Correlation-based damage detection for complicated truss bridges using multi-layer genetic algorithms

The study presents a multi-layer genetic algorithm (GA) approach using correlation-based methods to facilitate damage determination for through-truss bridge structures. To begin, the structure’s damage-suspicious elements are divided into several groups. In the first GA layer, the damage is initially optimised for all groups using correlation objective function. In the second layer, the groups are combined to larger groups and the optimisation starts over at the normalised point of the first layer result. Then the identification process repeats until reaching the final layer where one group includes all structural elements and only minor optimisations are required to fine tune the final result. Several damage scenarios on a complicated through-truss bridge example are nominated to address the proposed approach’s effectiveness. Structural modal strain energy has been employed as the variable vector in the correlation function for damage determination. Simulations and comparison with the traditional single-layer optimisation shows that the proposed approach is efficient and feasible for complicated truss bridge structures when the measurement noise is taken into account.

Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

Structural identification of a laboratory-based steel through truss cantilevered bridge with suspended span using a layered genetic algorithm with patternsearch step (GAPS)

Structural identification (St-Id) can be considered as the process of updating a finite element (FE) model of a structural system to match the measured response of the structure. This paper presents the St-Id of a laboratory-based steel through-truss cantilevered bridge with suspended span. There are a total of 600 degrees of freedom (DOFs) in the superstructure plus additional DOFs in the substructure. The St-Id of the bridge model used the modal parameters from a preliminary modal test in the objective function of a global optimisation technique using a layered genetic algorithm with patternsearch step (GAPS). Each layer of the St-Id process involved grouping of the structural parameters into a number of updating parameters and running parallel optimisations. The number of updating parameters was increased at each layer of the process. In order to accelerate the optimisation and ensure improved diversity within the population, a patternsearch step was applied to the fittest individuals at the end of each generation of the GA. The GAPS process was able to replicate the mode shapes for the first two lateral sway modes and the first vertical bending mode to a high degree of accuracy and, to a lesser degree, the mode shape of the first lateral bending mode. The mode shape and frequency of the torsional mode did not match very well. The frequencies of the first lateral bending mode, the first longitudinal mode and the first vertical mode matched very well. The frequency of the first sway mode was lower and that of the second sway mode was higher than the true values, indicating a possible problem with the FE model. Improvements to the model and the St-Id process will be presented at the upcoming conference and compared to the results presented in this paper. These improvements will include the use of multiple FE models in a multi-layered, multi-solution, GAPS St-Id approach.

A new data source for fisheries resource assessment: genetic estimates of the effective number of spawners. Final Report to the Fisheries Research and Development Corporation.

The development of innovative methods of stock assessment is a priority for State and Commonwealth fisheries agencies. It is driven by the need to facilitate sustainable exploitation of naturally occurring fisheries resources for the current and future economic, social and environmental well being of Australia. This project was initiated in this context and took advantage of considerable recent achievements in genomics that are shaping our comprehension of the DNA of humans and animals. The basic idea behind this project was that genetic estimates of effective population size, which can be made from empirical measurements of genetic drift, were equivalent to estimates of the successful number of spawners that is an important parameter in process of fisheries stock assessment. The broad objectives of this study were to 1. Critically evaluate a variety of mathematical methods of calculating effective spawner numbers (Ne) by a. conducting comprehensive computer simulations, and by b. analysis of empirical data collected from the Moreton Bay population of tiger prawns (P. esculentus). 2. Lay the groundwork for the application of the technology in the northern prawn fishery (NPF). 3. Produce software for the calculation of Ne, and to make it widely available. The project pulled together a range of mathematical models for estimating current effective population size from diverse sources. Some of them had been recently implemented with the latest statistical methods (eg. Bayesian framework Berthier, Beaumont et al. 2002), while others had lower profiles (eg. Pudovkin, Zaykin et al. 1996; Rousset and Raymond 1995). Computer code and later software with a user-friendly interface (NeEstimator) was produced to implement the methods. This was used as a basis for simulation experiments to evaluate the performance of the methods with an individual-based model of a prawn population. Following the guidelines suggested by computer simulations, the tiger prawn population in Moreton Bay (south-east Queensland) was sampled for genetic analysis with eight microsatellite loci in three successive spring spawning seasons in 2001, 2002 and 2003. As predicted by the simulations, the estimates had non-infinite upper confidence limits, which is a major achievement for the application of the method to a naturally-occurring, short generation, highly fecund invertebrate species. The genetic estimate of the number of successful spawners was around 1000 individuals in two consecutive years. This contrasts with about 500,000 prawns participating in spawning. It is not possible to distinguish successful from non-successful spawners so we suggest a high level of protection for the entire spawning population. We interpret the difference in numbers between successful and non-successful spawners as a large variation in the number of offspring per family that survive – a large number of families have no surviving offspring, while a few have a large number. We explored various ways in which Ne can be useful in fisheries management. It can be a surrogate for spawning population size, assuming the ratio between Ne and spawning population size has been previously calculated for that species. Alternatively, it can be a surrogate for recruitment, again assuming that the ratio between Ne and recruitment has been previously determined. The number of species that can be analysed in this way, however, is likely to be small because of species-specific life history requirements that need to be satisfied for accuracy. The most universal approach would be to integrate Ne with spawning stock-recruitment models, so that these models are more accurate when applied to fisheries populations. A pathway to achieve this was established in this project, which we predict will significantly improve fisheries sustainability in the future. Regardless of the success of integrating Ne into spawning stock-recruitment models, Ne could be used as a fisheries monitoring tool. Declines in spawning stock size or increases in natural or harvest mortality would be reflected by a decline in Ne. This would be good for data-poor fisheries and provides fishery independent information, however, we suggest a species-by-species approach. Some species may be too numerous or experiencing too much migration for the method to work. During the project two important theoretical studies of the simultaneous estimation of effective population size and migration were published (Vitalis and Couvet 2001b; Wang and Whitlock 2003). These methods, combined with collection of preliminary genetic data from the tiger prawn population in southern Gulf of Carpentaria population and a computer simulation study that evaluated the effect of differing reproductive strategies on genetic estimates, suggest that this technology could make an important contribution to the stock assessment process in the northern prawn fishery (NPF). Advances in the genomics world are rapid and already a cheaper, more reliable substitute for microsatellite loci in this technology is available. Digital data from single nucleotide polymorphisms (SNPs) are likely to super cede ‘analogue’ microsatellite data, making it cheaper and easier to apply the method to species with large population sizes.

Negligible evidence for regional genetic population structure for two shark species Rhizoprionodon acutus (Rüppell, 1837) and Sphyrna lewini (Griffith & Smith, 1834) with contrasting biology

Biodiversity of sharks in the tropical Indo-Pacific is high, but species-specific information to assist sustainable resource exploitation is scarce. The null hypothesis of population genetic homogeneity was tested for scalloped hammerhead shark (Sphyrna lewini, n = 237) and the milk shark (Rhizoprionodon acutus, n = 207) from northern and eastern Australia, using nuclear (S. lewini, eight microsatellite loci; R. acutus, six loci) and mitochondrial gene markers (873 base pairs of NADH dehydrogenase subunit 4). We were unable to reject genetic homogeneity for S. lewini, which was as expected based on previous studies of this species. Less expected were similar results for R. acutus, which is more benthic and less vagile than S. lewini. These features are probably driving the genetic break found between Australian and central Indonesian R. acutus (F-statistics; mtDNA, 0.751–0.903, respectively; microsatellite loci, 0.038–0.047 respectively). Our results support the spatially homogeneous monitoring and management plan for shark species in Queensland, Australia.

Genetic diversity and microcystin production by Anabaena in the Gulf of Finland, Baltic Sea

Syanobakteerit (sinilevät) ovat olleet Itämeressä koko nykymuotoisen Itämeren ajan, sillä paleolimnologiset todisteet niiden olemassaolosta Itämeren alueella ovat noin 7000 vuoden takaa. Syanobakteerien massaesiintymät eli kukinnat ovat kuitenkin sekä levinneet laajemmille alueille että tulleet voimakkaimmiksi viimeisten vuosikymmenien aikana. Tähän on osasyynä ihmisten aiheuttama kuormitus, joka rehevöittää Itämerta. Suomenlahti, jota tämä tutkimus käsittelee, on kärsinyt tästä rehevöitymiskehityksestä muita Itämeren altaita enemmän. Syanobakteerit muodostavat jokakesäisiä kukintoja Suomenlahdella - niin sen avomerialueilla kuin rannoillakin. Yleisimmät kukintoja muodostavat syanobakteerisuvut ovat Nodularia, Anabaena ja Aphanizomenon. Kukinnat aiheuttavat paitsi esteettistä haittaa myös terveydellisen riskitekijän. Niiden myrkyllisyys liitetään usein Nodularia-suvun tuottamaan nodulariini-maksamyrkkyyn. Itämeren Aphanizomenon-suvun on todettu olevan myrkytön. Vaikka Itämeren kukintoja aiheuttavista Nodularia- ja Aphanizomenon-syanobakteereista tiedetään varsin paljon, on molekyylimenetelmiin pohjautuva syanobakteeritutkimus ohittanut Itämeren Anabaena-suvun monelta osin. Tämän työn tarkoituksena oli syventää käsitystämme Itämeren Anabaena-syanobakteerista, sen mahdollisesta myrkyllisyydestä, geneettisestä monimuotoisuudesta ja fylogeneettisista sukulaisuussuhteista. Tässä työssä eristettiin 49 planktista Anabaena-kantaa, joista viisi tuottivat mikrokystiinejä. Tämä oli ensimmäinen yksiselitteinen todiste, että Itämeren Anabaena tuottaa maksamyrkyllisiä mikrokystiini-yhdisteitä. Jokainen eristetty myrkyllinen Anabaena-kanta tuotti useita mikrokystiini-variantteja. Lisäksi mikrokystiinejä löydettiin kukintanäytteistä, joissa oli myrkkyä syntetisoivia geenejä sisältäneitä Anabaena-syanobakteereita. Myrkkyjä löydettiin molempina tutkimusvuosina 2003 ja 2004. Myrkkyjen esiintyminen ei siten ollut vain yksittäinen ilmiö. Tässä työssä saimme viitteitä siitä, että maksamyrkyllinen Anabaena-syanobakteeri esiintyisi vähäsuolaisissa vesissä. Tämä riippuvuussuhde jää kuitenkin tulevien tutkimuksien selvitettäväksi. Tässä työssä havaittiin mikrokystiinisyntetaasi-geenien inaktivoituminen Itämeren Anabaena-kannassa ja kukintanäytteissä. Kuvasimme Anabaena-kannan mikrokystiinisyntetaasigeenien sisältä insertioita, jotka hyvin todennäköisesti inaktivoivat myrkyntuoton. Insertion sisältäneeltä kannalta löysimme kuitenkin kaikki mikrokystiinisyntetaasigeenit osoittaen, että geenien olemassaolo ei välttämättä varmista kannan mikrokystiinintuottoa. Mielenkiintoista oli se, että inaktivaation aiheuttavia insertioita löytyi kukintanäytteistä molemmilta tutkimusvuosilta. Vastaavia insertioita ei kuitenkaan löydetty makean veden Anabaena-kannoista tai järvinäytteistä. On yleistä, että syanobakteerikukinnoista löytyy usean syanobakteerisuvun edustajia. Myrkyllisiä sukuja tai lajeja ei voida kuitenkaan erottaa mikroskooppisesti myrkyttömistä. Käsillä olevassa tutkimuksessa kehitettiin molekyylimenetelmä, jolla on mahdollista määrittää kukinnan mahdollisesti maksamyrkylliset syanobakteerisuvut. Tätä menetelmää sovellettiin Itämeren kukintojen tutkimiseen. Itämeren pintavesistä ja ranta-alueiden pohjasta eristetyt Anabaena-kannat osoittautuivat geneettisesti monimuotoisiksi. Tämä Anabaena-syanobakteerien geneettinen monimuotoisuus vahvistettiin monistamalla geenejä suoraan kukintanäytteistä ilman kantojen eristystä. Makeiden vesien ja Itämeren Anabaena-kannat ovat geneettisesti hyvin samankaltaisia. Geneettisissä vertailuissa kävi kuitenkin ilmi, että pohjassa elävien Anabaena-kantojen geneettinen monimuotoisuus oli suurempaa kuin pintavesistä eristettyjen kantojen. Itämeren Anabaena-kantojen sekvenssit muodostivat omia ryhmiä sukupuun sisällä, jolloin on mahdollista, että nämä edustavat Itämeren omia Anabaena-ekotyyppejä. Tämä tutkimus oli ensimmäinen, jossa uusin molekyylimenetelmin systemaattisesti selvitettiin Itämeren Anabaena-syanobakteerin geneettistä populaatiorakennetta, fylogeniaa ja myrkyntuottoa. Tulevaisuudessa monitorointitutkimuksissa on otettava huomioon myös Itämeren Anabaena-syanobakteerin mahdollinen maksamyrkyntuotto – erityisesti vähäsuolaisemmilla rannikkovesillä.

Phenotyping novel stay-green traits to capture genetic variation in senescence dynamics

Stay-green plants retain green leaves longer after anthesis and can have improved yield, particularly under water limitation. As senescence is a dynamic process, genotypes with different senescence patterns may exhibit similar final normalised difference vegetative index (NDVI). By monitoring NDVI from as early as awn emergence to maturity, we demonstrate that analysing senescence dynamics improves insight into genotypic stay-green variation. A senescence evaluation tool was developed to fit a logistic function to NDVI data and used to analyse data from three environments for a wheat (Triticum aestivum L.) population whose lines contrast for stay-green. Key stay-green traits were estimated including, maximum NDVI, senescence rate and a trait integrating NDVI variation after anthesis, as well as the timing from anthesis to onset, midpoint and conclusion of senescence. The integrative trait and the timing to onset and mid-senescence exhibited high positive correlations with yield and a high heritability in the three studied environments. Senescence rate was correlated with yield in some environments, whereas maximum NDVI was associated with yield in a drought-stressed environment. Where resources preclude frequent measurements, we found that NDVI measurements may be restricted to the period of rapid senescence, but caution is required when dealing with lines of different phenology. In contrast, regular monitoring during the whole period after flowering allows the estimation of senescence dynamics traits that may be reliably compared across genotypes and environments. We anticipate that selection for stay-green traits will enhance genetic progress towards high-yielding, stay-green germplasm.

Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

Fuzzy-logic-based health monitoring and residual-life prediction for composite helicopter rotor

A health-monitoring and life-estimation strategy for composite rotor blades is developed in this work. The cross-sectional stiffness reduction obtained by physics-based models is expressed as a function of the life of the structure using a recent phenomenological damage model. This stiffness reduction is further used to study the behavior of measurable system parameters such as blade deflections, loads, and strains of a composite rotor blade in static analysis and forward flight. The simulated measurements are obtained using an aeroelastic analysis of the composite rotor blade based on the finite element in space and time with physics-based damage modes that are then linked to the life consumption of the blade. The model-based measurements are contaminated with noise to simulate real data. Genetic fuzzy systems are developed for global online prediction of physical damage and life consumption using displacement- and force-based measurement deviations between damaged and undamaged conditions. Furthermore, local online prediction of physical damage and life consumption is done using strains measured along the blade length. It is observed that the life consumption in the matrix-cracking zone is about 12-15% and life consumption in debonding/delamination zone is about 45-55% of the total life of the blade. It is also observed that the success rate of the genetic fuzzy systems depends upon the number of measurements, type of measurements and training, and the testing noise level. The genetic fuzzy systems work quite well with noisy data and are recommended for online structural health monitoring of composite helicopter rotor blades.

Tool wear monitoring using ant behaviour

In this paper we show the applicability of Ant Colony Optimisation (ACO) techniques for pattern classification problem that arises in tool wear monitoring. In an earlier study, artificial neural networks and genetic programming have been successfully applied to tool wear monitoring problem. ACO is a recent addition to evolutionary computation technique that has gained attention for its ability to extract the underlying data relationships and express them in form of simple rules. Rules are extracted for data classification using training set of data points. These rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in ACO based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The classification accuracy obtained in ACO based approach is as good as obtained in other biologically inspired techniques.

Identification of delamination in composite beams using spectral estimation and a genetic algorithm

An efficient strategy for identification of delamination in composite beams and connected structures is presented. A spectral finite-element model consisting of a damaged spectral element is used for model-based prediction of the damaged structural response in the frequency domain. A genetic algorithm (GA) specially tailored for damage identification is derived and is integrated with finite-element code for automation. For best application of the GA, sensitivities of various objective functions with respect to delamination parameters are studied and important conclusions are presented. Model-based simulations of increasing complexity illustrate some of the attractive features of the strategy in terms of accuracy as well as computational cost. This shows the possibility of using such strategies for the development of smart structural health monitoring softwares and systems.

Single cell proteomics microchip to profile immune function, with applications in stem cell biology, translational disease mechanism study and clinical therapeutics monitoring

In response to infection or tissue dysfunction, immune cells develop into highly heterogeneous repertoires with diverse functions. Capturing the full spectrum of these functions requires analysis of large numbers of effector molecules from single cells. However, currently only 3-5 functional proteins can be measured from single cells. We developed a single cell functional proteomics approach that integrates a microchip platform with multiplex cell purification. This approach can quantitate 20 proteins from >5,000 phenotypically pure single cells simultaneously. With a 1-million fold miniaturization, the system can detect down to ~100 molecules and requires only ~104 cells. Single cell functional proteomic analysis finds broad applications in basic, translational and clinical studies. In the three studies conducted, it yielded critical insights for understanding clinical cancer immunotherapy, inflammatory bowel disease (IBD) mechanism and hematopoietic stem cell (HSC) biology.

To study phenotypically defined cell populations, single cell barcode microchips were coupled with upstream multiplex cell purification based on up to 11 parameters. Statistical algorithms were developed to process and model the high dimensional readouts. This analysis evaluates rare cells and is versatile for various cells and proteins. (1) We conducted an immune monitoring study of a phase 2 cancer cellular immunotherapy clinical trial that used T-cell receptor (TCR) transgenic T cells as major therapeutics to treat metastatic melanoma. We evaluated the functional proteome of 4 antigen-specific, phenotypically defined T cell populations from peripheral blood of 3 patients across 8 time points. (2) Natural killer (NK) cells can play a protective role in chronic inflammation and their surface receptor – killer immunoglobulin-like receptor (KIR) – has been identified as a risk factor of IBD. We compared the functional behavior of NK cells that had differential KIR expressions. These NK cells were retrieved from the blood of 12 patients with different genetic backgrounds. (3) HSCs are the progenitors of immune cells and are thought to have no immediate functional capacity against pathogen. However, recent studies identified expression of Toll-like receptors (TLRs) on HSCs. We studied the functional capacity of HSCs upon TLR activation. The comparison of HSCs from wild-type mice against those from genetics knock-out mouse models elucidates the responding signaling pathway.

In all three cases, we observed profound functional heterogeneity within phenotypically defined cells. Polyfunctional cells that conduct multiple functions also produce those proteins in large amounts. They dominate the immune response. In the cancer immunotherapy, the strong cytotoxic and antitumor functions from transgenic TCR T cells contributed to a ~30% tumor reduction immediately after the therapy. However, this infused immune response disappeared within 2-3 weeks. Later on, some patients gained a second antitumor response, consisted of the emergence of endogenous antitumor cytotoxic T cells and their production of multiple antitumor functions. These patients showed more effective long-term tumor control. In the IBD mechanism study, we noticed that, compared with others, NK cells expressing KIR2DL3 receptor secreted a large array of effector proteins, such as TNF-α, CCLs and CXCLs. The functions from these cells regulated disease-contributing cells and protected host tissues. Their existence correlated with IBD disease susceptibility. In the HSC study, the HSCs exhibited functional capacity by producing TNF-α, IL-6 and GM-CSF. TLR stimulation activated the NF-κB signaling in HSCs. Single cell functional proteome contains rich information that is independent from the genome and transcriptome. In all three cases, functional proteomic evaluation uncovered critical biological insights that would not be resolved otherwise. The integrated single cell functional proteomic analysis constructed a detail kinetic picture of the immune response that took place during the clinical cancer immunotherapy. It revealed concrete functional evidence that connected genetics to IBD disease susceptibility. Further, it provided predictors that correlated with clinical responses and pathogenic outcomes.

A critical appraisal of tools available for monitoring epigenetic changes in clinical samples from patients with myeloid malignancies

Research over the past decade has confirmed that epigenetic alterations act in concert with genetic lesions to deregulate gene expression in acute myeloid leukemia and myelodysplastic syndromes. In addition, we now have the capability to pharmaceutically target epigenetic modifications, and there is an urgent need forearly validation of the efficacy of the drugs. Also, an improved understanding of the functionality of epigenetic modifications may further pave the road towards an individualized therapy. Here, we provide the pros and cons of the currently most feasible methods used for characterizing the methylome in clinical samples, and give a brief introduction to novel approaches to sequencing that may revolutionize our abilities to characterize the genomes and epigenomes in acute myeloid leukemia and myelodysplastic syndrome patients.

A primer for use of genetic tools in selecting and testing the suitability of set-aside sites protected from deep-sea seafloor massive sulfide mining activities

Seafloor massive sulfide (SMS) mining will likely occur at hydrothermal systems in the near future. Alongside their mineral wealth, SMS deposits also have considerable biological value. Active SMS deposits host endemic hydrothermal vent communities, whilst inactive deposits support communities of deep water corals and other suspension feeders. Mining activities are expected to remove all large organisms and suitable habitat in the immediate area, making vent endemic organisms particularly at risk from habitat loss and localised extinction. As part of environmental management strategies designed to mitigate the effects of mining, areas of seabed need to be protected to preserve biodiversity that is lost at the mine site and to preserve communities that support connectivity among populations of vent animals in the surrounding region. These "set-aside" areas need to be biologically similar to the mine site and be suitably connected, mostly by transport of larvae, to neighbouring sites to ensure exchange of genetic material among remaining populations. Establishing suitable set-asides can be a formidable task for environmental managers, however the application of genetic approaches can aid set-aside identification, suitability assessment and monitoring. There are many genetic tools available, including analysis of mitochondrial DNA (mtDNA) sequences (e.g. COI or other suitable mtDNA genes) and appropriate nuclear DNA markers (e.g. microsatellites, single nucleotide polymorphisms), environmental DNA (eDNA) techniques and microbial metagenomics. When used in concert with traditional biological survey techniques, these tools can help to identify species, assess the genetic connectivity among populations and assess the diversity of communities. How these techniques can be applied to set-aside decision making is discussed and recommendations are made for the genetic characteristics of set-aside sites. A checklist for environmental regulators forms a guide to aid decision making on the suitability of set-aside design and assessment using genetic tools. This non-technical primer document represents the views of participants in the VentBase 2014 workshop.

Codon ambiguities as a mechanism to alter the genetic code in Saccharomyces cerevisiae

Although the genetic code is generally viewed as immutable, alterations to its standard form occur in the three domains of life. A remarkable alteration to the standard genetic code occurs in many fungi of the Saccharomycotina CTG clade where the Leucine CUG codon has been reassigned to Serine by a novel transfer RNA (Ser-tRNACAG). The host laboratory made a major breakthrough by reversing this atypical genetic code alteration in the human pathogen Candida albicans using a combination of tRNA engineering, gene recombination and forced evolution. These results raised the hypothesis that synthetic codon ambiguities combined with experimental evolution may release codons from their frozen state. In this thesis we tested this hypothesis using S. cerevisiae as a model system. We generated ambiguity at specific codons in a two-step approach, involving deletion of tRNA genes followed by expression of non-cognate tRNAs that are able to compensate the deleted tRNA. Driven by the notion that rare codons are more susceptible to reassignment than those that are frequently used, we used two deletion strains where there is no cognate tRNA to decode the rare CUC-Leu codon and AGG-Arg codon. We exploited the vulnerability of the latter by engineering mutant tRNAs that misincorporate Ser at these sites. These recombinant strains were evolved over time using experimental evolution. Although there was a strong negative impact on the growth rate of strains expressing mutant tRNAs at high level, such expression at low level had little effect on cell fitness. We found that not only codon ambiguity, but also destabilization of the endogenous tRNA pool has a strong negative impact in growth rate. After evolution, strains expressing the mutant tRNA at high level recovered significantly in several growth parameters, showing that these strains adapt and exhibit higher tolerance to codon ambiguity. A fluorescent reporter system allowing the monitoring of Ser misincorporation showed that serine was indeed incorporated and possibly codon reassignment was achieved. Beside the overall negative consequences of codon ambiguity, we demonstrated that codons that tolerate the loss of their cognate tRNA can also tolerate high Ser misincorporation. This raises the hypothesis that these codons can be reassigned to standard and eventually to new amino acids for the production of proteins with novel properties, contributing to the field of synthetic biology and biotechnology.