Yes / Naphtha isomerization is an important issue in petroleum industries and it has to be a simple and cost effective technology for producing clean fuel with high gasoline octane number. In this work, based on real industrial data, a detailed process model is developed for an existing naphtha isomerization reactor of Baiji North Refinery (BNR) of Iraq which involves estimation of the kinetic parameters of the reactor. The optimal values of the kinetic parameters are estimated via minimizing the sum of squared errors between the predicted and the experimental data of BNR. Finally, a new isomerization process (named as AJAM process) is proposed and using the reactor model developed earlier, the reactor condition is optimized which maximizes the yield and research octane number (RON) of the reactor.

Yes / Increasing the yield and research octane number (RON) of naphtha isomerization process are the most important issues in industries. There are many alternative industrial naphtha isomerization processes practiced around the world. In addition, AJAM is a new naphtha isomerization process proposed by the authors recently (Ahmed et al., 2018) where the isomerization reactor model was validated using real data of Baiji North Refinery (BNR) of Iraq. In this work, first, the performance of the AJAM Process is evaluated against 8 existing industrial isomerization processes in terms of RON, yield and the cost using model based optimisation techniques. To be consistent, we have used the same isomerization reactor model in all the industrial processes we evaluated here. Secondly, energy saving opportunity in the new AJAM process is studied using pinch technology.

Dengue viral infection is a mosquito-borne disease with four distinct strains, where the interactions between these strains have implications on the severity of the disease outcomes. The two competing hypotheses for the increased severity during secondary infections are antibody dependent enhancement and original antigenic sin. Antibody dependent enhancement suggests that long-lived antibodies from primary infection remain during secondary infection but do not neutralize the virus. Original antigenic sin proposes that T cells specific to primary infection dominate cellular immune responses during secondary infections, but are inefficient at clearing cells infected with non-specific strains. To analyze these hypotheses, we developed within-host mathematical models. In previous work, we predicted a decreased non-neutralizing antibody effect during secondary infection. Since this effect accounts for decreased viral clearance and the virus is in quasi-equilibrium with infected cells, we could be accounting for reduced cell killing and the original antigenic sin hypothesis. To further understand these interactions, we develop a model of T cell responses to primary and secondary dengue virus infections that considers the effect of T cell cross-reactivity in disease enhancement. We fit the models to published patient data and show that the overall infected cell killing is similar in dengue heterologous infections, resulting in dengue fever and dengue hemorrhagic fever. The contribution to overall killing, however, is dominated by non-specific T cell responses during the majority of secondary dengue hemorrhagic fever cases. By contrast, more than half of secondary dengue fever cases have predominant strain-specific T cell responses. These results support the hypothesis that cross-reactive T cell responses occur mainly during severe disease cases of heterologous dengue virus infections. Finally, using the results from our within-host models, we develop a multiscale model of dengue viral infection which couples the within-host virus dynamics to the population level dynamics through a system of partial differential equations. We analytically determine the relationship between the model parameters and the characteristics of the solutions, and find thresholds under which infections persist in the population. Furthermore, we develop and implement a full numerical scheme for our model. / Ph. D.

Mathematical modeling of biological systems has improved the knowledge of scientists for many years. In virology, particularly in the study of hepatitis B virus, mathematical models were used to explain interactions between hepatitis B virus and the human host in the absence and presence of interventions such as drug therapy and vaccines. This thesis seeks to explain the role of e-Antigen, a particle produced by hepatitis B virus, in the pathogenesis of hepatitis B infection. To accomplish this goal, I will provide biological background as well as previous modeling work on the role of e-Antigen in hepatitis B virus infection, before finally developing a new model adapted specifically for connecting hepatitis B progression with e-Antigen and drug therapy. I will analyze the model both analytically and numerically, fit it to virus data from humans chronically infected with hepatitis B that undergo drug therapy, and draw conclusions about the relation between drugs, immune activation, and loss of e-Antigen. / Master of Science

In recent years, dengue viral infection has become one of the most widely-spread mosquito-borne diseases in the world, with an estimated 50-100 million cases annually, resulting in 500,000 hospitalizations. Due to the nature of the immune response to each of the four serotypes of dengue virus, secondary infections of dengue put patients at higher risk for more severe infection as opposed to primary infections. The current hypothesis for this phenomenon is antibody-dependent enhancement, where strain-specific antibodies from the primary infection enhance infection by a heterologous serotype. To determine the mechanisms responsible for the increase in disease severity, we develop mathematical models of within-host virus-cell interaction, epidemiological models of virus transmission, and a combination of the within-host and between-host models. The main results of this thesis focus on the within-host model. We model the effects of antibody responses against primary and secondary virus strains. We find that secondary infections lead to a reduction of virus removal. This is slightly different than the current antibody-dependent enhancement hypothesis, which suggests that the rate of virus infectivity is higher during secondary infections due to antibody failure to neutralize the virus. We use the results from the within-host model in an epidemiological multi-scale model. We start by constructing a two-strain SIR model and vary the parameters to account for the effect of antibody-dependent enhancement. / Master of Science

Stochastic modeling to represent intrinsic and extrinsic noise is an important challenge in molecular systems biology. There are numerous ways to model intrinsic noise. One framework for intrinsic noise in gene regulatory networks was recently proposed within the discrete setting. In contrast, extrinsic perturbations were rarely modeled due to the complex mechanisms that contribute to its emergence. Here a discrete framework to model extrinsic noise is proposed. The interacting species of the model are represented by discrete variables and are perturbed to represent extrinsic noise. In particular, they are subject to a discretized lognormal distribution. Additionally, a delay is imposed on the update with a certain probability. These two perturbations represent global extrinsic noise and pathway-specic extrinsic noise. It leads to large variations in the concentration of proteins, which is consistent with an existing continuous way of modeling extrinsic fluctuations. The framework is applied to three different published discrete models: the cell fate of lambda phage infection of bacteria, the lactose utilization system in E. coli, and a signaling network in melanoma cells. The framework captures factors that signicantly contribute to the random decision between lysis and lysogeny as well as explains the bistable switch in the model of the lac operon. Finally, a feed-forward loop analysis is conducted by measuring and comparing the noise level in the target protein of feed-forward loops. This analysis reveals the ability of certain feed-forward loops to attenuate or amplify fluctuations, dependent upon various levels of noise. In conclusion, this thesis aims to resolve the question of how the extrinsic noise can be modeled and how biological systems are able to maintain functionality in the wake of such large variations. / Master of Science

Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Having caused the deaths of more than 10 million individuals since 2000 with most of them occurring in Africa, malaria remains a serious disease that requires undivided attention. To this end a detailed kinetic model of Plasmodium falciparum glycolysis was constructed, validated and used to determine potential drug targets for the development of novel, effective antimalarial therapies. The kinetic model described the behaviour of the glycolytic enzymes with a set of ordinary differential equations that was solved to obtain the steady state fluxes and concentrations of internal metabolites. The model included a glycerol branch represented in a single fitted equation. This present study set out to detect, characterise, and incorporate into the model the enzymes that constitute the glycerol branch of P. falciparum glycolysis. The kinetic parameters of glycerol 3-phosphate dehydrogenase (G3PDH), the first enzyme in the branch and catalyst of the dihydroxyacetone phosphosate (DHAP) reducing reaction, was determined and added to the detailed kinetic model. The model was subsequently validated by comparing its prediction of steady state fluxes with experimentally measured fluxes. Once it was evident that the predictions of the unfitted model agreed with experimentally measured fluxes, metabolic control analysis was performed on this branched system to ascertain the distribution of control over the steady state flux through the glycerol branch. The control G3PDH exercised over its own flux was less than expected due to the enzyme’s sensitivity to changes in NADH and thus the redox balance of the cell. Attempts were made to detect the enzymes responsible for the conversion of glycerol 3-phosphate (G3P) to glycerol. Very low levels of glycerol kinase activity was observed. Although G3P-dependent release of inorganic phosphate was detected results were inconclusive as to whether a non-specific phosphatase also mediated the conversion. Overall, the expansion of the model to include G3PDH did not affect the steady state metabolite concentrations and flux adversely. / AFRIKAANSE OPSOMMING: Vanaf die jaar 2000 het malaria die dood van meer as 10 miljoen mense veroorsaak. Die meeste sterftes het in Afrika voorgekom —’n aanduiding van hoe ernstige siekte dit is en een wat onverdeelde aandag moet geniet. Om hierdie rede is ’n gedetaileerde kinetiese model van glikoliese in Plasmodium falciparum gebou, gevalideer en gebruik om potensiële dwelm teikens te identifiseer vir die ontwikkeling van nuwe, meer effektiewe anti-malaria terapieë. Die kinetiese model beskryf die gedrag van die glikolitiese ensieme in terme van gewone differensiële vergelykings wat opgelos is om die bestendige toestand fluksies en interne metaboliet konsentrasies te bepaal. Die model sluit ’n gliserol-tak in wat deur ’n enkele aangepaste vergelyking verteenwoordig word. Hierdie studie het voorgeneem om die ensieme van die gliserol-tak van P. falciparum glikoliese te identifiseer, karakteriseer en in die model te inkorporeer. Ons het die kinetiese parameters van die eerste ensiem in die gliserol-tak, gliserol 3-fosfaat dehidrogenase (G3PDH), die katalis van die dihidroksiasetoon fosfaat(DHAP) reduserende reaksie, bepaal. Die kinetiese parameters is by die gedetaileerde model gevoeg. Validering het plaasgevind deur die model se voorspellings met eksperimenteel bepaalde waardes te vergelyk. Toe dit duidelik geword het dat die voorspellings van die model met die eksperimenteel bepaalde fluks ooreenstem, is metaboliese kontrole analiese op die vertakte sisteem uitgevoer. Dit is gedoen om vas te stel hoe die bestendige toestand fluks deur die gliserol-tak beheer word. G3PDH het nie volle beheer oor sy eie fluks nie, in teenstelling met ons vergewagtinge. Daar is gepoog om vas te stel watter ensieme verantwoordelik is vir die produksie van gliserol vanuit gliserol 3-fosfaat (G3P). ’n Lae gliserolkinase aktiwiteit is waargeneem. Alhoewel G3P afhanklike vrystelling van anorganise fosfaat waargeneem is, is dit nie duidelik vanuit die resultate of die proses deur ’n nie-spesifieke fosfatase uitgevoer word nie. Die uitbreiding van die model om ’n G3PDH vergelyking in te sluit het nie die bestendige toestand metaboliet konsentrasies en fluks negatief geaffekteer nie.

An important protective feature of the cerebral circulation is its ability to maintain sufficient cerebral blood flow and oxygen supply in accordance with the energy demands of the brain despite variations in a number of external factors such as arterial blood pressure, heart rate and respiration rate. If cerebral autoregulation is impaired, abnormally low or high CBF can lead to cerebral ischemia, intracranial hypertension or even capillary damage, thus contributing to the onset of cerebrovascular events. The control and regulation of cerebral blood flow is a dynamic, multivariate phenomenon. Sensitive techniques are required to monitor and process experimental data concerning cerebral blood flow and metabolic rate in a clinical setting. This thesis presents a model simulation study and 4 related signal processing studies concerned with CBF regulation. The first study models the regulation of the cerebral vasculature to systemic changes in blood pressure, dissolved blood gas concentration and neural activation in a integrated haemodynamic system. The model simulations show that the three pathways which are generally thought to be independent (pressure, CO₂ and activation) greatly influence each other, it is vital to consider parallel changes of unmeasured variability when performing a single pathway study. The second study shows how simultaneously measured blood gas concentration fluctuations can improve the accuracy of an existing frequency domain technique for recovering cerebral autoregulation dynamics from spontaneous fluctuations in blood pressure and cerebral blood flow velocity. The third study shows how the continuous wavelet transform can recover both time and frequency information about dynamic autoregulation, including the contribution of blood gas concentration. The fourth study shows how the discrete wavelet transform can be used to investigate frequency-dependent coupling between cerebral and systemic cardiovascular dynamics. The final study then uses these techniques to investigate the systemic effects on resting BOLD variability. The general approach taken in this thesis is a combined analysis of both modelling and data analysis. Physiologically-based models encapsulate hypotheses about features of CBF regulation, particularly those features that may be difficult to recover using existing analysis methods, and thus provide the motivation for developing both new analysis methods and criteria to evaluate these methods. On the other hand, the statistical features extracted directly from experimental data can be used to validate and improve the model.

Cancer is one of the leading causes of premature deaths across the world today, and there is an urgent need for imaging techniques that can help in early diagnosis and treatment planning for cancer patients. In the last four decades, magnetic resonance imaging (MRI) has emerged as one of the leading modalities for non-invasive imaging of tumours. By using dynamic contrast-enhanced magnetic resonance imaging (DCEMRI), this modality can be used to acquire information about perfusion and vascularity of tumours, which can help in predicting response to treatment. There are many factors that complicate the analysis of DCE-MRI data, and make clinical predictions based on it unreliable. During data acquisition, there are many sources of uncertainties and errors, especially patient motion, which result in the same image position being representative of many different anatomical locations across time. Apart from motion, there are also other inherent uncertainties and noise associated with the measurement of DCE-MRI parameters, which contribute to the model-fitting error observed when trying to apply pharmacokinetic (PK) models to the data. In this thesis, a probabilistic, model-based registration and parameter estimation (MoRPE) framework for motion correction and PK-parameter estimation in DCE-MRI sequences is presented. The MoRPE framework is first compared with conventional motion correction methods on simulated data, and then applied to data from a clinical trial involving twenty colorectal cancer patients. On clinical data, the ability of MoRPE to discriminate between responders and non-responders to combined chemoand radiotherapy is tested, and found to be superior to other methods. The effect of incorporating different arterial input functions within MoRPE is also assessed. Following this, a quantitative analysis of the uncertainties associated with the different PK parameters is performed using a variational Bayes mathematical framework. This analysis provides a quantitative estimate of the extent to which motion correction affects the uncertainties associated with different parameters. Finally, the importance of estimating spatial heterogeneity of PK parameters within tumours is assessed. The efficacy of different measures of spatial heterogeneity, in predicting response to therapy based on the pre-therapy scan alone are compared, and the prognostic value of a new derived PK parameter the 'acceleration constant' is investigated. The integration of uncertainty estimates of different DCE-MRI parameters into the calculation of their heterogeneity measures is also shown to improve the prediction of response to therapy.

As mathematical modeling gains popularity in K-12 classrooms, it is important to define what this entails for both students and teachers. The following study reviews various definitions of mathematical modeling and how these definitions are relevant for middle grades (5-9) teachers. Following a professional development workshop on mathematical modeling, four middle school teachers expressed their views about teaching mathematics through modeling tasks. This study documents the teachers' perceptions of what it means to model with mathematics, which tasks are most appropriate for their students, and why this is important in each of their classrooms. Although the teachers varied in their views depending on the context and circumstances surrounding each modeling task, they agreed that mathematical modeling helps students build critical thinking skills and provides an opportunity to align mathematics concepts with engaging, realistic phenomena.