Efficient design of synthetic gene circuits under cell-to-cell variability.

BACKGROUND: Synthetic biologists use and combine diverse biological parts to build systems such as genetic circuits that perform desirable functions in, for example, biomedical or industrial applications. Computer-aided design methods have been developed to help choose appropriate network structures and biological parts for a given design objective. However, they almost always model the behavior of the network in an average cell, despite pervasive cell-to-cell variability. RESULTS: Here, we present a computational framework and an efficient algorithm to guide the design of synthetic biological circuits while accounting for cell-to-cell variability explicitly. Our design method integrates a Non-linear Mixed-Effects (NLME) framework into a Markov Chain Monte-Carlo (MCMC) algorithm for design based on ordinary differential equation (ODE) models. The analysis of a recently developed transcriptional controller demonstrates first insights into design guidelines when trying to achieve reliable performance under cell-to-cell variability. CONCLUSION: We anticipate that our method not only facilitates the rational design of synthetic networks under cell-to-cell variability, but also enables novel applications by supporting design objectives that specify the desired behavior of cell populations.

Performance assessment of a multi-epitope chimeric antigen for the serological diagnosis of acute Mayaro fever.

Mayaro virus (MAYV), which causes mayaro fever, is endemic to limited regions of South America that may expand due to the possible involvement of Aedes spp. mosquitoes in its transmission. Its effective control will require the accurate identification of infected individuals, which has been restricted to nucleic acid-based tests due to similarities with other emerging members of the Alphavirus genus of the Togaviridae family; both in structure and clinical symptoms. Serological tests have a more significant potential to expand testing at a reasonable cost, and their performance primarily reflects that of the antigen utilized to capture pathogen-specific antibodies. Here, we describe the assembly of a synthetic gene encoding multiple copies of antigenic determinants mapped from the nsP1, nsP2, E1, and E2 proteins of MAYV that readily expressed as a stable chimeric protein in bacteria. Its serological performance as the target in ELISAs revealed a high accuracy for detecting anti-MAYV IgM antibodies. No cross-reactivity was observed with serum from seropositive individuals for dengue, chikungunya, yellow fever, Zika, and other infectious diseases as well as healthy individuals. Our data suggest that this bioengineered antigen could be used to develop high-performance serological tests for MAYV infections.

Population structure promotes the evolution of costly sex in artificial gene networks.

We build on previous observations that Hill-Robertson interference generates an advantage of sex that, in structured populations, can be large enough to explain the evolutionary maintenance of costly sex. We employed a gene network model that explicitly incorporates interactions between genes. Mutations in the gene networks have variable effects that depend on the genetic background in which they appear. Consequently, our simulations include two costs of sex-recombination and migration loads-that were missing from previous studies of the evolution of costly sex. Our results suggest a critical role for population structure that lies in its ability to align the long- and short-term advantages of sex. We show that the addition of population structure favored the evolution of sex by disproportionately decreasing the equilibrium mean fitness of asexual populations, primarily by increasing the strength of Muller's Ratchet. Population structure also increased the ability of the short-term advantage of sex to counter the primary limit to the evolution of sex in the gene network model-recombination load. On the other hand, highly structured populations experienced migration load in the form of Dobzhansky-Muller incompatibilities, decreasing the effective rate of migration between demes and, consequently, accelerating the accumulation of drift load in the sexual populations.

Computational analysis of the oscillatory behavior at the translation level induced by mRNA levels oscillations due to finite intracellular resources.

Recent studies have demonstrated how the competition for the finite pool of available gene expression factors has important effect on fundamental gene expression aspects. In this study, based on a whole-cell model simulation of translation in S. cerevisiae, we evaluate for the first time the expected effect of mRNA levels fluctuations on translation due to the finite pool of ribosomes. We show that fluctuations of a single gene or a group of genes mRNA levels induce periodic behavior in all S. cerevisiae translation factors and aspects: the ribosomal densities and the translation rates of all S. cerevisiae mRNAs oscillate. We numerically measure the oscillation amplitudes demonstrating that fluctuations of endogenous and heterologous genes can cause a significant fluctuation of up to 50% in the steady-state translation rates of the rest of the genes. Furthermore, we demonstrate by synonymous mutations that oscillating the levels of mRNAs that experience high ribosomal occupancy (e.g. ribosomal "traffic jam") induces the largest impact on the translation of the S. cerevisiae genome. The results reported here should provide novel insights and principles related to the design of synthetic gene expression circuits and related to the evolutionary constraints shaping gene expression of endogenous genes.

Automated Design of Synthetic Cell Classifier Circuits Using a Two-Step Optimization Strategy.

Cell classifiers are genetic logic circuits that transduce endogenous molecular inputs into cell-type-specific responses. Designing classifiers that achieve optimal differential response between specific cell types is a hard computational problem because it involves selection of endogenous inputs and optimization of both biochemical parameters and a logic function. To address this problem, we first derive an optimal set of biochemical parameters with the largest expected differential response over a diverse set of logic circuits, and second, we use these parameters in an evolutionary algorithm to select circuit inputs and optimize the logic function. Using this approach, we design experimentally feasible microRNA-based circuits capable of perfect discrimination for several real-world cell-classification tasks. We also find that under realistic cell-to-cell variation, circuit performance is comparable to standard cross-validation performance estimates. Our approach facilitates the generation of candidate circuits for experimental testing in therapeutic settings that require precise cell targeting, such as cancer therapy.

Enrichment of error-free synthetic DNA sequences by CEL I nuclease.

As the availability of DNA sequence information has grown, so has the need to replicate DNA sequences synthetically. Synthetically produced DNA sequences allow the researcher to exert greater control over model systems and allow for the combinatorial design and construction of novel metabolic and regulatory pathways, as well as optimized protein-coding sequences for biotechnological applications. This utility has made synthetically produced DNA a hallmark of the molecular biosciences and a mainstay of synthetic biology. However, synthetically produced DNA has a significant shortcoming in that it typically has an error rate that is orders of magnitude higher when compared to DNA sequences derived directly from a biological source. This relatively high error rate adds to the cost and labor necessary to obtain sequence-verified clones from synthetically produced DNA sequences. This unit describes a protocol to enrich error-free sequences from a population of error-rich DNA via treatment with CEL I (Surveyor) endonuclease. This method is a straightforward and quick way of reducing the error content of synthetic DNA pools and reliably reduces the error rates by >6-fold per round of treatment.

TopDown real-time gene synthesis.

This chapter introduces a simple, cost-effective TopDown one-step gene synthesis method, which is suitable for the sequence assembly of fairly long DNA. This method can be distinguished from conventional gene synthesis methods by two key features: (1) the melting temperature of the outer primers is designed to be ∼8°C lower than that of the assembly oligonucleotides, and (2) different annealing temperatures are utilized to selectively control the efficiencies of oligonucleotide assembly and full-length template amplification. This method eliminates the interference between polymerase chain reactions (PCR) assembly and amplification in one-step gene synthesis. Additionally, the TopDown gene synthesis has been combined with the LCGreen I DNA fluorescence dye in a real-time gene synthesis approach for investigating the stepwise efficiency and kinetics of PCR-based gene synthesis. The obtained real-time fluorescence signals are compared with gel electrophoresis results to optimize gene synthesis conditions.

Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization.

BACKGROUND: Direct gene synthesis is becoming more popular owing to decreases in gene synthesis pricing. Compared with using natural genes, gene synthesis provides a good opportunity to optimize gene sequence for specific applications. In order to facilitate gene optimization, we have developed a stand-alone software called Visual Gene Developer. RESULTS: The software not only provides general functions for gene analysis and optimization along with an interactive user-friendly interface, but also includes unique features such as programming capability, dedicated mRNA secondary structure prediction, artificial neural network modeling, network & multi-threaded computing, and user-accessible programming modules. The software allows a user to analyze and optimize a sequence using main menu functions or specialized module windows. Alternatively, gene optimization can be initiated by designing a gene construct and configuring an optimization strategy. A user can choose several predefined or user-defined algorithms to design a complicated strategy. The software provides expandable functionality as platform software supporting module development using popular script languages such as VBScript and JScript in the software programming environment. CONCLUSION: Visual Gene Developer is useful for both researchers who want to quickly analyze and optimize genes, and those who are interested in developing and testing new algorithms in bioinformatics. The software is available for free download at http://www.visualgenedeveloper.net.

Multiallelic synthetic quality control material: lessons learned from the cystic fibrosis external quality assessment scheme.

AIM: With the arrival of increasingly complex molecular tests, we are obliged to create new ways to monitor and troubleshoot the underperformance of these multiplex assays. A synthetic multiallelic quality control material has been designed to augment genomic DNA controls. We aimed to evaluate the control on a large scale, testing it on a wide variety of oligonucleotide ligation assays, test protocols, and analysis software. In addition, we investigated how laboratories treat untried and complex materials. METHODS: The synthetic control monitored 32 cystic fibrosis transmembrane conductance regulator mutations and polymorphisms simultaneously. Participants of a cystic fibrosis external quality assessment scheme were invited to analyze the quality control. RESULTS: In total, 58 laboratories participated in this study. Twenty-seven (47%) laboratories detected 32 variants; another 27 laboratories (47%) detected from 31 to 4 variants and 4 participants reported no variants (6%). The main observations included administrative errors when indicating variants on a checklist, errors caused by misreading the instructions for use of the control or assay, and technical problems related to the assay used. CONCLUSION: Synthetic quality control materials proved to be valuable in troubleshooting underperforming assays and complement existing genomic controls. The study also revealed a strong need for increased quality control in the postanalytical phase of testing.

Robust model matching design methodology for a stochastic synthetic gene network.

Synthetic biology has shown its potential and promising applications in the last decade. However, many synthetic gene networks cannot work properly and maintain their desired behaviors due to intrinsic parameter variations and extrinsic disturbances. In this study, the intrinsic parameter uncertainties and external disturbances are modeled in a non-linear stochastic gene network to mimic the real environment in the host cell. Then a non-linear stochastic robust matching design methodology is introduced to withstand the intrinsic parameter fluctuations and to attenuate the extrinsic disturbances in order to achieve a desired reference matching purpose. To avoid solving the Hamilton-Jacobi inequality (HJI) in the non-linear stochastic robust matching design, global linearization technique is used to simplify the design procedure by solving a set of linear matrix inequalities (LMIs). As a result, the proposed matching design methodology of the robust synthetic gene network can be efficiently designed with the help of LMI toolbox in Matlab. Finally, two in silico design examples of the robust synthetic gene network are given to illustrate the design procedure and to confirm the robust model matching performance to achieve the desired behavior in spite of stochastic parameter fluctuations and environmental disturbances in the host cell.

Hierarchical gene synthesis using DNA microchip oligonucleotides.

High-cost of oligonucleotides is one of the major problems to low-cost gene synthesis. Although DNA oligonucleotides from cleavable DNA microchips has been adopted for the low-cost gene synthesis, construction of DNA molecules larger than 1 kb has been largely hampered due to the difficulties of DNA assembly associated with the negligible quantity of chip oligonucleotides. Here we report a hierarchical method for the synthesis of large genes using oligonucleotides from programmable DNA microchips. Using this hierarchical method, we successfully synthesized 1056 bp Dpo4 and 2325 bp Pfu DNA polymerase genes as models. This hierarchical strategy can be further expanded for the syntheses of multiple large genes in a scalable manner.

High-quality gene assembly directly from unpurified mixtures of microarray-synthesized oligonucleotides.

To meet the growing demand for synthetic genes more robust, scalable and inexpensive gene assembly technologies must be developed. Here, we present a protocol for high-quality gene assembly directly from low-cost marginal-quality microarray-synthesized oligonucleotides. Significantly, we eliminated the time- and money-consuming oligonucleotide purification steps through the use of hybridization-based selection embedded in the assembly process. The protocol was tested on mixtures of up to 2000 oligonucleotides eluted directly from microarrays obtained from three different chip manufacturers. These mixtures containing <5% perfect oligos, and were used directly for assembly of 27 test genes of different sizes. Gene quality was assessed by sequencing, and their activity was tested in coupled in vitro transcription/translation reactions. Genes assembled from the microarray-eluted material using the new protocol matched the quality of the genes assembled from >95% pure column-synthesized oligonucleotides by the standard protocol. Both averaged only 2.7 errors/kb, and genes assembled from microarray-eluted material without clonal selection produced only 30% less protein than sequence-confirmed clones. This report represents the first demonstration of cost-efficient gene assembly from microarray-synthesized oligonucleotides. The overall cost of assembly by this method approaches 5¢ per base, making gene synthesis more affordable than traditional cloning.

Improved PCR-based gene synthesis method and its application to the Citrobacter freundii phytase gene codon modification.

Gene synthesis technologies provide a powerful tool for increasing protein expression through codon optimization and gene modification. Here we describe an improved PCR-based gene synthesis technology, which is accurate, simple and cheap. The improved PCR-based gene synthesis (IPS) method consists of two steps. The first one is the synthesis of 300-400bp fragments by PCR reaction with Pfu DNA polymerase from 60-mer and 30-mer oligonucleotides with a 15bp overlap. The second one is assembling of fragments from the first step into the full-length gene by PCR reaction. Using this approach, we have successfully synthesized a modified phytase gene with 1256bp in length with optimal codons for expression in Pichia pastoris. P. pastoris strain that expressed the modified phytase gene (phyA-mod) showed a 50% increase in phytase activity level. In addition, we propose an inexpensive method for error correction, based on overlap-extension PCR (OE-PCR).