Improving the microbial production of biofuels through metabolic engineering

Les défis conjoints du changement climatique d'origine anthropique et la diminution des réserves de combustibles fossiles sont le moteur de recherche intense pour des sources d'énergie alternatives. Une avenue attrayante est d'utiliser un processus biologique pour produire un biocarburant. Parmi les différentes options en matière de biocarburants, le bio-hydrogène gazeux est un futur vecteur énergétique attrayant en raison de son efficacité potentiellement plus élevé de conversion de puissance utilisable, il est faible en génération inexistante de polluants et de haute densité d'énergie. Cependant, les faibles rendements et taux de production ont été les principaux obstacles à l'application pratique des technologies de bio-hydrogène. Des recherches intensives sur bio-hydrogène sont en cours, et dans les dernières années, plusieurs nouvelles approches ont été proposées et étudiées pour dépasser ces inconvénients. À cette fin, l'objectif principal de cette thèse était d'améliorer le rendement en hydrogène moléculaire avec un accent particulier sur l'ingénierie métabolique et l’utilisation de bioprocédés à variables indépendantes. Une de nos hypothèses était que la production d’hydrogène pourrait être améliorée et rendue plus économiquement viable par ingénierie métabolique de souches d’Escherichia coli producteurs d’hydrogène en utilisant le glucose ainsi que diverses autres sources de carbone, y compris les pentoses. Les effets du pH, de la température et de sources de carbone ont été étudiés. La production maximale d'hydrogène a été obtenue à partir de glucose, à un pH initial de 6.5 et une température de 35°C. Les études de cinétiques de croissance ont montré que la μmax était 0.0495 h-1 avec un Ks de 0.0274 g L-1 lorsque le glucose est la seule source de carbone en milieu minimal M9. .Parmi les nombreux sucres et les dérivés de sucres testés, les rendements les plus élevés d'hydrogène sont avec du fructose, sorbitol et D-glucose; 1.27, 1.46 et 1.51 mol H2 mol-1 de substrat, respectivement. En outre, pour obtenir les interactions entre les variables importantes et pour atteindre une production maximale d'hydrogène, un design 3K factoriel complet Box-Behnken et la méthodologie de réponse de surface (RSM) ont été employées pour la conception expérimentale et l'analyse de la souche d'Escherichia coli DJT135. Le rendement en hydrogène molaire maximale de 1.69 mol H2 mol-1 de glucose a été obtenu dans les conditions optimales de 75 mM de glucose, à 35°C et un pH de 6.5. Ainsi, la RSM avec un design Box-Behken était un outil statistique utile pour atteindre des rendements plus élevés d'hydrogène molaires par des organismes modifiés génétiquement. Ensuite, l'expression hétérologue de l’hydrogénases soluble [Ni-Fe] de Ralstonia eutropha H16 (l'hydrogénase SH) a tenté de démontrer que la mise en place d'une voie capable de dériver l'hydrogène à partir de NADH pourrait surpasser le rendement stoechiométrique en hydrogène.. L’expression a été démontrée par des tests in vitro de l'activité enzymatique. Par ailleurs, l'expression de SH a restaurée la croissance en anaérobie de souches mutantes pour adhE, normalement inhibées en raison de l'incapacité de réoxyder le NADH. La mesure de la production d'hydrogène in vivo a montré que plusieurs souches modifiées métaboliquement sont capables d'utiliser l'hydrogénase SH pour dériver deux moles d’hydrogène par mole de glucose consommé, proche du maximum théorique. Une autre stratégie a montré que le glycérol brut pourrait être converti en hydrogène par photofermentation utilisant Rhodopseudomonas palustris par photofermentation. Les effets de la source d'azote et de différentes concentrations de glycérol brut sur ce processus ont été évalués. À 20 mM de glycérol, 4 mM glutamate, 6.1 mol hydrogène / mole de glycérol brut ont été obtenus dans des conditions optimales, un rendement de 87% de la théorie, et significativement plus élevés que ce qui a été réalisé auparavant. En prolongement de cette étude, l'optimisation des paramètres a également été utilisée. Dans des conditions optimales, une intensité lumineuse de 175 W/m2, 30 mM glycérol et 4.5 mM de glutamate, 6.69 mol hydrogène / mole de glycérol brut ont été obtenus, soit un rendement de 96% de la valeur théorique. La détermination de l'activité de la nitrogénase et ses niveaux d'expression ont montré qu'il y avait relativement peu de variation de la quantité de nitrogénase avec le changement des variables alors que l'activité de la nitrogénase variait considérablement, avec une activité maximale (228 nmol de C2H4/ml/min) au point central optimal. Dans la dernière section, la production d'hydrogène à partir du glucose via la photofermentation en une seule étape a été examinée avec la bactérie photosynthétique Rhodobacter capsulatus JP91 (hup-). La méthodologie de surface de réponse avec Box-Behnken a été utilisée pour optimiser les variables expérimentales de façon indépendante, soit la concentration de glucose, la concentration du glutamate et l'intensité lumineuse, ainsi que d'examiner leurs effets interactifs pour la maximisation du rendement en hydrogène moléculaire. Dans des conditions optimales, avec une intensité lumineuse de 175 W/m2, 35 mM de glucose, et 4.5 mM de glutamate,, un rendement maximal d'hydrogène de 5.5 (± 0.15) mol hydrogène /mol glucose, et un maximum d'activité de la nitrogénase de 246 (± 3.5) nmol C2H4/ml/min ont été obtenus. L'analyse densitométrique de l'expression de la protéine-Fe nitrogenase dans les différentes conditions a montré une variation significative de l'expression protéique avec un maximum au point central optimisé. Même dans des conditions optimales pour la production d'hydrogène, une fraction significative de la protéine Fe a été trouvée dans l'état ADP-ribosylée, suggérant que d'autres améliorations des rendements pourraient être possibles. À cette fin, un mutant amtB dérivé de Rhodobacter capsulatus JP91 (hup-) a été créé en utilisant le vecteur de suicide pSUP202. Les résultats expérimentaux préliminaires montrent que la souche nouvellement conçue métaboliquement, R. capsulatus DG9, produit 8.2 (± 0.06) mol hydrogène / mole de glucose dans des conditions optimales de cultures discontinues (intensité lumineuse, 175 W/m2, 35 mM de glucose et 4.5 mM glutamate). Le statut d'ADP-ribosylation de la nitrogénase-protéine Fe a été obtenu par Western Blot pour la souche R. capsulatus DG9. En bref, la production d'hydrogène est limitée par une barrière métabolique. La principale barrière métabolique est due au manque d'outils moléculaires possibles pour atteindre ou dépasser le rendement stochiométrique en bio-hydrogène depuis les dernières décennies en utilisant les microbes. À cette fin, une nouvelle approche d’ingénierie métabolique semble très prometteuse pour surmonter cette contrainte vers l'industrialisation et s'assurer de la faisabilité de la technologie de la production d'hydrogène. Dans la présente étude, il a été démontré que l’ingénierie métabolique de bactéries anaérobiques facultatives (Escherichia coli) et de bactéries anaérobiques photosynthétiques (Rhodobacter capsulatus et Rhodopseudomonas palustris) peuvent produire de l'hydrogène en tant que produit majeur à travers le mode de fermentation par redirection métabolique vers la production d'énergie potentielle. D'autre part, la méthodologie de surface de réponse utilisée dans cette étude représente un outil potentiel pour optimiser la production d'hydrogène en générant des informations appropriées concernant la corrélation entre les variables et des producteurs de bio-de hydrogène modifiés par ingénierie métabolique. Ainsi, un outil d'optimisation des paramètres représente une nouvelle avenue pour faire un pont entre le laboratoire et la production d'hydrogène à l'échelle industrielle en fournissant un modèle mathématique potentiel pour intensifier la production de bio-hydrogène. Par conséquent, il a été clairement mis en évidence dans ce projet que l'effort combiné de l'ingénierie métabolique et la méthodologie de surface de réponse peut rendre la technologie de production de bio-hydrogène potentiellement possible vers sa commercialisation dans un avenir rapproché.

STUDY OF INFLUENCE OF SAFETY ENGINEERING AND MANAGEMENT PRACTICES IN SELECTED INDUSTRIES IN KERALA

In the twentieth century, as technology grew with it. This resulted in collective efforts and thinking in the direction of controlling work related hazards and accidents. Thus, safety management developed and became an important part of industrial management. While considerable research has been reported on the topic of safety management in industries from various parts of the world, there is scarcity of literature from India. It is logical to think that a clear understanding of the critical safety management practices and their relationships with accident rates and management system certifications would help in the development and implementation of safety management systems. In the first phase of research, a set of six critical safety management practices has been identified based on a thorough review of the prescriptive, practitioner, conceptual and empirical literature. An instrument for measuring the level of practice of these safety conduction a survey using questionnaire in chemical/process industry. The instrument has been empirically validated using Confirmatory Factor Analysis (CFA) approach. As the second step. Predictive validity of safety management practices and the relationship between safety management practices and self-reported accident rates and management system certifications have been investigated using ANOVA. Results of the ANOVA tests show that there is significant difference in the identified safety management practices and the determinants of safety performance have been investigated using Multiple Regression Analysis. The inter-relationships between safety management practices, determinants of safety performance and components of safety performance have been investigated with the help of structural equation modeling. Further investigations into engineering and construction industries reveal that safety climate factors are not stable across industries. However, some factors are found to be common in industries irrespective of the type of industry. This study identifies the critical safety management practices in major accident hazard chemical/process industry from the perspective of employees and the findings empirically support the necessity for obtaining safety specific management system certifications

Downscaling and Modeling the Effects of Climate Change on Hydrology and Water Resources in the Upper Blue Nile River Basin, Ethiopia

The Upper Blue Nile River Basin (UBNRB) located in the western part of Ethiopia, between 7° 45’ and 12° 45’N and 34° 05’ and 39° 45’E has a total area of 174962 km2 . More than 80% of the population in the basin is engaged in agricultural activities. Because of the particularly dry climate in the basin, likewise to most other regions of Ethiopia, the agricultural productivity depends to a very large extent on the occurrence of the seasonal rains. This situation makes agriculture highly vulnerable to the impact of potential climate hazards which are about to inflict Africa as a whole and Ethiopia in particular. To analyze these possible impacts of future climate change on the water resources in the UBNRB, in the first part of the thesis climate projection for precipitation, minimum and maximum temperatures in the basin, using downscaled predictors from three GCMs (ECHAM5, GFDL21 and CSIRO-MK3) under SRES scenarios A1B and A2 have been carried out. The two statistical downscaling models used are SDSM and LARS-WG, whereby SDSM is used to downscale ECHAM5-predictors alone and LARS-WG is applied in both mono-model mode with predictors from ECHAM5 and in multi-model mode with combined predictors from ECHAM5, GFDL21 and CSIRO-MK3. For the calibration/validation of the downscaled models, observed as well as NCEP climate data in the 1970 - 2000 reference period is used. The future projections are made for two time periods; 2046-2065 (2050s) and 2081-2100 (2090s). For the 2050s future time period the downscaled climate predictions indicate rise of 0.6°C to 2.7°C for the seasonal maximum temperatures Tmax, and of 0.5°C to 2.44°C for the minimum temperatures Tmin. Similarly, during the 2090s the seasonal Tmax increases by 0.9°C to 4.63°C and Tmin by 1°C to 4.6°C, whereby these increases are generally higher for the A2 than for the A1B scenario. For most sub-basins of the UBNRB, the predicted changes of Tmin are larger than those of Tmax. Meanwhile, for the precipitation, both downscaling tools predict large changes which, depending on the GCM employed, are such that the spring and summer seasons will be experiencing decreases between -36% to 1% and the autumn and winter seasons an increase of -8% to 126% for the two future time periods, regardless of the SRES scenario used. In the second part of the thesis the semi-distributed, physically based hydrologic model, SWAT (Soil Water Assessment Tool), is used to evaluate the impacts of the above-predicted future climate change on the hydrology and water resources of the UBNRB. Hereby the downscaled future predictors are used as input in the SWAT model to predict streamflow of the Upper Blue Nile as well as other relevant water resources parameter in the basin. Calibration and validation of the streamflow model is done again on 1970-2000 measured discharge at the outlet gage station Eldiem, whereby the most sensitive out the numerous “tuneable” calibration parameters in SWAT have been selected by means of a sophisticated sensitivity analysis. Consequently, a good calibration/validation model performance with a high NSE-coefficient of 0.89 is obtained. The results of the future simulations of streamflow in the basin, using both SDSM- and LARS-WG downscaled output in SWAT reveal a decline of -10% to -61% of the future Blue Nile streamflow, And, expectedly, these obviously adverse effects on the future UBNRB-water availibiliy are more exacerbated for the 2090’s than for the 2050’s, regardless of the SRES.

Development of an ArcGIS extension to model urban climate factors

The possibility to develop automatically running models which can capture some of the most important factors driving the urban climate would be very useful for many planning aspects. With the help of these modulated climate data, the creation of the typically used “Urban Climate Maps” (UCM) will be accelerated and facilitated. This work describes the development of a special ArcGIS software extension, along with two support databases to achieve this functionality. At the present time, lacking comparability between different UCMs and imprecise planning advices going along with the significant technical problems of manually creating conventional maps are central issues. Also inflexibility and static behaviour are reducing the maps’ practicality. From experi-ence, planning processes are formed more productively, namely to implant new planning parameters directly via the existing work surface to map the impact of the data change immediately, if pos-sible. In addition to the direct climate figures, information of other planning areas (like regional characteristics / developments etc.) have to be taken into account to create the UCM as well. Taking all these requirements into consideration, an automated calculation process of urban climate impact parameters will serve to increase the creation of homogenous UCMs efficiently.

The prediction of seasonal and inter-annual climate variations and their impacts on the water resources in the eastern seaboard of Thailand

The research of this thesis dissertation covers developments and applications of short-and long-term climate predictions. The short-term prediction emphasizes monthly and seasonal climate, i.e. forecasting from up to the next month over a season to up to a year or so. The long-term predictions pertain to the analysis of inter-annual- and decadal climate variations over the whole 21st century. These two climate prediction methods are validated and applied in the study area, namely, Khlong Yai (KY) water basin located in the eastern seaboard of Thailand which is a major industrial zone of the country and which has been suffering from severe drought and water shortage in recent years. Since water resources are essential for the further industrial development in this region, a thorough analysis of the potential climate change with its subsequent impact on the water supply in the area is at the heart of this thesis research. The short-term forecast of the next-season climate, such as temperatures and rainfall, offers a potential general guideline for water management and reservoir operation. To that avail, statistical models based on autoregressive techniques, i.e., AR-, ARIMA- and ARIMAex-, which includes additional external regressors, and multiple linear regression- (MLR) models, are developed and applied in the study region. Teleconnections between ocean states and the local climate are investigated and used as extra external predictors in the ARIMAex- and the MLR-model and shown to enhance the accuracy of the short-term predictions significantly. However, as the ocean state – local climate teleconnective relationships provide only a one- to four-month ahead lead time, the ocean state indices can support only a one-season-ahead forecast. Hence, GCM- climate predictors are also suggested as an additional predictor-set for a more reliable and somewhat longer short-term forecast. For the preparation of “pre-warning” information for up-coming possible future climate change with potential adverse hydrological impacts in the study region, the long-term climate prediction methodology is applied. The latter is based on the downscaling of climate predictions from several single- and multi-domain GCMs, using the two well-known downscaling methods SDSM and LARS-WG and a newly developed MLR-downscaling technique that allows the incorporation of a multitude of monthly or daily climate predictors from one- or several (multi-domain) parent GCMs. The numerous downscaling experiments indicate that the MLR- method is more accurate than SDSM and LARS-WG in predicting the recent past 20th-century (1971-2000) long-term monthly climate in the region. The MLR-model is, consequently, then employed to downscale 21st-century GCM- climate predictions under SRES-scenarios A1B, A2 and B1. However, since the hydrological watershed model requires daily-scale climate input data, a new stochastic daily climate generator is developed to rescale monthly observed or predicted climate series to daily series, while adhering to the statistical and geospatial distributional attributes of observed (past) daily climate series in the calibration phase. Employing this daily climate generator, 30 realizations of future daily climate series from downscaled monthly GCM-climate predictor sets are produced and used as input in the SWAT- distributed watershed model, to simulate future streamflow and other hydrological water budget components in the study region in a multi-realization manner. In addition to a general examination of the future changes of the hydrological regime in the KY-basin, potential future changes of the water budgets of three main reservoirs in the basin are analysed, as these are a major source of water supply in the study region. The results of the long-term 21st-century downscaled climate predictions provide evidence that, compared with the past 20th-reference period, the future climate in the study area will be more extreme, particularly, for SRES A1B. Thus, the temperatures will be higher and exhibit larger fluctuations. Although the future intensity of the rainfall is nearly constant, its spatial distribution across the region is partially changing. There is further evidence that the sequential rainfall occurrence will be decreased, so that short periods of high intensities will be followed by longer dry spells. This change in the sequential rainfall pattern will also lead to seasonal reductions of the streamflow and seasonal changes (decreases) of the water storage in the reservoirs. In any case, these predicted future climate changes with their hydrological impacts should encourage water planner and policy makers to develop adaptation strategies to properly handle the future water supply in this area, following the guidelines suggested in this study.

Running climate models on grids using G-Rex

Compute grids are used widely in many areas of environmental science, but there has been limited uptake of grid computing by the climate modelling community, partly because the characteristics of many climate models make them difficult to use with popular grid middleware systems. In particular, climate models usually produce large volumes of output data, and running them also involves complicated workflows implemented as shell scripts. A new grid middleware system that is well suited to climate modelling applications is presented in this paper. Grid Remote Execution (G-Rex) allows climate models to be deployed as Web services on remote computer systems and then launched and controlled as if they were running on the user's own computer. Output from the model is transferred back to the user while the run is in progress to prevent it from accumulating on the remote system and to allow the user to monitor the model. G-Rex has a REST architectural style, featuring a Java client program that can easily be incorporated into existing scientific workflow scripts. Some technical details of G-Rex are presented, with examples of its use by climate modellers.

Land surface anomaly simulations and predictions with a climate model: an El Nino Southern Oscillation case study

The ability of climate models to reproduce and predict land surface anomalies is an important but little-studied topic. In this study, an atmosphere and ocean assimilation scheme is used to determine whether HadCM3 can reproduce and predict snow water equivalent and soil moisture during the 1997–1998 El Nino Southern Oscillation event. Soil moisture is reproduced more successfully, though both snow and soil moisture show some predictability at 1- and 4-month lead times. This result suggests that land surface anomalies may be reasonably well initialized for climate model predictions and hydrological applications using atmospheric assimilation methods over a period of time.

Modelling climate change: the role of unresolved processes

Our understanding of the climate system has been revolutionized recently, by the development of sophisticated computer models. The predictions of such models are used to formulate international protocols, intended to mitigate the severity of global warming and its impacts. Yet, these models are not perfect representations of reality, because they remove from explicit consideration many physical processes which are known to be key aspects of the climate system, but which are too small or fast to be modelled. The purpose of this paper is to give a personal perspective of the current state of knowledge regarding the problem of unresolved scales in climate models. A recent novel solution to the problem is discussed, in which it is proposed, somewhat counter-intuitively, that the performance of models may be improved by adding random noise to represent the unresolved processes.

Developing the next-generation climate system models: challenges and achievements

Although climate models have been improving in accuracy and efficiency over the past few decades, it now seems that these incremental improvements may be slowing. As tera/petascale computing becomes massively parallel, our legacy codes are less suitable, and even with the increased resolution that we are now beginning to use, these models cannot represent the multiscale nature of the climate system. This paper argues that it may be time to reconsider the use of adaptive mesh refinement for weather and climate forecasting in order to achieve good scaling and representation of the wide range of spatial scales in the atmosphere and ocean. Furthermore, the challenge of introducing living organisms and human responses into climate system models is only just beginning to be tackled. We do not yet have a clear framework in which to approach the problem, but it is likely to cover such a huge number of different scales and processes that radically different methods may have to be considered. The challenges of multiscale modelling and petascale computing provide an opportunity to consider a fresh approach to numerical modelling of the climate (or Earth) system, which takes advantage of the computational fluid dynamics developments in other fields and brings new perspectives on how to incorporate Earth system processes. This paper reviews some of the current issues in climate (and, by implication, Earth) system modelling, and asks the question whether a new generation of models is needed to tackle these problems.

Decadal climate prediction (project GCEP)

Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.

Solar change and climate: an update in the light of the current exceptional solar minimum

Solar outputs during the current solar minimum are setting record low values for the space age. Evidence is here reviewed that this is part of a decline in solar activity from a grand solar maximum and that the Sun has returned to a state that last prevailed in 1924. Recent research into what this means, and does not mean, for climate change is reviewed.

Recent oppositely directed trends in solar climate forcings and the global mean surface air temperature. II. Different reconstructions of the total solar irradiance variation and dependence on response time scale

We have previously placed the solar contribution to recent global warming in context using observations and without recourse to climate models. It was shown that all solar forcings of climate have declined since 1987. The present paper extends that analysis to include the effects of the various time constants with which the Earth’s climate system might react to solar forcing. The solar input waveform over the past 100 years is defined using observed and inferred galactic cosmic ray fluxes, valid for either a direct effect of cosmic rays on climate or an effect via their known correlation with total solar irradiance (TSI), or for a combination of the two. The implications, and the relative merits, of the various TSI composite data series are discussed and independent tests reveal that the PMOD composite used in our previous paper is the most realistic. Use of the ACRIM composite, which shows a rise in TSI over recent decades, is shown to be inconsistent with most published evidence for solar influences on pre-industrial climate. The conclusions of our previous paper, that solar forcing has declined over the past 20 years while surface air temperatures have continued to rise, are shown to apply for the full range of potential time constants for the climate response to the variations in the solar forcings.

Dynamical influences on European climate: an uncertain future

Climate science is coming under increasing pressure to deliver projections of future climate change at spatial scales as small as a few kilometres for use in impacts studies. But is our understanding and modelling of the climate system advanced enough to offer such predictions? Here we focus on the Atlantic–European sector, and on the effects of greenhouse gas forcing on the atmospheric and, to a lesser extent, oceanic circulations. We review the dynamical processes which shape European climate and then consider how each of these leads to uncertainty in the future climate. European climate is unique in many regards, and as such it poses a unique challenge for climate prediction. Future European climate must be considered particularly uncertain because (i) the spread between the predictions of current climate models is still considerable and (ii) Europe is particularly strongly affected by several processes which are known to be poorly represented in current models.

Meteorology and climate inspire secondary school students

As part of its National Science and Engineering Week activities in 2009 and 2010, the University of Reading organised two open days for 60 local key stage 4 pupils. The theme of both open days was ‘How do we predict weather and climate?’ Making use of the students’ familiarity with weather and climate, several concepts of relevance to secondary science were investigated. The open days also provided an opportunity for more than 30 research staff from the university to interact with the students. Feedback from students and teachers was extremely positive. This article shows how meteorological science can be used to illustrate elements of the secondary science and mathematics curricula.