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Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

Glycans are essential to all scales of biology, with their intricate structures being crucial for their biological functions. The structural complexity of glycans is communicated through simplified and unified visual representations according to the Symbol Nomenclature for Glycans (SNFG) guidelines adopted by the community. Here, we introduce GlycoDraw, a Python-native implementation for high-throughput generation of high-quality, SNFG-compliant glycan figures with flexible display options. GlycoDraw is released as part of our glycan analysis ecosystem, glycowork, facilitating integration into existing workflows by enabling fully automated annotation of glycan-related figures and thus assisting the analysis of e.g., differential abundance data or glycomics mass spectra.

Breast milk is abundant with functionalized milk oligosaccharides (MOs), to nourish and protect the neonate. Yet we lack a comprehensive understanding of the repertoire and evolution of MOs across Mammalia. We report ∼400 MO-species associations (>100 novel structures) from milk glycomics of nine mostly understudied species: alpaca, beluga whale, black rhinoceros, bottlenose dolphin, impala, L’Hoest’s monkey, pygmy hippopotamus, domestic sheep, and striped dolphin. This revealed the hitherto unknown existence of the LacdiNAc motif (GalNAcβ1-4GlcNAc) in MOs of all species except alpaca, sheep, and striped dolphin, indicating widespread occurrence of this potentially antimicrobial motif in MOs. We also characterize glucuronic acid-containing MOs in the milk of impala, dolphins, sheep, and rhinoceros, previously only reported in cows. We demonstrate that these GlcA-MOs exhibit potent immunomodulatory effects. Our study extends the number of known MOs by >15%. Combined with >1,900 curated MO-species associations, we characterize MO motif distributions, presenting an exhaustive overview of MO biodiversity.

Glycans are important polysaccharides on cellular surfaces that are bound to glycoproteins and glycolipids. These are one of the most common post-translational modifications of proteins in eukaryotic cells. They play important roles in protein folding, cell-cell interactions, and other extracellular processes. Changes in glycan structures may influence the course of different diseases, such as infections or cancer. Glycans are commonly represented using the IUPAC-condensed notation. IUPAC-condensed is a textual representation of glycans operating on the same topological level as the Symbol Nomenclature for Glycans (SNFG) that assigns colored, geometrical shapes to the main monomers. These symbols are then connected in tree-like structures, visualizing the glycan structure on a topological level. Yet for a representation on the atomic level, notations such as SMILES should be used. To our knowledge, there is no easy-to-use, general, open-source, and offline tool to convert the IUPAC-condensed notation to SMILES. Here, we present the open-access Python package GlyLES for the generalizable generation of SMILES representations out of IUPAC-condensed representations. GlyLES uses a grammar to read in the monomer tree from the IUPAC-condensed notation. From this tree, the tool can compute the atomic structures of each monomer based on their IUPAC-condensed descriptions. In the last step, it merges all monomers into the atomic structure of a glycan in the SMILES notation. GlyLES is the first package that allows conversion from the IUPAC-condensed notation of glycans to SMILES strings. This may have multiple applications, including straightforward visualization, substructure search, molecular modeling and docking, and a new featurization strategy for machine-learning algorithms. GlyLES is available at https://github.com/kalininalab/GlyLES.

In this study, we designed and built a gene switch that employs metabolically inert l-glucose to regulate transgene expression in mammalian cells via d-idonate-mediated control of the bacterial regulator LgnR. To this end, we engineered a metabolic cascade in mammalian cells to produce the inducer molecule d-idonate from its precursor l-glucose by ectopically expressing the Paracoccus species 43P-derived catabolic enzymes LgdA, LgnH, and LgnI. To obtain ON- and OFF-switches, we fused LgnR to the human transcriptional silencer domain Krüppel associated box (KRAB) and the viral trans-activator domain VP16, respectively. Thus, these artificial transcription factors KRAB-LgnR or VP16-LgnR modulated cognate promoters containing LgnR-specific binding sites in a d-idonate-dependent manner as a direct result of l-glucose metabolism. In a proof-of-concept experiment, we show that the switches can control production of the model biopharmaceutical rituximab in both transiently and stably transfected HEK-293T cells, as well as CHO-K1 cells. Rituximab production reached 5.9 µg/ml in stably transfected HEK-293T cells and 3.3 µg/ml in stably transfected CHO-K1 cells.

LectinOracle In article number 2103807, Daniel Bojar and co‐workers develope a generalizable deep learning platform for predicting and understanding protein‐carbohydrate interactions, LectinOracle. Combining information from protein and carbohydrate sequences, LectinOracle can predict interactions for new proteins and new carbohydrates or glycans. This has been used for many purposes, such as understanding interactions in the microbiome or predicting epidemiological outcomes. [Image: see text]

Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan‐binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, LectinOracle, a model combining transformer‐based representations for proteins and graph convolutional neural networks for glycans to predict their interaction, is presented. Using a curated data set of 564,647 unique protein–glycan interactions, it is shown that LectinOracle predictions agree with literature‐annotated specificities for a wide range of lectins. Using a range of specialized glycan arrays, it is shown that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. It is further demonstrated that LectinOracle can be used to improve lectin classification, accelerate lectin directed evolution, predict epidemiological outcomes in the context of influenza virus, and analyze whole lectomes in host–microbe interactions. It is envisioned that the herein presented platform will advance both the study of lectins and their role in (glyco)biology., Using a large, curated data set of protein–carbohydrate interactions, a new deep learning model, LectinOracle, is introduced. LectinOracle predicts protein–carbohydrate interactions from their sequences and generalizes to new proteins, carbohydrates, and contexts. Applying LectinOracle to the microbiome and virus epidemics demonstrates its utility for analyzing carbohydrate‐binding proteins in health and disease.

Glycans are critical to every facet of biology and medicine, from viral infections to embryogenesis. Tools to study glycans are rapidly evolving, however the majority of our knowledge is deeply dependent on binding by glycan binding proteins (e.g., lectins). The specificities of lectins, which are often naturally isolated proteins, have not been well- defined, making it difficult to leverage their full potential for glycan analysis. Herein, we use glycan microarray analysis of 116 commercially available lectins, including different preparations of the same lectin, to extract the specific glycan features required for lectin binding. Data was obtained using the Consortium for Functional Glycomics microarray (CFG v5.0) containing 611 glycans. We use a combination of machine learning algorithms to define lectin specificity, mapping inputs (glycan sequences) to outputs (lectin-glycan binding) for a large-scale evaluation of lectin-glycan binding behaviours. Our motif analysis was performed by integrating 68 manually defined glycan features with systematic probing of computational rules for significant binding motifs using mono- and disaccharides- and linkages. Using a combination of machine learning and manual annotation of the data, we created a detailed interpretation of glycan-binding specificity for 57 unique lectins, categorized by their major binding motifs: mannose, complex-type N-glycan, O-glycan, fucose, sialic acid and sulfate, GlcNAc and chitin, Gal and LacNAc, and GalNAc. Our work provides fresh insights into the complex binding features of commercially available lectins in current use, providing a critical guide to these important reagents.

Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins abound in nature. Widely used as staining and characterization reagents in cell biology, and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, we present LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction. Using a curated dataset of 564,647 unique protein-glycan interactions, we show that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. We further identify clusters of lectins with related binding specificity that are not clustered based on sequence similarity. Using a range of specialized glycan arrays, we show that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. We further demonstrate that LectinOracle can analyze whole lectomes and their role in host-microbe interactions. We envision that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.

Controlling gene expression in mammalian cells constitutes one of the core principles of mammalian synthetic biology. Especially for cell-based therapies, inducers of gene expression which demonstrate the highest degree of safety and patient adherence are needed. In this chapter, I describe methods to implement caffeine-controlled gene expression systems into mammalian cells. Using an array of different implementation strategies, from reconstituting transcription factors to activating endogenous signaling pathways, allows for a wide range of sensitivity and capacity of the resulting caffeine-responsive gene switches.

SummaryAs the only nonlinear and most diverse biological sequence, glycans offer substantial challenges for computational biology. These complex carbohydrates participate in nearly all biological processes – from protein folding to the cellular entry of viruses – yet are still not well understood. There are few computational methods to link glycan sequences to functions and those that do exist do not take full advantage of all the available information of glycans. SweetNet is a graph convolutional neural network model that uses graph representation learning to facilitate a computational understanding of glycobiology. SweetNet explicitly incorporates the nonlinear nature of glycans and establishes a framework to map any glycan sequence to a representation. We show that SweetNet outperforms other computational methods in predicting glycan properties on all reported tasks. More importantly, we show that glycan representations, learned by SweetNet, are predictive of organismal phenotypic and environmental properties. Finally, we present a new application for glycan-focused machine learning, the prediction of viral glycan-binding, that can be used to discover new viral receptors and monitor rapidly mutating viruses.

Controlling gene expression in mammalian cells constitutes one of the core principles of mammalian synthetic biology. Especially for cell-based therapies, inducers of gene expression which demonstrate the highest degree of safety and patient adherence are needed. In this chapter, I describe methods to implement caffeine-controlled gene expression systems into mammalian cells. Using an array of different implementation strategies, from reconstituting transcription factors to activating endogenous signaling pathways, allows for a wide range of sensitivity and capacity of the resulting caffeine-responsive gene switches.

Glycans, the most diverse biopolymer, are shaped by evolutionary pressures stemming from host-microbe interactions. Here, we present machine learning and bioinformatics methods to leverage the evolutionary information present in glycans to gain insights into how pathogens and commensals interact with hosts. By using techniques from natural language processing, we develop deep-learning models for glycans that are trained on a curated dataset of 19,299 unique glycans and can be used to study and predict glycan functions. We show that these models can be utilized to predict glycan immunogenicity and the pathogenicity of bacterial strains, as well as investigate glycan-mediated immune evasion via molecular mimicry. We also develop glycan-alignment methods and use these to analyze virulence-determining glycan motifs in the capsular polysaccharides of bacterial pathogens. These resources enable one to identify and study glycan motifs involved in immunogenicity, pathogenicity, molecular mimicry, and immune evasion, expanding our understanding of host-microbe interactions.

Glycans, the most diverse biopolymer and crucial for many biological processes, are shaped by evolutionary pressures stemming in particular from host-pathogen interactions. While this positions glycans as being essential for understanding and targeting host-pathogen interactions, their considerable diversity and a lack of methods has hitherto stymied progress in leveraging their predictive potential. Here, we utilize a curated dataset of 12,674 glycans from 1,726 species to develop and apply machine learning methods to extract evolutionary information from glycans. Our deep learning-based language model SweetOrigins provides evolution-informed glycan representations that we utilize to discover and investigate motifs used for molecular mimicry-mediated immune evasion by commensals and pathogens. Novel glycan alignment methods enable us to identify and contextualize virulence-determining motifs in the capsular polysaccharide of Staphylococcus aureus and Acinetobacter baumannii. Further, we show that glycan-based phylogenetic trees contain most of the information present in traditional 16S rRNA-based phylogenies and improve on the differentiation of genetically closely related but phenotypically divergent species, such as Bacillus cereus and Bacillus anthracis. Leveraging the evolutionary information inherent in glycans with machine learning methodology is poised to provide further – critically needed – insights into host-pathogen interactions, sequence-to-function relationships, and the major influence of glycans on phenotypic plasticity.

Despite their ubiquitous presence in biological systems, glycans have historically received less attention than they deserved. Investigations in recent years have featured important findings about the role of glycans in regulating the human gut microbiota. Here, we present a brief overview of current trends that shape future directions of computational and experimental research approaches and add to our understanding of host-microbe glycointeractions.

Strategies for expanding the sensor space of designer receptors are urgently needed to tailor cell-based therapies to respond to any type of medically relevant molecules. Here, we describe a universal approach to designing receptor scaffolds that enables antibody-specific molecular input to activate JAK/STAT, MAPK, PLCG or PI3K/Akt signaling rewired to transgene expression driven by synthetic promoters. To demonstrate its scope, we equipped the GEMS (generalized extracellular molecule sensor) platform with antibody fragments targeting a synthetic azo dye, nicotine, a peptide tag and the PSA (prostate-specific antigen) biomarker, thereby covering inputs ranging from small molecules to proteins. These four GEMS devices provided robust signaling and transgene expression with high signal-to-noise ratios in response to their specific ligands. The sensitivity of the nicotine- and PSA-specific GEMS devices matched the clinically relevant concentration ranges, and PSA-specific GEMS were able to detect pathological PSA levels in the serum of patients diagnosed with prostate cancer.

Exosomes are cell-derived nanovesicles (50–150 nm), which mediate intercellular communication, and are candidate therapeutic agents. However, inefficiency of exosomal message transfer, such as mRNA, and lack of methods to create designer exosomes have hampered their development into therapeutic interventions. Here, we report a set of EXOsomal transfer into cells (EXOtic) devices that enable efficient, customizable production of designer exosomes in engineered mammalian cells. These genetically encoded devices in exosome producer cells enhance exosome production, specific mRNA packaging, and delivery of the mRNA into the cytosol of target cells, enabling efficient cell-to-cell communication without the need to concentrate exosomes. Further, engineered producer cells implanted in living mice could consistently deliver cargo mRNA to the brain. Therapeutic catalase mRNA delivery by designer exosomes attenuated neurotoxicity and neuroinflammation in in vitro and in vivo models of Parkinson’s disease, indicating the potential usefulness of the EXOtic devices for RNA delivery-based therapeutic applications. Exosomes function as intercellular information transmitters and are candidates for delivery of therapeutic agents. Here the authors present EXOtic, a synthetic biology device for in-situ production of designer exosomes and demonstrate in vivo application in models of Parkinson's disease.

We previously reported novel technology to differentiate induced pluripotent stem cells (IPSCs) into glucose-sensitive insulin-secreting beta-like cells by engineering a synthetic lineage-control network regulated by the licensed food additive vanillic acid. This genetic network was able to program intricate expression dynamics of the key transcription factors Ngn3 (neurogenin 3, OFF-ON-OFF), Pdx1 (pancreatic and duodenal homeobox 1, ON-OFF-ON) and MafA (V-maf musculoaponeurotic fibrosarcoma oncogene homologue A, OFF-ON) to guide the differentiation of IPSC-derived pancreatic progenitor cells to beta-like cells. In the present study, we show for the first time that this network can also program the expression dynamics of Ngn3, Pdx1 and MafA in human embryonic stem cell (hESC)-derived pancreatic progenitor cells and drive differentiation of these cells into glucose-sensitive insulin-secreting beta-like cells. Therefore, synthetic lineage-control networks appear to be a robust methodology for differentiating pluripotent stem cells into somatic cell types for basic research and regenerative medicine.

While nucleic acids and proteins receive ample attention, progress on understanding the structural and functional roles of carbohydrates has lagged behind. Here, we develop a language model for glycans, SweetTalk, taking into account glycan connectivity and composition. We use this model to investigate motifs in glycan substructures, classify them according to their O-/N-linkage, and predict their immunogenicity with an accuracy of ∼92%, opening up the potential for rational glycoengineering.

Pancreatic islet beta cells, the sole producers of insulin in the human body, are the target of autoimmune destruction in type I diabetes. Human islets are regularly transplanted into diabetic patients, but even though this can lead to therapeutic success, their availability is limited. In this chapter, we review current developments in synthetic biology aimed at generating functional islet-like tissues. Implementing synthetic gene switches has already led to progress in overcoming the inefficiencies of stem cell differentiation into beta-like cells, offering the prospect of a consistent supply of glucose-sensitive insulin-secreting cells for biomedical applications, such as cell therapy, disease modeling, and drug discovery. Even though synthetic biology-inspired therapeutics still face considerable challenges, including long-term functionality, safety, and manufacturing hurdles, they clearly have enormous potential for treating the diabetic patients of tomorrow.

Detecting relationship state and quality in romantic couples is an important endeavor for relationship research, couple therapy, and of course couples themselves. Yet current methods to achieve this are intrusive, asynchronous, plagued by ceiling effects, and only assess subjective responses to questionnaires while trying to capture the objective state of a relationship. According to social appraisal theory, human beings rely on emotional responses to assess interpersonal situations, a key element for relationship functioning in couples. Using couples is particularly advantageous as strong emotional reactions are triggered in romantic relationships. Prior research has repeatedly demonstrated that emotion expression in couple interactions is related to relationship satisfaction. Here, we employ deep learning methods to assess the relationship state of romantic couples from images via facial and bodily emotion expression and other features. Our model, DeepConnection, comprises pretrained residual neural networks, spatial pyramid pooling layers, and power mean transformations to extract relevant features from images for binary classification. With this, we achieved an average accuracy of nearly 97% on a separate validation dataset. We also engaged in model interpretation using Gradient-weighted Class Activation Mapping (Grad-CAM) to identify which features allow DeepConnection to detect relationship state. To demonstrate generalizability and robustness, we used DeepConnection to analyze videos of couples exhibiting a range of different postures and facial expressions. The work presented here could inform couples, advance relationship research, and find application in couple therapy to assist the therapist.

Controlling gene expression with sophisticated logic gates has been and remains one of the central aims of synthetic biology. However, conventional implementations of biocomputers use central processing units (CPUs) assembled from multiple protein-based gene switches, limiting the programming flexibility and complexity that can be achieved within single cells. Here, we introduce a CRISPR/Cas9-based core processor that enables different sets of user-defined guide RNA inputs to program a single transcriptional regulator (dCas9-KRAB) to perform a wide range of bitwise computations, from simple Boolean logic gates to arithmetic operations such as the half adder. Furthermore, we built a dual-core CPU combining two orthogonal core processors in a single cell. In principle, human cells integrating multiple orthogonal CRISPR/Cas9-based core processors could offer enormous computational capacity., Proceedings of the National Academy of Sciences of the United States of America, 116 (15), ISSN:0027-8424, ISSN:1091-6490

Capitalizing on the ability of mammalian cells to conduct complex post-translational modifications, most protein therapeutics are currently produced in cell culture systems. Addition of a signal peptide to the product protein enables its accumulation in the cell culture supernatant, but separation of the product from endogenously secreted proteins remains costly and labor-intensive. We considered that global downregulation of translation of non-product proteins would be an efficient strategy to minimize downstream processing requirements. Therefore, taking advantage of the ability of mammalian protein kinase R (PKR) to switch off most cellular translation processes in response to infection by viruses, we fused a caffeine-inducible dimerization domain to the catalytic domain of PKR. Addition of caffeine to this construct results in homodimerization and activation of PKR, effectively rewiring rapid global translational downregulation to the addition of the stimulus in a dose-dependent manner. Then, to protect translation of the target therapeutic, we screened viral and cellular internal ribosomal entry sites (IRESes) known or suspected to be resistant to PKR-induced translational stress. After choosing the best-in-class Seneca valley virus (SVV) IRES, we additionally screened for IRES transactivation factors (ITAFs) as well as for supplementary small molecules to further boost the production titer of the product protein under conditions of global translational downregulation. Importantly, the residual global translation activity of roughly 10% under maximal downregulation is sufficient to maintain cellular viability during a production timeframe of at least five days. Standard industrially used adherent as well as suspension-adapted cell lines transfected with this synthetic biology-inspired Protein Kinase R-Enhanced Protein Production (PREPP) system could produce several medicinally relevant protein therapeutics, such as the blockbuster drug rituximab, in substantial quantities and with significantly higher purity than previous culture technologies. We believe incorporation of such purity-by-design technology in the production process will alleviate downstream processing bottlenecks in future biopharmaceutical manufacturing.

Green fluorescent protein (GFP) fusions are pervasively used to study structures and processes. Specific GFP-binders are thus of great utility for detection, immobilization or manipulation of GFP-fused molecules. We determined structures of two designed ankyrin repeat proteins (DARPins), complexed with GFP, which revealed different but overlapping epitopes. Here we show a structure-guided design strategy that, by truncation and computational reengineering, led to a stable construct where both can bind simultaneously: by linkage of the two binders, fusion constructs were obtained that “wrap around” GFP, have very high affinities of about 10–30 pM, and extremely slow off-rates. They can be natively produced in E. coli in very large amounts, and show excellent biophysical properties. Their very high stability and affinity, facile site-directed functionalization at introduced unique lysines or cysteines facilitate many applications. As examples, we present them as tight yet reversible immobilization reagents for surface plasmon resonance, as fluorescently labelled monomeric detection reagents in flow cytometry, as pull-down ligands to selectively enrich GFP fusion proteins from cell extracts, and as affinity column ligands for inexpensive large-scale protein purification. We have thus described a general design strategy to create a “clamp” from two different high-affinity repeat proteins, even if their epitopes overlap., Scientific Reports, 7 (1), ISSN:2045-2322

Synthetic biology, the synthesis of engineering and biology, has rapidly matured and has dramatically increased the complexity of artificial gene circuits in recent years. The deployment of intricate synthetic gene circuits in mammalian cells requires the establishment of very precise and orthogonal control of transgene expression. In this chapter, we describe methods of modulating the expression of transgenes at the transcriptional level. Using cAMP-response element-binding protein (CREB)-dependent promoters as examples, a tool for the precise tuning of gene expression by using different core promoters and by varying the binding affinity of transcription factor operator sites is described.

SUMMARY Brassinosteroids, which control plant growth and development, are sensed by the membrane receptor kinase BRASSINOSTEROID INSENSITIVE 1 (BRI1). Brassinosteroid binding to the BRI1 leucine-rich repeat (LRR) domain induces heteromerisation with a SOMATIC EMBRYOGENESIS RECEPTOR KINASE (SERK)family co-receptor. This process allows the cytoplasmic kinase domains of BRI1 and SERK to interact, transphosphorylate and activate each other. Here we report crystal structures of the BRI1 kinase domain in its activated form and in complex with nucleotides. BRI1 has structural features reminiscent of both serine/ threonine and tyrosine kinases, providing insight into the evolution of dual-specificity kinases in plants. Phosphorylation of Thr1039, Ser1042 and Ser1044 causes formation of a catalytically competent activation loop. Mapping previously identified serine/threonine and tyrosine phosphorylation sites onto the structure, we analyse their contribution to brassinosteroid signaling. The location of known genetic missense alleles provide detailed insight into the BRI1 kinase mechanism, while our analyses are inconsistent with a previously reported guanylate cyclase activity. We identify a protein interaction surface on the C-terminal lobe of the kinase and demonstrate that the isolated BRI1, SERK2 and SERK3 cytoplasmic segments form homodimers in solution and have a weak tendency to heteromerise. We propose a model in which heterodimerisation of the BRI1 and SERK ectodomains brings their cytoplasmic kinase domains in a catalytically competent arrangement, an interaction that can be modulated by the BRI1 inhibitor protein BKI1.

Synthetic biology, the synthesis of engineering and biology, has rapidly matured and has dramatically increased the complexity of artificial gene circuits in recent years. The deployment of intricate synthetic gene circuits in mammalian cells requires the establishment of very precise and orthogonal control of transgene expression. In this chapter, we describe methods of modulating the expression of transgenes at the transcriptional level. Using cAMP-response element-binding protein (CREB)-dependent promoters as examples, a tool for the precise tuning of gene expression by using different core promoters and by varying the binding affinity of transcription factor operator sites is described.

Synthetic biology has revolutionized the field of biology in the last two decades. By taking apart natural systems and recombining engineered parts in novel constellations, it has not only unlocked a staggering variety of biological control mechanisms but it has also created a panoply of biomedical achievements, such as innovative diagnostics and therapies. The most common mode of action in the field of synthetic biology is mediated by synthetic gene circuits assembled in a systematic and rational manner. This review covers the most recent therapeutic gene circuits implemented in mammalian and bacterial cells designed for the diagnosis and therapy of an extensive array of diseases. Highlighting new tools for therapeutic gene circuits, we describe a future that holds a plethora of potentialities for the medicine of tomorrow.

Engineered proteins with enhanced or altered functionality, generated for example by mutation or domain fusion, are at the core of nearly all synthetic biology endeavors in the context of precision medicine, also known as personalized medicine. From designer receptors sensing elevated blood markers to effectors rerouting signaling pathways to synthetic transcription factors and the customized therapeutics they regulate, engineered proteins play a crucial role at every step of novel therapeutic approaches using synthetic biology. Here, recent developments in protein engineering aided by advances in directed evolution, de novo design, and machine learning are discussed. Building on clinical successes already achieved with chimeric antigen receptor (CAR-) T cells and other cell-based therapies, these developments are expected to further enhance the capabilities of mammalian synthetic biology in biomedical and other applications.