Hybrid systems biology: application to Escherichia coli

Dissertation presented to obtain a Master degree in BiotechnologyIn complex biological systems, it is unlikely that all relevant cellular functions can be fully described either by a mechanistic (parametric) or by a statistic (nonparametric) modelling approach. Quite often, hybrid semiparametric models are the most appropriate to handle such problems. Hybrid semiparametric systems make simultaneous use of the parametric and nonparametric systems analysis paradigms to solve complex problems. The main advantage of the semiparametric over the parametric or nonparametric frameworks lies in that it broadens the knowledge base that can be used to solve a particular problem, thus avoiding reductionism. In this M.Sc. thesis, a hybrid modelling method was adopted to describe in silico Escherichia coli cells. The method consists in a modified projection to latent structures model that explores elementary flux modes (EFMs) as metabolic network principal components. It maximizes the covariance between measured fluxome and any input “omic” dataset. Additionally this method provides the ranking of EFMs in increasing order of explained flux variance and the identification of correlations between EFMs weighting factors and input variables. When applied to a subset of E. coli transcriptome, metabolome, proteome and envirome (and combinations thereof) datasets from different E. coli strains (both wild-type and single gene knockout strains) the model is able, in general, to make accurate flux predictions. More particularly, the results show that envirome and the combination of envirome and transcriptome are the most appropriate datasets to make an accurate flux prediction (with 88.5% and 85.2% of explained flux variance in the validation partition, respectively). Furthermore, the correlations between EFMs weighting factors and input variables are consistent with previously described regulatory patterns, supporting the idea that the regulation of metabolic functions is conserved among distinct envirome and genotype variants, denoting a high level of modularity of cellular functions

ANN application in maritime industry : Baltic Dry Index forecasting & optimization of the number of container cranes

This dissertation is a study of dry bulk freight index forecasting and port planning, both based on Artificial Neural network application. First the dry bulk market is reviewed, and the reason for the high fluctuation of freight rates through the demand-supply mechanism is examined. Due to the volatile BDI, the traditional linear regression forecasting method cannot guarantee the performance of forecasting, but ANN overcomes this difficulty and gives better performance especially in a short time. Besides, in order to improve the performance of ANN further, wavelet is introduced to pre-process the BDI data. But when the noise (high frequency parts) is stripped, the hidden useful data may also be eliminated. So the performance of different degrees of de-noising models is evaluated, and the best one (most suitable de-noising model) is chosen to forecast BDI, which avoids over de-noising and keeps a fair ability of forecasting. In the second case study, the collected container terminals and ranked, and the throughput of each combination (different crane number) is estimated by applying a trained BP network. The BP network with DEA output is combined, simulating the efficiency of each combination. And finally, the optimal container crane number is fixed due to the highest efficiency and practical reasons. The Conclusion and Recommendation chapter gives some further advice, and many recommendations are given

Neural Circuitry Deficits Associated With Dysfunctional Myelin

In the current study, we have generated mutant mice that lack Claudin 11 (Cldn11) tight junctions in CNS myelin sheaths. In myelin sheaths, Cldn11 forms tight junctions located along the outer and inner edges of the membrane spiral, preventing ions and small molecules from entering the intramyelinic space. The function of Cldn11 tight junctions is to improve the passive properties of the myelin membrane, by increasing membrane resistance and reducing capacitance, thereby improving the speed of saltatory conduction. In the absence of Cldn11, conduction velocity is slowed, most dramatically in small diameter myelinated fibers, somewhat analogous to reducing myelin thickness. Notably, the absence of Cldn11 is without degenerative myelin pathology, enabling direct study on the impact of dysfunctional myelin on neural processing. Undoubtedly, slowed conduction velocity along myelinated axons increases temporal dispersion and, consequently, degrades information transfer between neural circuits. Herein, this dissertation work explores the impact of dysfunctional myelin on neural processing in the conserved integration circuit of the auditory brainstem. We find that dysfunctional myelin alters neural processing, generating an inability to lateralize sound sources on the azimuth plane. Extrapolating this information to higher order circuitry within the cortex, we find that dysfunctional myelin generates a disconnection between brain regions, manifesting in behavioral abnormalities and alteration in neurotransmitter levels. Together, these data demonstrate that non-degenerative changes in myelin membrane passive properties can lead to neurochemistry changes that perturb behavior/perception. Second, they have important implications for the etiology of behavioral disorders in general, and more specifically for the behavioral components of hypomyelinating and demyelinating diseases like multiple sclerosis

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy, Zagreb, Croatia, March 22-23, 2022. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy

Book of abstracts of the 15th International Symposium of Croatian Metallurgical Society - SHMD \u272022, Materials and metallurgy, Zagreb, Croatia, March 22-23, 2022. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Developing and Evaluating a Flexible Wireless Microcoil Array Based Integrated Interface for Epidural Cortical Stimulation.

Stroke leads to serious long-term disability. Electrical epidural cortical stimulation has made significant improvements in stroke rehabilitation therapy. We developed a preliminary wireless implantable passive interface, which consists of a stimulating surface electrode, receiving coil, and single flexible passive demodulated circuit printed by flexible printed circuit (FPC) technique and output pulse voltage stimulus by inductively coupling an external circuit. The wireless implantable board was implanted in cats\u27 unilateral epidural space for electrical stimulation of the primary visual cortex (V1) while the evoked responses were recorded on the contralateral V1 using a needle electrode. The wireless implantable board output stable monophasic voltage stimuli. The amplitude of the monophasic voltage output could be adjusted by controlling the voltage of the transmitter circuit within a range of 5-20 V. In acute experiment, cortico-cortical evoked potential (CCEP) response was recorded on the contralateral V1. The amplitude of N2 in CCEP was modulated by adjusting the stimulation intensity of the wireless interface. These results demonstrated that a wireless interface based on a microcoil array can offer a valuable tool for researchers to explore electrical stimulation in research and the dura mater-electrode interface can effectively transmit electrical stimulation

Roll compaction of pharmaceutical excipients and prediction using intelligent software

Roll compaction is a dry granulation method. In the pharmaceutical industry it assists in binding tablet ingredients together to form a larger mass. This is conducted to ease subsequent processing, decrease dust, improve flowability, improve material distribution, more suitable for moisture and heat sensitive materials than wet granulation methods, minimises operating space and suited for a continuous manufacturing set-up. In pharmaceutical roll compaction various types of powder material mixtures are compacted into ribbon that are subsequently milled and tableted. The aim of this research is to investigate the use of intelligent software (FormRules and INForm software) for predicting the effects of the roll compaction process and formulation characteristics on final ribbon quality. Firstly, the tablet formulations were characterised in terms of their particle size distribution, densities, compressibility, compactibility, effective angle of friction and angle of wall friction. These tablet formulations were then roll compacted. The tablet formulation characteristics and roll compaction results formed 64 datasets, which were then used in FormRules and INForm software training. FormRules software highlighted the key input variables (i.e. tablet formulations, characteristics and roll compaction process parameters). Next these key input variables were used as input variables in the model development training of INForm. The INForm software produced models which were successful in predicting experimental results. The predicted nip angle values of the INForm models were found to be within 5%, which was more accurate to those derived from Johanson’s model prediction. The Johanson’s model was not successful in predicting nip angle above the roll speed of 1 rpm due to air entrainment. It also over-predicted the experimental nip angle of DCPA and MCC by 200%, while the approximation using Johanson’s pressure profile under-predicted the experimental nip angle of DCPA by 5-20% and MCC by 20%