 5-fold above the level produced by the reference strain. Genomic mutations that could be associated with higher amylase secretion were identified through whole-genome sequencing. Several single-point mutations, including an S196I point mutation in the VTA1 gene coding for a protein involved in vacuolar sorting, were evaluated by introducing these to the starting strain. By applying this modification alone, the amylase secretion could be improved by 35%. As a complement to the identification of genomic variants, transcriptome analysis was also performed in order to understand on a global level the transcriptional changes associated with the improved amylase production caused by UV mutagenesis.

Comparative genomics and evolution of regulons of the LacI-family transcription factors

DNA-binding transcription factors (TFs) are essential components of transcriptional regulatory networks in bacteria. LacI-family TFs (LacI-TFs) are broadly distributed among certain lineages of bacteria. The majority of characterized LacI-TFs sense sugar effectors and regulate carbohydrate utilization genes. The comparative genomics approaches enable in silico identification of TF-binding sites and regulon reconstruction. To study the function and evolution of LacI-TFs, we performed genomics-based reconstruction and comparative analysis of their regulons. For over 1300 LacI-TFs from over 270 bacterial genomes, we predicted their cognate DNA-binding motifs and identified target genes. Using the genome context and metabolic subsystem analyses of reconstructed regulons, we tentatively assigned functional roles and predicted candidate effectors for 78 and 67% of the analyzed LacI-TFs, respectively. Nearly 90% of the studied LacI-TFs are local regulators of sugar utilization pathways, whereas the remaining 125 global regulators control large and diverse sets of metabolic genes. The global LacI-TFs include the previously known regulators CcpA in Firmicutes, FruR in Enterobacteria, and PurR in Gammaproteobacteria, as well as the three novel regulators—GluR, GapR, and PckR—that are predicted to control the central carbohydrate metabolism in three lineages of Alphaproteobacteria. Phylogenetic analysis of regulators combined with the reconstructed regulons provides a model of evolutionary diversification of the LacI protein family. The obtained genomic collection of in silico reconstructed LacI-TF regulons in bacteria is available in the RegPrecise database (http://regprecise.lbl.gov). It provides a framework for future structural and functional classification of the LacI protein family and identification of molecular determinants of the DNA and ligand specificity. The inferred regulons can be also used for functional gene annotation and reconstruction of sugar catabolic networks in diverse bacterial lineages.

Enhancing crop resilience to combined abiotic and biotic stress through the dissection of physiological and molecular crosstalk

Plants growing in their natural habitats are often challenged simultaneously by multiple stress factors, both abiotic and biotic. Research has so far been limited to responses to individual stresses, and understanding of adaptation to combinatorial stress is limited, but indicative of non-additive interactions. Omics data analysis and functional characterization of individual genes has revealed a convergence of signaling pathways for abiotic and biotic stress adaptation. Taking into account that most data originate from imposition of individual stress factors, this review summarizes these findings in a physiological context, following the pathogenesis timeline and highlighting potential differential interactions occurring between abiotic and biotic stress signaling across the different cellular compartments and at the whole plant level. Potential effects of abiotic stress on resistance components such as extracellular receptor proteins, R-genes and systemic acquired resistance will be elaborated, as well as crosstalk at the levels of hormone, reactive oxygen species, and redox signaling. Breeding targets and strategies are proposed focusing on either manipulation and deployment of individual common regulators such as transcription factors or pyramiding of non- (negatively) interacting components such as R-genes with abiotic stress resistance genes. We propose that dissection of broad spectrum stress tolerance conferred by priming chemicals may provide an insight on stress cross regulation and additional candidate genes for improving crop performance under combined stress. Validation of the proposed strategies in lab and field experiments is a first step toward the goal of achieving tolerance to combinatorial stress in crops.

Evaluation of novel design strategies for developing zinc finger nucleases tools for treating human diseases

Zinc finger nucleases (ZFNs) are associated with cell death and apoptosis by binding at countless undesired locations. This cytotoxicity is associated with the binding ability of engineered zinc finger domains to bind dissimilar DNA sequences with high affinity. In general, binding preferences of transcription factors are associated with significant degenerated diversity and complexity which convolutes the design and engineering of precise DNA binding domains. Evolutionary success of natural zinc finger proteins, however, evinces that nature created specific evolutionary traits and strategies, such as modularity and rank-specific recognition to cope with binding complexity that are critical for creating clinical viable tools to precisely modify the human genome. Our findings indicate preservation of general modularity and significant alteration of the rank-specific binding preferences of the three-finger binding domain of transcription factor SP1 when exchanging amino acids in the 2nd finger.

From measurement to implementation of metabolic fluxes

The intracellular reaction rates (fluxes) are the ultimate outcome of the activities of the complete inventory (from DNA to metabolite) and in their sum determine the cellular phenotype. The genotype-phenotype relationship is fundamental in such different fields as cancer research and biotechnology. Here, we summarize the developments in determining metabolic fluxes, inferring major pathways from the DNA-sequence, estimating optimal flux distributions, and how these flux distributions can be achieved in vivo. The technical advances to intervene with the many levels of the cellular architecture allow the implementation of new strategies in for example Metabolic Engineering.

Cellular hyperproliferation and cancer as evolutionary variables

Technological advances in biology have begun to dramatically change the way we think about evolution, development, health and disease. The ability to sequence the genomes of many individuals within a population, and across multiple species, has opened the door to the possibility of answering some long-standing and perplexing questions about our own genetic heritage. One such question revolves around the nature of cellular hyperproliferation. This cellular behavior is used to effect wound healing in most animals, as well as, in some animals, the regeneration of lost body parts. Yet at the same time, cellular hyperproliferation is the fundamental pathological condition responsible for cancers in humans. Here, I will discuss why microevolution, macroevolution and developmental biology all have to be taken into consideration when interpreting studies of both normal and malignant hyperproliferation. I will also illustrate how a synthesis of evolutionary sciences and developmental biology through the study of diverse model organisms can inform our understanding of both health and disease.

Regulatory role of cAMP receptor protein over Escherichia coli fumarase genes

Escherichia coli expresses three fumarase genes, namely, fumA, fumB, and fumC. In the present study, catabolite repression was observed in the fumA-lacZ and fumC-lacZ fusion strains, but not in the fumB-lacZ fusion strain. The Crp-binding sites in fumA and fumC were identified using an electrophoretic mobility shift assay and footprint analysis. However, the electrophoretic mobility shift assay did not detect band shifts in fumB. Fnr and ArcA serve as transcription regulators of fumarase gene expression. In relation to this, different mutants, including Δcya, Δcrp, Δfnr, and ΔarcA, were used to explore the regulatory role of Crp over fumA and fumC. The results show that Crp is an activator of fumA and fumC gene expression under various oxygen conditions and growth rates. ArcA was identified as the dominant repressor, with the major repression occurring at 0-4% oxygen. In addition, Fnr was confirmed as a repressor of fumC for the first time. This study elucidates the effects of Crp on fumarase gene expression.

Modular control of multiple pathways using engineered orthogonal T7 polymerases

Synthetic genetic sensors and circuits enable programmable control over the timing and conditions of gene expression. They are being increasingly incorporated into the control of complex, multigene pathways and cellular functions. Here, we propose a design strategy to genetically separate the sensing/circuitry functions from the pathway to be controlled. This separation is achieved by having the output of the circuit drive the expression of a polymerase, which then activates the pathway from polymerase-specific promoters. The sensors, circuits and polymerase are encoded together on a 'controller' plasmid. Variants of T7 RNA polymerase that reduce toxicity were constructed and used as scaffolds for the construction of four orthogonal polymerases identified via part mining that bind to unique promoter sequences. This set is highly orthogonal and induces cognate promoters by 8- to 75-fold more than off-target promoters. These orthogonal polymerases enable four independent channels linking the outputs of circuits to the control of different cellular functions. As a demonstration, we constructed a controller plasmid that integrates two inducible systems, implements an AND logic operation and toggles between metabolic pathways that change Escherichia coli green (deoxychromoviridans) and red (lycopene). The advantages of this organization are that (i) the regulation of the pathway can be changed simply by introducing a different controller plasmid, (ii) transcription is orthogonal to host machinery and (iii) the pathway genes are not transcribed in the absence of a controller and are thus more easily carried without invoking evolutionary pressure.

Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems

Despite the efforts that bioengineers have exerted in designing and constructing biological processes that function according to a predetermined set of rules, their operation remains fundamentally circumstantial. The contextual situation in which molecules and single-celled or multi-cellular organisms find themselves shapes the way they interact, respond to the environment and process external information. Since the birth of the field, synthetic biologists have had to grapple with contextual issues, particularly when the molecular and genetic devices inexplicably fail to function as designed when tested in vivo. In this review, we set out to identify and classify the sources of the unexpected divergences between design and actual function of synthetic systems and analyze possible methodologies aimed at controlling, if not preventing, unwanted contextual issues.

Combinatorial targeting of ribbon-helix-helix artificial transcription factors to chimeric recognition sites

Artificial transcription factors (ATFs) are potent synthetic biology tools for modulating endogenous gene expression and precision genome editing. The ribbon-helix-helix (RHH) superfamily of transcription factors are widespread in bacteria and archaea. The principal DNA binding determinant in this family comprises a two-stranded antiparallel β-sheet (ribbons) in which a pair of eight-residue motifs insert into the major groove. Here, we demonstrate that ribbons of divergent RHH proteins are compact and portable elements that can be grafted into a common α-helical scaffold producing active ATFs. Hybrid proteins cooperatively recognize DNA sites possessing core tetramer boxes whose functional spacing is dictated by interactions between the α-helical backbones. These interactions also promote combinatorial binding of chimeras with different transplanted ribbons, but identical backbones, to synthetic sites bearing cognate boxes for each protein either in vitro or in vivo. The composite assembly of interacting hybrid proteins offers potential advantages associated with combinatorial approaches to DNA recognition compared with ATFs that involve binding of a single protein. Moreover, the new class of RHH ATFs may be utilized to re-engineer transcriptional circuits, or may be enhanced with affinity tags, fluorescent moieties or other elements for targeted genome marking and manipulation in bacteria and archaea.

Bayesian multiple-instance motif discovery with BAMBI: inference of recombinase and transcription factor binding sites

Finding conserved motifs in genomic sequences represents one of essential bioinformatic problems. However, achieving high discovery performance without imposing substantial auxiliary constraints on possible motif features remains a key algorithmic challenge. This work describes BAMBI-a sequential Monte Carlo motif-identification algorithm, which is based on a position weight matrix model that does not require additional constraints and is able to estimate such motif properties as length, logo, number of instances and their locations solely on the basis of primary nucleotide sequence data. Furthermore, should biologically meaningful information about motif attributes be available, BAMBI takes advantage of this knowledge to further refine the discovery results. In practical applications, we show that the proposed approach can be used to find sites of such diverse DNA-binding molecules as the cAMP receptor protein (CRP) and Din-family site-specific serine recombinases. Results obtained by BAMBI in these and other settings demonstrate better statistical performance than any of the four widely-used profile-based motif discovery methods: MEME, BioProspector with BioOptimizer, SeSiMCMC and Motif Sampler as measured by the nucleotide-level correlation coefficient. Additionally, in the case of Din-family recombinase target site discovery, the BAMBI-inferred motif is found to be the only one functionally accurate from the underlying biochemical mechanism standpoint. C++ and Matlab code is available at http://www.ee.columbia.edu/~guido/BAMBI or http://genomics.lbl.gov/BAMBI/.

Reengineering Cro protein functional specificity with an evolutionary code

Cro proteins from different lambdoid bacteriophages are extremely variable in their target consensus DNA sequences and constitute an excellent model for evolution of transcription factor specificity. We experimentally tested a bioinformatically derived evolutionary code relating switches between pairs of amino acids at three recognition helix sites in Cro proteins to switches between pairs of nucleotide bases in the cognate consensus DNA half-sites. We generated all eight possible code variants of bacteriophage λ Cro and used electrophoretic mobility shift assays to compare binding of each variant to its own putative cognate site and to the wild-type cognate site; we also tested the wild-type protein against all eight DNA sites. Each code variant showed stronger binding to its putative cognate site than to the wild-type site, except some variants containing proline at position 27; each also bound its cognate site better than wild-type Cro bound the same site. Most code variants, however, displayed poorer affinity and specificity than wild-type λ Cro. Fluorescence anisotropy assays on λ Cro and the triple code variant (PSQ) against the two cognate sites confirmed the switch in specificity and showed larger apparent effects on binding affinity and specificity. Bacterial one-hybrid assays of λ Cro and PSQ against libraries of sequences with a single randomized half-site showed the expected switches in specificity at two of three coded positions and no clear switches in specificity at noncoded positions. With a few caveats, these results confirm that the proposed Cro evolutionary code can be used to reengineer Cro specificity.

Automatic design of digital synthetic gene circuits

De novo computational design of synthetic gene circuits that achieve well-defined target functions is a hard task. Existing, brute-force approaches run optimization algorithms on the structure and on the kinetic parameter values of the network. However, more direct rational methods for automatic circuit design are lacking. Focusing on digital synthetic gene circuits, we developed a methodology and a corresponding tool for in silico automatic design. For a given truth table that specifies a circuit's input–output relations, our algorithm generates and ranks several possible circuit schemes without the need for any optimization. Logic behavior is reproduced by the action of regulatory factors and chemicals on the promoters and on the ribosome binding sites of biological Boolean gates. Simulations of circuits with up to four inputs show a faithful and unequivocal truth table representation, even under parametric perturbations and stochastic noise. A comparison with already implemented circuits, in addition, reveals the potential for simpler designs with the same function. Therefore, we expect the method to help both in devising new circuits and in simplifying existing solutions.

Synthetic Biology is a novel discipline that aims at the construction of new biological systems able to perform specific tasks. Following the example of electrical engineering, most of the synthetic systems so far realized look like circuits where smaller DNA-encoded components are interconnected by the exchange of different kinds of molecules. According to this modular approach, we developed, in a previous work, a tool for the visual design of new genetic circuits whose components are displayed on the computer screen and connected through hypothetical wires where molecules flow. Here, we present an extension of this tool that automatically computes the structure of a digital gene circuit–where the inputs and the output take only 0/1 values–by applying procedures commonly used in electrical engineering to biology. In this way, our method generalizes and simplifies the design of genetic circuits far more complex than the ones so far realized. Moreover, different from other currently used methods, our approach limits the use of optimization procedures and drastically reduces the computational time necessary to derive the circuit structure. Future improvements can be achieved by exploiting some more biological mechanisms able to mimic Boolean behavior, without a substantial growth of the algorithmic complexity.

Local gene regulation details a recognition code within the LacI transcriptional factor family

The specific binding of regulatory proteins to DNA sequences exhibits no clear patterns of association between amino acids (AAs) and nucleotides (NTs). This complexity of protein-DNA interactions raises the question of whether a simple set of wide-coverage recognition rules can ever be identified. Here, we analyzed this issue using the extensive LacI family of transcriptional factors (TFs). We searched for recognition patterns by introducing a new approach to phylogenetic footprinting, based on the pervasive presence of local regulation in prokaryotic transcriptional networks. We identified a set of specificity correlations –determined by two AAs of the TFs and two NTs in the binding sites– that is conserved throughout a dominant subgroup within the family regardless of the evolutionary distance, and that act as a relatively consistent recognition code. The proposed rules are confirmed with data of previous experimental studies and by events of convergent evolution in the phylogenetic tree. The presence of a code emphasizes the stable structural context of the LacI family, while defining a precise blueprint to reprogram TF specificity with many practical applications.

Transcriptional factors (TF) are proteins that bind specific short DNA sequences adjacent to the genes whose transcription they regulate. Although the nucleotide sequence recognized by a given regulator depends on the amino acids contacting the DNA, the mode in which amino acids and nucleotides interact is strongly influenced by the overall protein structure. This prevents the existence of a universal amino acid/nucleotide recognition code. However, recognition rules could be formulated for regulators sharing a similar structure, i.e., for a family or subfamily of TFs. In fact, such rules have already been described for several sets which, in each case, involved a limited number of related TFs. In this study, we ask to what extent a wide-coverage recognition code might actually be found. To answer this question, we use the extensive LacI family of transcriptional regulators. Our analysis suggests that a set of relatively consistent recognition rules does apply within a major subset of this family. These rules could ultimately act as a blueprint for the synthetic redesign of TFs with new specificities.

Programming cells: towards an automated 'Genetic Compiler'

One of the visions of synthetic biology is to be able to program cells using a language that is similar to that used to program computers or robotics. For large genetic programs, keeping track of the DNA on the level of nucleotides becomes tedious and error prone, requiring a new generation of computer-aided design (CAD) software. To push the size of projects, it is important to abstract the designer from the process of part selection and optimization. The vision is to specify genetic programs in a higher-level language, which a genetic compiler could automatically convert into a DNA sequence. Steps towards this goal include: defining the semantics of the higher-level language, algorithms to select and assemble parts, and biophysical methods to link DNA sequence to function. These will be coupled to graphic design interfaces and simulation packages to aid in the prediction of program dynamics, optimize genes, and scan projects for errors.

Analyzing protein structure and function using ancestral gene reconstruction

Protein families with functionally diverse members can illuminate the structural determinants of protein function and the process by which protein structure and function evolve. To identify the key amino acid changes that differentiate one family member from another, most studies have taken a horizontal approach, swapping candidate residues between present-day family members. This approach has often been stymied, however, by the fact that shifts in function often require multiple interacting mutations; chimeric proteins are often nonfunctional, either because one lineage has amassed mutations that are incompatible with key residues that conferred a new function on other lineages, or because it lacks mutations required to support those key residues. These difficulties can be overcome by using a vertical strategy, which reconstructs ancestral genes and uses them as the appropriate background in which to study the effects of historical mutations on functional diversification. In this review, we discuss the advantages of the vertical strategy and highlight several exemplary studies that have used ancestral gene reconstruction to reveal the molecular underpinnings of protein structure, function, and evolution.

Synthetic biology: tools to design, build, and optimize cellular processes

The general central dogma frames the emergent properties of life, which make biology both necessary and difficult to engineer. In a process engineering paradigm, each biological process stream and process unit is heavily influenced by regulatory interactions and interactions with the surrounding environment. Synthetic biology is developing the tools and methods that will increase control over these interactions, eventually resulting in an integrative synthetic biology that will allow ground-up cellular optimization. In this review, we attempt to contextualize the areas of synthetic biology into three tiers: (1) the process units and associated streams of the central dogma, (2) the intrinsic regulatory mechanisms, and (3) the extrinsic physical and chemical environment. Efforts at each of these three tiers attempt to control cellular systems and take advantage of emerging tools and approaches. Ultimately, it will be possible to integrate these approaches and realize the vision of integrative synthetic biology when cells are completely rewired for biotechnological goals. This review will highlight progress towards this goal as well as areas requiring further research.

Directed evolution: new parts and optimized function

Constructing novel biological systems that function in a robust and predictable manner requires better methods for discovering new functional molecules and for optimizing their assembly in novel biological contexts. By enabling functional diversification and optimization in the absence of detailed mechanistic understanding, directed evolution is a powerful complement to 'rational' engineering approaches. Aided by clever selection schemes, directed evolution has generated new parts for genetic circuits, cell-cell communication systems, and non-natural metabolic pathways in bacteria.