Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

Tube-jack and sonic testing for the evaluation of the state of stress in historical masonry

In the investigation and diagnosis of damages to historical masonry structures, the state of stress of the masonry is an important characteristic that must be determined with as much accuracy as possible. Flat-jack testing is a traditional method used to determine the state of stress in historical masonry structures. However, when irregular masonry is tested the method can cause damage to the masonry units and the accuracy of the method is reduced. An enhanced technique, called tube-jack testing, is being developed at the University of Minho to reduce the damage caused during testing and improve the accuracy when used on irregular masonry. This method uses multiple cylindrical jacks inserted in a line of holes drilled in the mortar joints of the masonry, avoiding damage to the masonry units. Concurrently with the development of tube-jack testing, the effect of stress state on sonic testing is being studied. Sonic testing is often used to determine locations of voids and damage in masonry. The focus of these studies was to determine if the state of stress is influencing the sonic test results. In this paper the results of tube-jack testing and sonic testing on masonry walls, built for the purpose of this study in the laboratory, loaded in compression is presented. The tube-jack testing is used to estimate the state of stress in the masonry and the sonic test results are evaluated based on the effect of the applied load on the wall. Future testing and study are suggested for continued development of these test methods.

Hierarchical modelling of timber reference properties using probabilistic methods: Maximum Likelihood Method, Bayesian Methods and Probability Networks

In recent decades, an increased interest has been evidenced in the research on multi-scale hierarchical modelling in the field of mechanics, and also in the field of wood products and timber engineering. One of the main motivations for hierar-chical modelling is to understand how properties, composition and structure at lower scale levels may influence and be used to predict the material properties on a macroscopic and structural engineering scale. This chapter presents the applicability of statistic and probabilistic methods, such as the Maximum Likelihood method and Bayesian methods, in the representation of timber’s mechanical properties and its inference accounting to prior information obtained in different importance scales. These methods allow to analyse distinct timber’s reference properties, such as density, bending stiffness and strength, and hierarchically consider information obtained through different non, semi or destructive tests. The basis and fundaments of the methods are described and also recommendations and limitations are discussed. The methods may be used in several contexts, however require an expert’s knowledge to assess the correct statistic fitting and define the correlation arrangement between properties.