SynBioFromLeukipposInstitute

Membrane proteins frequently assemble into higher order homo- or hetero-oligomers within their natural lipid environment. This complex formation can modulate their folding, activity as well as substrate selectivity. Non-disruptive methods avoiding critical steps such as membrane disintegration, transfer into artificial environments or chemical modifications are therefore essential to analyze molecular mechanisms of native membrane protein assemblies. The combination of cell-free synthetic biology, nanodisc-technology and non-covalent mass spectrometry provides excellent synergies for the analysis of membrane protein oligomerization within defined membranes. We exemplify our strategy by oligomeric state characterization of various membrane proteins including ion channels, transporters and membrane integrated enzymes assembling up to hexameric complexes. We further indicate a lipid dependent dimer formation of MraY translocase correlating with the enzymatic activity. The detergent free synthesis of membrane protein/nanodisc samples and the analysis by LILBID mass spectrometry provides a versatile platform for the analysis of membrane proteins in a native environment.

To artificially reprogram cell fate, experimentalists manipulate the gene regulatory networks (GRNs) that maintain a cell's phenotype. In practice, reprogramming is often performed by constant overexpression of specific transcription factors (TFs). This process can be unreliable and inefficient. Here, we address this problem by introducing a new approach to reprogramming based on mathematical analysis. We demonstrate that reprogramming GRNs using constant overexpression may not succeed in general. Instead, we propose an alternative reprogramming strategy: a synthetic genetic feedback controller that dynamically steers the concentration of a GRN's key TFs to any desired value. The controller works by adjusting TF expression based on the discrepancy between desired and actual TF concentrations. Theory predicts that this reprogramming strategy is guaranteed to succeed, and its performance is independent of the GRN's structure and parameters, provided that feedback gain is sufficiently high. As a case study, we apply the controller to a model of induced pluripotency in stem cells.

The chemical synthesis of DNA oligonucleotides and their assembly into synthons, genes, circuits, and even entire genomes by gene synthesis methods has become an enabling technology for modern molecular biology and enables the design, build, test, learn, and repeat cycle underpinning innovations in synthetic biology. In this perspective, we briefly review the techniques and technologies that enable the synthesis of DNA oligonucleotides and their assembly into larger DNA constructs with a focus on recent advancements that have sought to reduce synthesis cost and increase sequence fidelity. The development of lower-cost methods to produce high-quality synthetic DNA will allow for the exploration of larger biological hypotheses by lowering the cost of use and help to close the DNA read-write cost gap.

Computational programs for predicting RNA sequences with desired folding properties have been extensively developed and expanded in the past several years. Given a secondary structure, these programs aim to predict sequences that fold into a target minimum free energy secondary structure, while considering various constraints. This procedure is called inverse RNA folding. Inverse RNA folding has been traditionally used to design optimized RNAs with favorable properties, an application that is expected to grow considerably in the future in light of advances in the expanding new fields of synthetic biology and RNA nanostructures. Moreover, it was recently demonstrated that inverse RNA folding can successfully be used as a valuable preprocessing step in computational detection of novel noncoding RNAs. This review describes the most popular freeware programs that have been developed for such purposes, starting from RNAinverse that was devised when formulating the inverse RNA folding problem. The most recently published ones that consider RNA secondary structure as input are antaRNA, RNAiFold and incaRNAfbinv, each having different features that could be beneficial to specific biological problems in practice. The various programs also use distinct approaches, ranging from ant colony optimization to constraint programming, in addition to adaptive walk, simulated annealing and Boltzmann sampling. This review compares between the various programs and provides a simple description of the various possibilities that would benefit practitioners in selecting the most suitable program. It is geared for specific tasks requiring RNA design based on input secondary structure, with an outlook toward the future of RNA design programs.

CRISPR/Cas9-induced site-specific DNA double-strand breaks (DSBs) can be repaired by homology-directed repair (HDR) or non-homologous end joining (NHEJ) pathways. Extensive efforts have been made to knock-in exogenous DNA to a selected genomic locus in human cells; which, however, has focused on HDR-based strategies and was proven inefficient. Here, we report that NHEJ pathway mediates efficient rejoining of genome and plasmids following CRISPR/Cas9-induced DNA DSBs, and promotes high-efficiency DNA integration in various human cell types. With this homology-independent knock-in strategy, integration of a 4.6 kb promoterless ires-eGFP fragment into the GAPDH locus yielded up to 20% GFP+ cells in somatic LO2 cells, and 1.70% GFP+ cells in human embryonic stem cells (ESCs). Quantitative comparison further demonstrated that the NHEJ-based knock-in is more efficient than HDR-mediated gene targeting in all human cell types examined. These data support that CRISPR/Cas9-induced NHEJ provides a valuable new path for efficient genome editing in human ESCs and somatic cells.

Synthetic biology holds great potential for addressing pressing challenges for mankind and our planet. One technical challenge in tapping into the full potential of synthetic biology is the low efficiency and low throughput of genetic transformation for many types of cells. In this paper, we discuss a novel microfluidic system for improving bacterial electrotransformation efficiency and throughput. Our microfluidic system is comprised of non-uniform constrictions in microchannels to facilitate high electric fields with relatively small applied voltages to induce electroporation. Additionally, the microfluidic device has regions of low electric field to assist in electrophoretic transport of nucleic acids into the cells. The device features hydrodynamically controlled electric fields that allow cells to experience a time dependent electric field that is otherwise difficult to achieve using standard electronics. Results suggest that transformation efficiency can be increased by ∼4×, while throughput can increase by 100-1000× compared to traditional electroporation cuvettes. This work will enable high-throughput and high efficiency genetic transformation of microbes, facilitating accelerated development of genetically engineered organisms.

The chemical synthesis of DNA oligonucleotides and their assembly into synthons, genes, circuits, and even entire genomes by gene synthesis methods has become an enabling technology for modern molecular biology and enables the design, build, test, learn, and repeat cycle underpinning innovations in synthetic biology. In this perspective, we briefly review the techniques and technologies that enable the synthesis of DNA oligonucleotides and their assembly into larger DNA constructs with a focus on recent advancements that have sought to reduce synthesis cost and increase sequence fidelity. The development of lower-cost methods to produce high-quality synthetic DNA will allow for the exploration of larger biological hypotheses by lowering the cost of use and help to close the DNA read–write cost gap.

Regions of genomic DNA called enhancers encode binding sites for transcription factor proteins. Binding of activators and repressors increase and reduce transcription, respectively, but it is not understood how combinations of activators and repressors generate precise patterns of transcription during development. Here, we explore this problem using a fully synthetic transcriptional platform in Drosophila consisting of engineered transcription factor gradients and artificial enhancers. We found that binding sites for a transcription factor that makes DNA accessible are required together with binding sites for transcriptional activators to produce a functional enhancer. Only in this context can changes in the number of activator binding sites mediate quantitative control of transcription. Using an engineered transcriptional repressor gradient, we demonstrate that overlapping repressor and activator binding sites provide more robust repression and sharper expression boundaries than non-overlapping sites. This may explain why this common motif is observed in many developmental enhancers.

Recent advances in genome editing with programmable nucleases have opened up new avenues for multiple applications, from basic research to clinical therapy. The ease of use of the technology-and particularly clustered regularly interspaced short palindromic repeats (CRISPR)-will allow us to improve our understanding of genomic variation in disease processes via cellular and animal models. Here, we highlight the progress made in correcting gene mutations in monogenic hereditary disorders and discuss various CRISPR-associated applications, such as cancer research, synthetic biology, and gene therapy using induced pluripotent stem cells. The challenges, ethical issues, and future prospects of CRISPR-based systems for human research are also discussed.

If you’ve been keeping up with science news, you’ve probably heard of CRISPR, the not-so-new gene editing technique on the block. It’s not as scary as it m

The biochemical models describing complex and dynamic metabolic systems are typically multi-parametric and non-linear, thus the identification of their parameters requires non-linear regression analysis of the experimental data. The stochastic nature of the experimental samples poses the necessity to estimate not only the values fitting best to the model, but also the distribution of the parameters, and to test statistical hypotheses about the values of these parameters. In such situations the application of analytical models for parameter distributions is totally inappropriate because their assumptions are not applicable for intrinsically non-linear regressions. That is why, Monte Carlo simulations are a powerful tool to model biochemical processes. The classification of Monte Carlo approaches is not unified, so here we comply with the interpretation given in (Press et al., 1992), where the general Monte Carlo approach is to construct parallel virtual worlds, in which the experimental estimates will play the role of true parameters, if the way in which the true parameters generate a sample is known. Bootstrap is a modification of Monte Carlo, which uses very few premises imposed on the data, and does not need to know the mechanism by which the true parameters generate experimental samples. Instead, resampling with replacement from the experimental sample is used to construct synthetic samples. As far as confidence intervals (CI) are concerned, literature offers multiple types, but each of them belongs to one of the two main groups: root (Politis, 1998) and percentile intervals (Efron & Tibshirani, 1993). The difference in the philosophy of those two CI types is substantial for the biochemical interpretation of results. The difference here is explained with the difference between classical statistics (where the parameters are fixed unknown quantities) and Bayesian statistics (where the parameters are random variables with unknown distributions), and also with the philosophical differences between objectivity and subjectivity of scientific research. The main conclusion is that root confidence intervals are confidence intervals of the investigated parameters, whereas percentile confidence intervals refer to the estimates of the investigated parameters. Our first application of Monte Carlo and Bootstrap simulation procedures is with a simulation platform for training students in medical biochemistry (Tenekedjiev & Kolev, 2002). In this system, students search for estimates and confidence intervals of parameters of a given biochemical system for different enzyme-substrate pairs. The platform applies Monte Carlo simulation on two stages. Initially, a Monte Carlo procedure is applied to emulate a biochemical experimental measurement setting along with given enzyme kinetic reactions as realistically as possible. The system is in position to simulate continuous enzyme assay (used for adjustment of the “experimental” conditions) and end-point enzyme assay “measurements” (suitable for parameter identification). We use an ordinary differential equation (ODE) as basis of the generation of pseudo-experimental data. The pseudo-real nature of the generated data is ensured by the random incorporation of three types of errors for each repetition of the experiments. The Briggs-Haldane steady-state model is fitted to the pseudo-measured and end-point assay data obtained by the system. The kinetic parameters can be calculated by χ2-minimization. The task is simplified by the existence of a good initial guess from a linearized Lineweaver-Burk model. The two-dimensional root confidence regions of the parameters can be calculated by either Monte Carlo or Bootstrap, following similar procedures. The best point estimate is identified using trimmed mean over the flipped parameters taking only the values from the identified root confidence region. In the majority of biochemical reactions, parameters are unknown in very wide intervals, and may have different numerical order. Finding the root confidence regions (intervals) includes parameter flipping, which often generates results with an incorrect sign. That is why, in a second example (Tanka-Salamon et al., 2008) we propose a multiplicative modification for the estimation of root confidence regions and the best estimate of the parameters, which ensures that all estimates will have a physical meaning. The main assumption is that the ratio between the true parameter value and the optimal parameter value derived from the true data sample has the same distribution as the ratio between the optimal parameter value derived from the true data sample, and the optimal synthetic parameter value derived from the synthetic data sample. The assumption is equivalent to performing classical Bootstrap over the logarithms of the estimated parameters. This method is applied in a real experimental set-up for the estimation of root confidence regions of kinetic constants and root best estimates in amidolytic activity of plasmin under the influence of three fatty acids. By doing so, the inhibition effect of the three fatty acids can be proven and quantified. The measured data have the form of continuous reaction progress curves with several replicas. The product concentrations are predicted by three different models with increasing complexity. We model the instability of the inhibited enzyme and represent the resulting continuous assay model with concomitant inactivation of the enzyme as a system of two stiff ODE. From there, we derive the closed form of the progress curve. The four-dimensional root confidence regions are acquired by Monte Carlo simulation in every data point in each of the progress curves using an analytical model of the measured standard deviation, similarly to the first example.

Production of useful chemicals by industrial microorganisms has been attracting more and more attention. Microorganisms screened from their natural environment usually suffer from low productivity, low stress resistance, and accumulation of by-products. In order to overcome these disadvantages, rational engineering of microorganisms to achieve specific industrial goals has become routine. Rapid development of metabolic engineering and synthetic biology strategies provide novel methods to improve the performance of industrial microorganisms. Rational regulation of gene expression by specific promoters is essential to engineer industrial microorganisms for high-efficiency production of target chemicals. Identification, modification, and application of suitable promoters could provide powerful switches at the transcriptional level for fine-tuning of a single gene or a group of genes, which are essential for the reconstruction of pathways. In this review, the characteristics of promoters from eukaryotic, prokaryotic, and archaea microorganisms are briefly introduced. Identification of promoters based on both traditional biochemical and systems biology routes are summarized. Besides rational modification, de novo design of promoters to achieve gradient, dynamic, and logic gate regulation are also introduced. Furthermore, flexible application of static and dynamic promoters for the rational engineering of industrial microorganisms is highlighted. From the perspective of powerful promoters in industrial microorganisms, this review will provide an extensive description of how to regulate gene expression in industrial microorganisms to achieve more useful goals.

The development of temperature-inducible molecular bioswitches allows on-demand activation of synthetic genetic circuits in microbes.