Dissertations / Theses on the topic 'Biochemical engineering'

Thesis (PhD (Chemical Engineering))--Cape Peninsula University of Technology, 2019
The South African wine industry is constantly facing several challenges which affect the quality of wine, the local/global demand and consequently the revenue generated. These challenges include the ongoing drought, bush fires, climate change and several liquor amendment bills aimed at reducing alcohol consumption and alcohol outlets in South Africa. It is therefore critical for the wine industry to expand and find alternative ways in which sub-standard or surplus wine grapes can be used to prevent income losses and increase employment opportunities. Traditional Balsamic Vinegar (TBV) is a geographically and legislative protected product produced only in a small region in Italy. However, the methodology can be used to produce similar vinegars in other regions. Balsamic-styled vinegar (BSV), as defined in this thesis, is a vinegar produced by partially following the methods of TBV while applying process augmentation techniques. Balsamic-styled vinegar is proposed to be a suitable product of sub-standard quality or surplus wine grapes in South Africa. However, the production of BSV necessitates the use of cooked (high sugar) grape must which is a less favourable environment to the microorganisms used during fermentation. Factors that negatively affect the survival of the microorganisms include low water activity due to the cooking, high osmotic pressure and high acidity. To counteract these effects, methods to improve the survival of the non-Saccharomyces yeasts and acetic acid bacteria used are essential. The primary aim of this study was to investigate several BSV process augmentation techniques such as, aeration, agitation, cell immobilization, immobilized cell reusability and oxygen mass transfer kinetics in order to improve the performance of the microbial consortium used during BSV production. The work for this study was divided into four (4) phases. For all the phases a microbial consortium consisting of non-Saccharomyces yeasts (n=5) and acetic acid bacteria (n=5) was used. Inoculation of the yeast and bacteria occurred simultaneously. The 1st phase of the study entailed evaluating the effect of cells immobilized by gel entrapment in Ca-alginate beads alongside with free-floating cells (FFC) during the production of BSV. Two Ca-alginate bead sizes were tested i.e. small (4.5 mm) and large (8.5 mm) beads to evaluate the effects of surface area or bead size on the overall acetification rates. Ca-alginate beads and FFC fermentations were also evaluated under static and agitated (135 rpm) conditions. The 2nd phase of the study involved studying the cell adsorption technique for cell immobilization which was carried-out using corncobs (CC) and oak wood chips (OWC), while comparing to FFC fermentations. At this phase of the study, other vinegar bioreactor parameters such as agitation and aeration were studied in contrast to static fermentations. One agitation setting (135 rpm) and two aeration settings were tested i.e. high (0.3 vvm min−1) and low (0.15 vvm min−1) aeration conditions. Furthermore, to assess the variations in cell adsorption capabilities among individual yeast and AAB cells, the quantification of cells adsorbed on CC and OWC prior- and post-fermentation was conducted using the dry cell weight method. The 3rd phase of the study entailed evaluating the reusability abilities of all the matrices (small Ca-alginate beads, CC and OWC) for successive fermentations. The immobilized cells were evaluated for reusability on two cycles of fermentation under static conditions. Furthermore, the matrices used for cell immobilization were further analysed for structure integrity by scanning electron microscopy (SEM) before and after the 1st cycle of fermentations. The 3rd phase of the study also involved the sensorial (aroma and taste) evaluations of the BSV’s obtained from the 1st cycle of fermentation in order to understand the sensorial effects of the Ca-alginate beads, CC and OWC on the final BSV. The 4th phase of the study investigated oxygen mass transfer kinetics during non-aerated and aerated BSV fermentation. The dynamic method was used to generate several dissolved oxygen profiles at different stages of the fermentation. Consequently, the data obtained from the dynamic method was used to compute several oxygen mass transfer parameters, these include oxygen uptake rate ( 𝑟𝑟𝑂𝑂2 ), the stoichiometric coefficient of oxygen consumption vs acid yield (𝑌𝑌𝑂𝑂/𝐴𝐴), the oxygen transfer rate (𝑁𝑁𝑂𝑂2 ), and the volumetric mass transfer coefficients (𝐾𝐾𝐿𝐿𝑎𝑎). During all the phases of the study samples were extracted on weekly intervals to evaluate pH, sugar, salinity, alcohol and total acidity using several analytical instruments. The 4th phase of the study involved additional analytical tools, i.e. an oxygen µsensor to evaluate dissolved oxygen and the ‘Speedy breedy’ to measure the respiratory activity of the microbial consortium used during fermentation. The data obtained from the 1st phase of the study demonstrated that smaller Ca-alginate beads resulted in higher (4.0 g L-1 day−1) acetification rates compared to larger (3.0 g L-1 day−1) beads, while freely suspended cells resulted in the lowest (0.6 g L-1 day−1) acetification rates. The results showed that the surface area of the beads had a substantial impact on the acetification rates when gel entrapped cells were used for BSV fermentation. The 2nd phase results showed high acetification rates (2.7 g L-1 day−1) for cells immobilized on CC in contrast to cells immobilized on OWC and FFC, which resulted in similar and lower acetification rates. Agitated fermentations were unsuccessful for all the treatments (CC, OWC and FFC) studied. Agitation was therefore assumed to have promoted cell shear stress causing insufficient acetification during fermentations. Low aerated fermentations resulted in better acetification rates between 1.45–1.56 g L-1 day−1 for CC, OWC and FFC. At a higher aeration setting, only free-floating cells were able to complete fermentations with an acetification rate of 1.2 g L-1 day−1. Furthermore, the adsorption competence data showed successful adsorption on CC and OWC for both yeasts and AAB with variations in adsorption efficiencies, whereby OWC displayed a lower cell adsorption capability compared to CC. On the other hand, OWC were less efficient adsorbents due to their smooth surface, while the rough surface and porosity of CC led to improved adsorption and, therefore, enhanced acetification rates. The 3rd phase results showed a substantial decline in acetification rates on the 2nd cycle of fermentations when cells immobilized on CC and OWC were reused. While cells entrapped in Ca-alginate beads were able to complete the 2nd cycle of fermentations at reduced acetification rates compared to the 1st cycle of fermentations. The sensory results showed positive ratings for BSV’s produced using cells immobilized in Ca-alginate beads and CC. However, BSV’s produced using OWC treatments were neither ‘liked nor disliked’ by the judges. The SEM imaging results further showed a substantial loss of structural integrity for Ca-alginate beads after the 1st cycle fermentations, with minor changes in structural integrity of CC being observed after the 1st cycle fermentations. OWC displayed the same morphological structure before and after the 1st cycle fermentations which was attributed to their robustness. Although Ca-alginate beads showed a loss in structural integrity, it was still assumed that Ca-alginate beads provided better protection against the harsh environmental conditions in contrast to CC and OWC adsorbents due to the acetification rates obtained on both cycles. The 4th phase data obtained from the computations showed that non-aerated fermentations had a higher 𝑌𝑌𝑂𝑂/𝐴𝐴, 𝑟𝑟𝑂𝑂2 , 𝑁𝑁𝑂𝑂2 and a higher 𝐾𝐾𝐿𝐿𝑎𝑎 . It was clear that aerated fermentations had a lower aeration capacity due to an inappropriate aeration system design and an inappropriate fermentor. Consequently, aeration led to several detrimental biochemical changes in the fermentation medium thus affecting 𝐾𝐾𝐿𝐿𝑎𝑎 and several oxygen mass transfer parameters which serve as a driving force. Overall, it was concluded that the best method for BSV production is the use of cells entrapped in small alginate beads or cells adsorbed on CC under static and non-aerated fermentations. This conclusion was based on several factors such as cell affinity/cell protection, acetification rates, fermentation period and sensorial contributions. However, cells entrapped in Ca-alginate beads had the highest acetification rates. The oxygen mass transfer computations demonstrated a high 𝐾𝐾𝐿𝐿𝑎𝑎 when Ca-alginate beads were used under static-non-aerated conditions compared to fermentations treated with CC. Therefore, a fermentor with a high aeration capacity needs to be designed to best suit the two BSV production systems (Ca-alginate beads and CC). It is also crucial to develop methods which can increase the robustness of Ca-alginate beads in order to improve cell retention and reduce the loss of structural integrity for subsequent cycles of fermentation. Studies to define parameters used for upscaling the BSV production process for large scale productions are also crucial.

Proteins are intimately involved in almost every cellular phenomenon, from life to death. Understanding the interactions of proteins with each other and other macromolecules and the ability to rationally redesign them to improve their activities or control their function are of considerable current interest. Split-protein methodologies provide an avenue for achieving many of these goals. Since the original discovery of conditionally activated split-ubiquitin, the field has grown exponentially to include the activities of over a dozen different proteins. The flexibility of the systems has resulted in their use across a wide spectrum, both literally and figuratively, to primarily screen, visualize and quantitate macromolecular interactions in a variety of biological systems. In another arena, there is significant interest the apoptosis-regulating proteins: the Bcl-2 family. These proteins are found in many cell types and control, through expression levels as well as other mechanisms, the apoptotic state of a protein as governed by intrinsic death signals generated from such sources as DNA damage and viral infection. The apoptotic function of these proteins are mainly governed by a single type of interaction: the helix:receptor binding of the BH3-Only helices to the anti-apoptotic receptor proteins. While this often promiscuous helix:receptor interaction has received much scrutiny, the nature of the anti-apoptotic binding pocket, especially with regard to the specific residues that govern the interaction, has been lacking. With the high sensitivity and rapid analysis platform afforded by the cell-free split-luciferase analysis methodology, we devised and carried out the first systematic and large scale alanine mutagenesis of all five major anti-apoptotic members of the Bcl-2 family, validated these results both with biophysical methods as well as correlation with previous studies. Our results help explain how different receptors can bind a wide range of helices and also uncovered details regarding binding that are not possible with structural or computational analysis alone. In a second area of research, we have utilized the interaction of BH3 helices and their receptors for designing small molecule controlled protein kinases and phosphatases. In this protein design area, BH3-Only helices were inserted using a knowledge based approach into particular loops within both a protein kinase and a protein phosphatase. The BH3-Only helix interaction with added receptors, such as Bcl-xL provided an allosteric switch for turning-off the activity of the helix-inserted enzymes. The activity of the enzymes could then be turned-on by the addition of a cell-permeable small molecule that is known to bind the receptor. This plug-and-play design was demonstrated to be successful for two very different enzyme classes and likely provides a general and tunable biological element for controlling the activity of one or more proteins and enzymes in a biochemical networks.

The term biological system encompasses all living systems within which components interact with one another on different levels of organisation. It is the goal of systems biology to explain how biological function emerges from the structure and dynamics of biological systems. The position of systems biology is that the functioning of biological systems is explicable in terms of the dynamic interaction of the components of that system. Reproducing these dynamics using computational models is therefore a promising way to inferring the causal relationships underlying biological function.This standpoint places systems thinking firmly on the centre stage of biological reasoning. However modelling complex systemic interactions is notoriously difficult for humans. It therefore becomes important to develop computer tools which support the modelling of biological systems. Such tools must harness the power of mathematics, engineering and computer science to support the creation of integrated and executable working models of biologigal systems.

Application of bioengineering technologies for enhanced biological hydrogen production is a promising approach that may play a vital role in sustainable energy. Due to the ability of several naturally occurring microorganisms to generate hydrogen through varying metabolic processes, biological hydrogen has become an attractive alternative energy and fuel source. One area of particular interest is the production of biological hydrogen in organically-rich engineered systems, such as those associated with waste treatment. Despite the potential for high energy yields, hydrogen yields generated by bacteria in waste systems are often limited due to a focus on microbial utilization of organic material towards cellular growth rather than production of biogas. To address this concern and to improve upon current technological applications, metabolic engineering approaches may be applied to known hydrogen producing organisms. However, to successfully modify metabolic pathways, full understanding of metabolic networks involved in expression of microbial traits in hydrogen producing organisms is necessary. Because microbial communities associated with hydrogen production are capable of exhibiting a number of phenotypes, attempts to apply metabolic engineering concepts have been restricted due to limited information regarding complex metabolic processes and regulatory networks involved in expression of microbial traits associated with biohydrogen production. To bridge this gap, this dissertation focuses on identification of phenotype-related biochemical processes within sets of phenotype-expressing organisms. Specifically, through co-development and application of evolutionary genome-scale phenotype-centric comparative network analysis tools, metabolic and cellular components related to three phenotypes (i.e., dark fermentative, hydrogen production and acid tolerance) were identified. The computational tools employed for the systematic elucidation of key phenotype-related genes and subsystems consisted of two complementary methods. The first method, the Network Instance-Based Biased Subgraph Search (NIBBS) algorithm, identified phenotype-related metabolic genes and subsystems through comparative analysis of multiple genome-scale metabolic networks. The second method was the multiple alignments of metabolic pathways for identification of conserved metabolic sub-systems in small sets of phenotype-expressing microorganisms. For both methodologies, key metabolic genes and sub-systems that are likely to be related to hydrogen production and acid-tolerance were identified and hypotheses regarding their role in phenotype expression were generated. In addition, analysis of hydrogen producing enzymes generated by NIBBS revealed the potential interplay, or cross-talk, between metabolic pathways. To identify phenotype-related subnetworks, three complementary approaches were applied to individual, and sets of phenotype-expressing microorganisms. In the first method, the Dense ENriched Subgraph Enumeration (DENSE) algorithm, partial "prior knowledge" about the proteins involved in phenotype-related processes are utilized to identify dense, enriched sets of known phenotype-related proteins in Clostridium acetobutylicum. The second approach utilized a bi-clustering algorithm to identify phenotype-related functional association modules associated with metabolic controls of phenotype-related pathways. Last, through comparison of hundreds of genome-scale networks of functionally associated proteins, the á, â-motifs approach, was applied to identify phenotype-related subsystems. Application of methodologies for identification of subnetworks resulted in detection of regulatory proteins, transporters, and signaling proteins predicted to be related to phenotype-expression. Through analysis of protein interactions, clues to the functional roles and associations of previously uncharacterized proteins were identified (DENSE) and hypotheses regarding potentially important acid-tolerant mechanisms were generated (á, â-motifs). Similar to the NIBBS algorithm, analysis of functional modules predicted by the bi-clustering algorithm suggest cross-talk is occurring between pathways associated with hydrogen production. The ability of these phenotype-centric comparative network analysis tools to identify both known and potentially new biochemical process is important for providing further understanding and insights into metabolic networks and system controls involved in the expression of microbial traits. In particular, identification of phenotype-related metabolic components through a systems approach provides the underlying foundation for the development of improved bioengineering technologies and experimental design for enhanced biological hydrogen production.

Combined bioreaction separation studies have been carried out for the first time on a moving port semi-continuous counter-current chromatographic reactor-separator (SCCR-S1) consisting of twelve 5.4cm id x 75cm long columns packed with calcium charged cross-linked polystyrene resin (KORELA V07C). The inversion of sucrose to glucose and fructose in the presence of the enzyme invertase and the biochemIcal synthesis of dextran and fructose from sucrose in the presence of the enzyme dextransucrase were investigated. A dilute stream of the appropriate enzyme in deionised water was used as the eluent stream. The effect of switch time, feed concentration, enzyme activity, eluent rate and enzyme to feed concentration ratio on the combined bioreaction-separation were investigated. For the invertase reaction, at 20.77% w/v sucrose feed concentrations complete conversions were achieved. The enzyme usage was 34% of the theoretical enzyme amount needed to convert an equivalent amount of sucrose over the same time period when using a conventional fermenter. The fructose rich (FRP) and glucose rich (GRP) product purities obtained were over 90%. By operating at 35% w/v sucrose feed concentration and employing the product splitting and recycling techniques, the total concentration and purity of the GRP increased from 32% w/v to 4.6% and from 92.3% to 95% respectively. The FRP concentration also increased from 1.82% w/v to 2.88% w/v. A mathematical model was developed for the combined reaction-separation and used to simulate the continuous inversion of sucrose and product separation using the SCCR-S1. In the biosynthesis of dextran studies, 52% conversion of a 2% w/v sucrose concentration feed was achieved. An average dextran molecular weight of 4 millIon was obtained in the dextran rich (DRP) product stream. The enzyme dextransucrase was purifed successfully using centrifugation and ultrafiltration techniques.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 109-112).
Scientists, both academic and industrial, develop two main types of drugs: 1) small molecule drugs, which are usually chemically synthesized and are taken orally and 2) large molecule, biotherapeutic, or protein-based drugs, which are often synthesized via ribosome transcription in bacteria cells and are injected. Historically, the majority of drug development, revenue, and products has come from small molecule drugs. However, recently biotherapeutic drugs have become more common due to their increased potency and specificity (the ability to chemically bond to the targeted protein of interest). Researchers now estimate that as much as 50% of current drug development activities (pre-market approval) are focused on these protein-based drugs. There are several well-documented steps necessary in the development of a new large molecule drug. One critical element during the end of the biotherapeutic drug discovery phase and the beginning of the manufacturing phase is known as preformulation or formulation development. During this stage scientists systematically test the effects of adding various excipients (non-protein additives added to enhance the protein stability, solubility, activity of the drug, etc.) to the potential large molecule drug. Differential scanning calorimetry (DSC) is a common technique used to perform these formulation studies. In a classic DSC experiment, a protein is heated from 20-80°C and the heat absorbed while the protein unfolds is measured. Many researchers prefer the use of a DSC instrument because of its label-free nature, meaning that no fluorescent or radio-labeled tag is necessary to perform the measurement. The heat absorbed during the unfolding event(s) is directly measured. However, current commercial DSC instruments suffer from high protein consumption (especially when compared to other labeled techniques), low sensitivity, and slow throughput. The aim of this thesis is to address two of the three areas mentioned above: high protein consumption and slow throughput. Since many formulation development studies are performed at therapeutic or high protein concentrations, one can reduce the experimental cell volume and thereby reduce the amount of protein material consumed. However, since there is less sample, less heat is produced. While in the literature there are several heat transfer models that describe how a DSC instrument literature there are several heat transfer models that describe how a DSC instrument functions, there are surprisingly few heat transfer models that detail how ambient temperature disturbances impact the thermal measurement. To better describe this behavior, a simplified state-space thermal model was created to predict the disturbance rejection of a custom DSC instrument. This model was verified experimentally using linear stochastic system identification techniques. To reduce sample throughput, the prototype calorimeter cell was made from disposable materials. Because the majority of protein systems are thermodynamically irreversible, at elevated temperatures the protein solution often aggregates and needs to be cleaned before a subsequent experiment can be run. This cleaning process constitutes a significant portion of the overall time to run an experiment. This thesis documents a fully functional DSC instrument that, while not completely disposable, has been designed, built, and tested with disposable microfluidic materials. Future work would then solve the technical hurdles of repeatably loading disposable microfluidic cells into the DSC instrument.
by Scott Jacob McEuen.
Ph.D.

The field of biochemical systems modeling and analysis is faced with an unprecedented flood of data from experimental methodologies of molecular biology. While these techniques continue to leapfrog ahead in the speed, volume and finesse with which they generate data, the methods of data analysis and interpretation, however, are still playing the catch-up game. The notions of systems analysis have found a new foothold, under the banner of Systems Biology, with the promise of uncovering the rationale for the designs of biological systems from their parts lists, as they are generated by experimentation and sorted and managed by bioinformatics tools. With an aim to complement hypothesis-driven and reductionistic biological research, and not replace it, a systems biologist relies on the tools of mathematical and computational modeling to be able to contribute meaningfully to any ongoing bio-molecular systems research. These systems analysis tools, however, should not only have their roots steeped well in the theoretical foundations of biochemistry, mathematics and numerical computation, but they should be married to a framework that facilitates the required systems way of thought for all its users computational scientists, experimentalists and molecular biologists alike. Hopefully, such framework-based tools would go beyond just providing fancy GUIs, numerical packages for integrating ODEs and/or optimization libraries. The intent of this thesis is to present a framework and toolbox for biochemical systems modeling, with an application in metabolic pathway analysis and/or metabolic engineering. The research presented here builds upon the tenets of a very well established and generic approach to biological systems modeling and analysis, called Biochemical Systems Theory (BST), which is almost forty years old. The nuances of modeling and practical hurdles to analysis are presented in the context of a real-time case study of analyzing the glucolytic pathway in the bacterium Lactococcus lactis. Alongside, the thesis presents the features of a MATLAB-based software application that has been built upon the framework of BST and is aptly named as Biochemical Systems Toolbox (BSTBox). The thesis presents novel contributions, made by the author during the course of his research, to state-of-the-art techniques in parameter estimation, and robustness and sensitivity analysis topics that, as this thesis will show, remain to be the most restrictive bottlenecks in the world of biological systems modeling and analysis.

This work is motivated by challenges in data-based modelling of complex systems due to limited information of sparse and noisy experimental data. Optimal experimental design (OED) techniques, which aim at devising necessary experiments to generate informative measurement data to facilitate model identification, have been investigated comprehensively. The limitations of existing experimental design approaches have been extensively discussed, based on which advanced experimental design methods and efficient numerical strategies have been developed for improved solutions. Two case study biochemical systems have been used through the research investigation, one is an enzyme reaction system, the other one is a lab-scale enzymatic biodiesel production system. The main contributions of this PhD work can be summarised as follows: Single objective experimental designs by considering one type of design factors, i.e. input intensity, measurement set selection, sampling profile design, respectively, has been formulated and numerical strategies to solve these optimisation problems have been described in detail. Implementations of these design methods to biochemical systems have demonstrated its efficiency in reducing parameter estimation errors. A new OED strategy has been proposed to cope with OED problems including multiple design factors in one optimisation framework. An iterative two-layer design structure is developed. In the lower layer for observation design, the sampling profile and the measurement set selection are combined and formulated as a single integrated observation design problem, which is relaxed to a convex optimization problem that can be solved with a local method. Thus the measurement set selection and the sampling profile can be determined simultaneously. In the upper layer for input design, the optimisation of input intensities is obtained through stochastic global searching. In this way, the multi-factor optimisation problem is solved through the integration of a stochastic method, for the upper layer, and a deterministic method, for the lower layer. A new enzyme reaction model has been established which represents a typical class of enzymatic kinetically controlled synthesis process. This model contains important kinetic reaction features, moderate complexity, and complete model information. It can be used as a benchmark problem for development and comparison of OED algorithms. Systematic analysis has been performed in order to examine the system behaviours, and the dependence on model parameters, initial operation conditions. Structural identifiability and practical identifiability of this system have been analysed and identifiable parameters determined. The design of experiment for the enzyme reactionsystem by considering different types of design variables have been investigated. The parameter estimation precision can be improved significantly by using the proposed OED techniques, compared to the non-designed condition. The OED techniques are numerically investigated based on a lab-scale biodiesel production process with real experimental data through research collaboration with DTU in Denmark. The OED applications on this real system model allow to examine the effectiveness and efficiency of those new proposed OED methods. The measurement set selection and the sampling design of this system are developed which provide detailed instructions on how to improve experiments through OED. Also, the sensitivity analysis and parameter identifiability analysis are conducted; and their impacts to experimental design are clearly identified.

The project objective was to develop a reliable selection procedure to match contact lens materials with individual wearers by the identification of a biochemical marker for assessment of in-eye performance of contact lenses. There is a need for such a procedure as one of the main reasons for contact lens wearers ceasing wearing contact lenses is poor end of day comfort i.e. the lenses become intolerable to the wearer as the day progresses. The selection of an optimal material for individual wearers has the potential benefit to reduce drop Qut, hence increasing the overall contact lens population, and to improve contact lens comfort for established wearers. Using novel analytical methods and statistical techniques, we were able to investigate the interactions between the composition of the tear film and of the biofilm deposited on the contact lenses and contact lens performance. The investigations were limited to studying the lipid components of the tear film; the lipid layer, which plays a key role in preventing evaporation and stabilising the tear film, has been reported to be significantly thinner and of different mixing characteristics during contact lens wear. Different lipid families were found to influence symptomatology, in vivo tear film structure and stability as well as ocular integrity. Whereas the symptomatology was affected by both the tear film lipid composition and the nature of the lipid deposition, the structure of the tear film and its stability were mainly influenced by the tear film lipid composition. The ocular integrity also appeared to be influenced by the nature of the lipid deposition. Potential markers within the lipid species have been identified and could be applied as follows: When required in order to identify a problematic wearer or to match the contact lens material to the contact lens wearer, tear samples collected by the clinician could be dispatched to an analytical laboratory where lipid analysis could be carried out by HPLC. A colorimetric kit based on the lipid markers could also be developed and used by clinician directly in the practice; such a kit would involve tear sampling and classification according to the colour into "Problem", "Border line" and "Good" contact lens wearers groups. A test kit would also have wider scope for marketing in other areas such as general dry-eye pathology.

Enzymes are proteins that catalyze biochemical reactions and their use report multiple advantages, as they can be very selective, low polluting (biodegradable), cheap and allow working in mild conditions compared with traditional non enzymatic processes. Despite their enormous benefits, their applications at the industrial level are still limited, mainly due to low productivity, low substrate tolerance (too specifics) and poor resistance to the industrial conditions, and for this reason, developing enhanced enzymes by means of enzyme engineering is a central research field nowadays. Notably, the application of computational chemistry in the field of enzyme engineering is increasing due to improvements in hardware and software. Moreover, this process is fast and low-priced and therefore, profitable for the application to the real problems that face industry. Therefore, motivated by this progress, the main goal of this thesis is the development of computational strategies that allow designing and evaluating modifications in enzymes, also aiming to obtain results quickly and inexpensively. This purpose was reached by the combination of different in silico methodologies that were further supported by experimental data in an interactive feedback process. As a result of this thesis, the enzymatic process in heme peroxidases was first satisfactorily described by dividing the process into two steps (from the ligand diffusion to the chemical reaction), using a combination of different computational techniques. The first step, which involves the protein/ligand recognition, was characterized with different molecular mechanics based techniques (MD, Docking and MC-PELE). On the other hand, the chemical reaction (including bond formation and electron transfer) was reproduced using QM based methods by means of energy calculation, spin density characterization, e-coupling calculations and QM/MM e-pathways descriptions. Following this procedure, the oxidation of veratryl alcohol by the enzyme lignin peroxidase was also characterized. Moreover, regarding the e-coupling calculations, a server to compute this vale faster and easy was developed. In the second part of the thesis, the results demonstrated that our protocol could reliably describe and predict enzymatic functions, not only in native enzymes but also in mutated ones, which results were in agreement with experimental data. For example, the structural implications over the reactivity in manganese peroxidase and its engineered variant obtained by cutting the last terminal residues were identified and characterized by the combination of Monte Carlo simulations (PELE) and electronic coupling calculations. The pH resistance in the mutant 2-1B (which was obtained experimentally by random directed evolution) in contrast with the wild type versatile peroxidase, were also rationalized by molecular dynamics, where the residues in the heme environment presented different conformation due to the mutations introduced, resulting in different pH resistance. Interestingly, the last part of the thesis was centered of engineering heme peroxidases. We engineered a peroxidase from in silico predictions to elucidate the long range electron transfer processes involved in the oxidation of the substrate veratryl alcohol by the enzyme versatile peroxidase. In this work we identify the key residues involved in the process, with further applications in engineering enhanced enzymes. Moreover, an enhanced manganese peroxidase mutant from a complete computational study was designed. First, the ligand diffusion study allowed finding the key aminoacids in the substrate/enzyme recognition and binding. Then, the chemical reaction in terms of the oxidation probability and kinetic constant for the proposed mutant were estimated, and the results were in agreement with experimental data. Therefore, the work of this thesis probed that computational biophysics and biochemistry are promising and valuable tools for enzyme engineering. In particular, in the field of rational design of heme peroxidases, they provide relevant information about the enzymatic mechanism and allow designing new enzymes, as well as checking their improvement/worsening, in an efficient way.
Las enzimas son proteínas que catalizan reacciones bioquímicas y cuyo uso aporta múltiples ventajas, ya que son en general muy selectivas, poco contaminantes (biodegradables), baratas y permiten trabajar en condiciones suaves, en comparación con los procesos tradicionales no enzimáticos. A pesar de sus enormes beneficios, sus aplicaciones a nivel industrial son todavía limitadas, debido principalmente a la baja productividad, baja tolerancia al sustrato (demasiado específicos) y una escasa resistencia a las condiciones industriales en general, y por esta razón el desarrollo de enzimas mejoradas es un campo de investigación muy importante hoy en día. En particular, la aplicación de la química computacional en el campo de la ingeniería de enzimas está en aumento debido a las mejoras en hardware y software. Motivado por este progreso, el objetivo principal de esta tesis es el desarrollo de estrategias de cálculo que, mediante la combinación de diferentes metodologías in silico permitan diseñar y evaluar modificaciones en las enzimas, centrándonos en la obtención de resultados de forma rápida y económica. La primera parte de la tesis está centrada en la descripción del mecanismo enzimático entendido como un proceso de dos pasos que incluyen la difusión ligando y la reacción química, mediante una combinación de diferentes técnicas computacionales. El primer paso, que implica el reconocimiento de la proteína / ligando, se caracterizó con diferentes técnicas basadas en la mecánica molecular (dinámica molecular, docking y Monte Carlo- PELE). Por otro lado, la reacción química (incluyendo la formación de enlaces y la transferencia de electrones) se simuló usando métodos basados en mecánica cuántica por medio de cálculos de energía, la caracterización del spin o cálculos de acoplamiento electrónico. Por ejemplo, siguiendo este procedimiento, se caracterizó la oxidación de alcohol veratrílico por medio de la enzima lignin peroxidasa. Además, con el objetivo de poder calcular los acoplamientos electrónicos de una manera más rápida y fácil, se desarrolló un servidor web: ecoupling server. En la segunda parte de la tesis, los resultados demostraron que el protocolo anterior podría describir funciones enzimáticas no sólo en las especies nativas sino también en las variantes mutadas. Por ejemplo, se identificaron las implicaciones estructurales de la reactividad en una manganeso peroxidasa de la subfamilia larga y su variante modificada obtenida mediante la reducción de los últimos residuos terminales gracias al estudio de simulaciones de Monte Carlo (PELE) y cálculos de acoplamiento electrónico. Además, la resistencia a pH ácido en el mutante 2-1B (que se había obtenido previamente por evolución dirigida al azar) se comparó con la especie nativa y también se racionalizó por dinámica molecular, donde se observó que los residuos del entorno del hemo presentaban diferente conformación debido a las mutaciones introducidas, resultando en una diferente resistencia a pH ácido. La última parte de la tesis se centra en la ingeniería racional de hemo peroxidasas. A partir de predicciones in silico se diseñaron variantes de peroxidasa versátil para tratar de entender los procesos de transferencia electrónica de largo alcance que participan en la oxidación del sustrato de alcohol veratrílico, mediante la identificación de los residuos intermedios involucrados en el proceso. Además, a partir de un estudio computacional completo, se diseñó un mutante mejorado de manganeso peroxidasa, cuyos valores cinéticos estimados computacionalmente se encontraban de acuerdo con los resultados experimentales. En conclusión, en esta tesis se ilustra cómo los métodos biofísicos y bioquímicos computacionales son herramientas prometedoras y valiosas para la ingeniería de enzimas, en particular en el campo del diseño racional.

Complementary Metal-Oxide-Semiconductor (CMOS) based sensor systems continue to play an important role in various biomedical, chemical, industrial, food safety, national security and defense, and environmental applications. This is because CMOS fabrication processes would allow one to produce miniaturized systems with low cost, low power and mass-producibility. This dissertation deals with the development of integrated CMOS optical and CMOS electrochemical sensor microsystems for biochemical sensing applications for monitoring of oxygen and bacteria. The CMOS optical sensor microsystems developed as part of this dissertation are based on the luminometric intensity and lifetime measurement techniques and use sol-gel derived xerogel based sensing elements to encapsulate analyte specific fluorophores in micro-/nano- scale porous structures. The majority of the research efforts have concentrated on the development of oxygen sensor microsystems using CMOS detection and processing circuits. A phase luminometric oxygen sensor system is developed with a novel circuit technique applied to enhance the detection sensitivity performance. Multi-sensor microsystem based on sensor microarrays and custom designed low power CMOS imager is developed to investigate the sensor microarrays imaging and temperature effects on sensor microarrays. Finally, CMOS based Direct Time Interval Measurement (DTIM) method is proposed as a new technique to perform direct luminescence lifetime measurement. The CMOS electrochemical microsystem developed as a part of this dissertation is based on the conductometric measurement technique and employs bacteriophages as the biological recognition elements (bioreceptors). We focus on the development of CMOS conductometric integrated circuit system which converts the resistance input to a digital output signal. We proposed a novel bacteria activity monitoring system by integrating CMOS conductometric IC with bacteriophages. In future, based on the prin
Les capteurs en semi-conducteur à oxyde de métal complémentaire (CMOS) continuent d'être cruciaux dans les domaines biomédicaux, chimiques, industriels, environnementaux et autres. La popularité des systèmes CMOS est attribuée à leurs processus de fabrication qui permet de produire des systèmes miniaturisés facile à reproduire en masses à un bas prix et à une basse consommation propre. Cette thèse de doctorat a pour sujet le développement de microsystèmes optiques CMOS intégrés et des capteurs électrochimiques pour surveiller les cultures bactériennes et le taux d'oxygène. Le capteur optique CMOS développé pour cette thèse est basé sur l'intensité luminométrique et les mesures de durée de vie. Le capteur utilise le sol-gel qui est dérivé des éléments de détection basé sur le Xerogel qui encapsule des fluorophores dans des structures poreuse sur l'échelle micro et nano. La plupart des efforts de recherche sont concentrés sur le développement d'un capteur d'oxygène qui utilise un capteur CMOS et un circuit d'analyse. Dans cette thèse un capteur luminométrique est développé avec un nouveau circuit qui améliore la sensibilité de la détection. Le microsystème de plusieurs capteurs en micro-matrice et d'imageurs a basses consommation personnalisés examine la formation d'image et les effets de la température sur les capteurs en micro-matrice. En plus, nous proposons l'utilisation d'une nouvelles technique CMOS pour mesurer la durée de vie de la luminescence suivant la méthode Direct Time Interval Measurement (DTIM). Le microsystème électrochimique développé pour cette thèse est basé sur des mesures conductométriques et utilise des bactériophages pour la reconnaissance biologique. Le système conductometrique intégré CMOS converti la résistance à l'entrée à un signal numérique a la sortie. Nous proposons une nouvelle méthode pour surveiller l'activité bactérienne en intégrant un circuit intégré

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 50-55).
In this thesis, we developed a microfluidic-based platform for the generation of physiologically relevant 3D lymphatic capillaries. This tissue engineering platform allowed us to probe the vascularization dynamics of lymphatic endothelial cells under a highly controlled microenvironment and isolate the effects of biochemical and biophysical inputs. Under this precise control over local extracellular factors, we studied the angiogenic response of lymphatic endothelial cells to different soluble pro-angiogenic factors, which accordingly induced different sprout formation dynamics . We also controlled the vascularization behaviors of lymphatics by modulating the intrinsic composition of the extracellular matrix. Finally, we explored the influence of mechanical stimuli, more specifically interstitial flow, on the formation of lymphatic sprouts to which we observe a dependency on the synergistic stimulus from the presence of pro-angiogenic factors while inducing interstitial flow. In summary, these results elucidate the physiological process of lymphatic angiogenesis and explores the individual contribution of local cues in the cellular microenvironment during this vascular morphogenesis phenomenon. Additionally, the development of this platform has potential applications for physiological studies regarding lymphatic function, regenerative medicine and drug development for lymphatic-associated diseases..
by Jean Carlos Serrano
S.M.
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.
Includes bibliographical references (leaves 97-103).
The mechanical integrity of cervical tissue is crucial for maintaining a healthy gestation. Altered tissue biochemistry can cause drastic changes in the mechanical properties of the cervix and contribute to premature cervical dilation and delivery. This work presents an investigation of the mechanical and biochemical properties of cervical samples from human hysterectomy specimens. Three clinical cases were investigated: non-pregnant hysterectomy patients with previous vaginal deliveries, non-pregnant hysterectomy patients with no previous vaginal deliveries, and pregnant hysterectomy patients at time of cesarean section. Tissue samples for the three clinical cases were tested mechanically and analyzed for biochemical content. Tissue samples were tested in confined and unconfined compression, and biochemical assays measured cervical tissue hydration, collagen content, collagen extractability, and sulfated glycosaminoglycan content. The non-pregnant tissue was found to be significantly stiffer than the pregnant tissue. Collagen extractability was significantly higher in the pregnant tissue. This study represents a first important step towards the attainment of an improved understanding of the complex interplay between the molecular structure of cervical tissue and its macroscopic mechanical properties.
by Kristin M. Myers.
S.M.

One goal of systems biology is to predict and modify the behavior of biological networks by accurately monitoring and modeling their responses to certain types of perturbations. The construction of mathematical models based on observation of these responses, referred to as reverse engineering, is an important step in elucidating the structure and dynamics of such networks. Continuous models, described by systems of differential equations, have been used to reverse engineer biochemical networks. Of increasing interest is the use of discrete models, which may provide a conceptual description of the network. In this dissertation we introduce a discrete modeling approach, rooted in computational algebra, to reverse-engineer networks from experimental time series data. The algebraic method uses algorithmic tools, including Groebner-basis techniques, to build the set of all discrete models that fit time series data and to select minimal models from this set. The models used in this work are discrete-time finite dynamical systems, which, when defined over a finite field, are described by systems of polynomial functions. We present novel reverse-engineering algorithms for discrete models, where each algorithm is suitable for different amounts and types of data. We demonstrate the effectiveness of the algorithms on simulated networks and conclude with a description of an ongoing project to reverse-engineer a real gene regulatory network in yeast.
Ph. D.

Biological systems exhibit complex behaviors through coordinated responses of individual biological components. With the advent of genome-scale techniques, one focus has been to develop methods to model interactions between components to accurately describe intact system function. Mathematical modeling techniques such as constraint-based modeling, agent-based modeling, cellular automata (CA) modeling and differential equation modeling are employed as computational tools to study biological phenomenon. We have shown that cellular automata simulations can be used as a computational tool for 12 predicting the dynamics of biological systems with stochastic behavior. The basic premise for the research was the observations made during a study of biologically important feed-forward motifs where CA simulations were compared with differential equation simulations. It was shown for classes of structural motifs with feed-forward architecture that network topology affects the overall rate of a process in a quantitatively predictable manner. The study which comprised of CA simulations compared with differential equation modeling show reasonable agreement in the predictability of system dynamics, which provided enough support to model biological systems at cellular level to observe dynamic system evolution. The great promise shown by CA simulations to model biochemical systems was then employed to elucidate evolutionary clues as to why biological networks show preference for certain types of motifs and preserve them with higher frequency during evolution. It was followed by modeling apoptotic networks to shed light on the efficacy of inhibitors and to model cellulose hydrolysis to evaluate efficiency of different enzyme systems used by cellulytic bacteria.

This thesis describes two main sections of work, an examination of a commercial product, Intrasite Gel, and the development of an algorithm for variable selection using projected latent structures.Following on from the successful development of a variable selection procedure for multivariate linear regression this work looks at transferring this idea for use with projected latent structures. The first part of this thesis will show how the variable selection algorithm was developed and used with three different data sets. The algorithm will be shown to be superior to standard projected latent structures, for linear multi-component data. Although the final algorithm developed requires considerable computing resources to carry out this is compensated for by significantly improved model predictions and robustness. The final algorithm developed is written to run using MATLAB on any computer platform that supports this application, though the principles of operation could be transferred to another method of execution, for example custom code written in C or Pascal. The approach used in the development of this method is that the ability of the model to predict unknownsamples is of far greater importance than the internal performance of the model. All the assessments of the procedures developed are based on the ability of the model to predict accurately and precisely samples that were not presented to the model during the training stage.The second section of this thesis is concerned with the study of Intrasite Gel, produced by Smith & Nephew Ltd. Hull. The material in question is a medical device intended to assist in the treatment and healing of wounds that are necrotic, sloughy or granulating. The product is characterised by its ability to maintain moisture equilibrium in a wound environment and to provide a suitable medium to encourage the growth of new cell tissue. Medical devices require registration, and as part of that registration a number of tests are made on samples to ensure that the material meets the required specifications. There was some concern at Smith & Nephew that the tests they were required to carry out as part of the device registration were not providing appropriate information about the product. Of particular interest was the fluid absorption property as it was suspected that the test has a large amount of random error associated with it and an investigation was required to examine this test and to provide an alternative procedure should the fluid absorption test prove inadequate. Also of interest to Smith & Nephew was the issue of sampling frequency, as it was felt that this should also be examined to determine whether the correct rate of sampling to ensure product quality was being carried out. The work reported here shows that the fluid absorption test as it stands is insufficient to the task of monitoring this property of Intrasite gel and that an alternative test should be considered. This work also showed that current sampling rate was too high and that the high sampling rate may in fact cause misleading assumptions as to the stability and quality of the product.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references.
The epithelial cell-extracellular matrix interface primarily comprises of complex glycans and glycoconjugates. The widespread distribution of these glycans on the epithelial cell surface makes them ideal targets for interaction with microbial pathogens. In this thesis, a framework of integrated approaches was developed to characterize the structure-function relationships of host cell surface glycans and examine their role in mediating hostpathogen interactions. The first part of the thesis involves a study of the effect of secreted bacterial sphingomyelinases on the epithelial cell surface proteoglycan (a large glycan- protein conjugate), syndecan-1 and on epithelial tight junctions. The findings presented in this work suggest mechanisms by which sphingomyelinases could enhance bacterial virulence by regulating epithelial cell function. The second part of the thesis investigates the glycan binding requirements that govern the human adaptation and transmission of influenza A viruses by characterizing the molecular interactions between sialylated glycan-receptors and viral hemagglutinin (HA). The study puts forth the concept that the topology or shape (going beyond the chemical c2-3 versus a2-6 sialic acid linkage) adopted by the sialylated glycans is the critical determinant for efficient human adaptation of these viruses. In conclusion, this thesis provides insights into the molecular mechanisms of host-pathogen interactions and enables development of improved strategies for targeted antimicrobial therapies.
by Aarthi Chandrasekaran.
Ph.D.

The predictive performance of supervised learning methods relies on large amounts of labeled data. Data sets used in Quantitative Structure Activity Relationship modeling often contain a limited amount of labeled data, while unlabeled data is abundant. Semi-supervised learning can improve the performance of supervised methods by incorporating a larger set of unlabeled samples with fewer labeled instances. A semi-supervised learning method known as Label Spreading was compared to a Random Forest in its effectiveness for correctly classifying the binding properties of molecules on ten different sets of compounds. Label Spreading using a k-Nearest Neighbors (LS-KNN) kernel was found to, on average, outperform the Random Forest. Using a randomly sampled labeled data set of sizes 50 and 100, LS-KNN achieved a mean accuracy of 4.03% and 1.97% higher than that of the Random Forest.The outcome was similar for the mean area under the Receiver Operating Characteristic curve (AUC). For large sets of labeled data, the performances between the methods were indistinguishable. It was also found that sampling labeled data from generated clusters using a k-Means clustering algorithm, as opposed to random sampling, increased the performance of all applied methods. For a labeled data set ofsize 50, Label Spreading using a Radial Basis Function kernel increased its meanaccuracy and AUC by 7.52% and 3.08%, respectively, when sampling from clusters. In conclusion, semi-supervised learning could be beneficial when applied to similar modeling scenarios. However, the improvements heavily depend on the underlying data, suggesting that there is no one-size-fits-all method.

This study is concerned with the analysis of tear proteins, paying particular attention to the state of the tears (e.g. non-stimulated, reflex, closed), created during sampling, and to assess their interactions with hydrogel contact lenses. The work has involved the use of a variety of biochemical and immunological analytical techniques for the measurement of proteins, (a), in tears, (b), on the contact lens, and (c), in the eluate of extracted lenses. Although a diverse range of tear components may contribute to contact lens spoilation, proteins were of particular interest in this study because of their theoretical potential for producing immunological reactions. Although normal host proteins in their natural state are generally not treated as dangerous or non-self, those which undergo denaturation or suffer a conformational change may provoke an excessive and unnecessary immune response. A novel on-lens cell based assay has been developed and exploited in order to study the role of the ubiquitous cell adhesion glycoprotein, vitronectin, in tears and contact lens wear under various parameters. Vitronectin, whose levels are known to increase in the closed eye environment and shown here to increase during contact lens wear, is an important immunoregulatory protein and may be a prominent marker of inflammatory activity. Difficulties arise when attempting to extract proteins from the contact lens in order to examine the individual nature of the proteins involved. These problems were partly alleviated with the use of the on-lens cell assay and a UV spectrophotometry assay, which can analyse the lens surface and bulk respectively, the latter yielding only total protein values. Various lens extraction methods were investigated to remove protein from the lens and the most efficient was employed in the analysis of lens extracts. Counter immunoelectrophoresis, an immunodiffusion assay, was then applied to the analysis of albumin, lactoferrin, IgA and IgG in the resultant eluates.

Crystallization from supercritical fluids was studied as a nontoxic, noncontaminating alternative to conventional techniques for purification and size manipulation of pharmaceutical solids. To proceed with crystallization solubilities of several pharmaceutical compounds in supercritical carbon dioxide were experimentally determined and modeled using solid-vapor phase equilibria. The compounds studied included benzoic acid, salicylic acid, aspirin, griseofulvin, and digoxin among others. A high pressure crystallizer was constructed and operated in batch and continuous modes. Supersaturation was generated by various schemes, such as optimal pressure reduction and salting-out. It was determined that, depending on the crystallization scheme, particles can be produced at submicron as well as large sizes. Particle nucleation and growth rates from saturated supercritical solutions were estimated and the product size distributions were simulated using the population balance theory. Observations were made regarding habit and morphology of particles nucleated and grown at supercritical conditions.

Thesis (PhD) - Faculty of Life and Social Sciences, Swinburne University of Technology, 2009.
Submitted for the degree of Doctor of Philosophy, [Faculty of Life and Social Sciences], Swinburne University of Technology - 2009. Typescript. Includes bibliographical references (p. 210-227)

Thesis (Ph. D.)--Chemical Engineering, Georgia Institute of Technology, 2009.
Committee Co-Chair: Eckert, Charles; Committee Co-Chair: Liotta, Charles; Committee Member: Bommarius, Andreas; Committee Member: Fernández, Facundo; Committee Member: Lu, Hang. Part of the SMARTech Electronic Thesis and Dissertation Collection.

In this Thesis, we present two systems for gaseous oxygen (O2) sensing. First, we describe a compact luminescent sensor microsystem that is based on the direct integration of sensor elements with a polymeric optical filter and placed on a low power Complementary Metal-Oxide Semiconductor (CMOS) imager Integrated Circuit (IC). The second system is a hand-held scale phase fluorometric system. This system is based on a new single-chip integrated circuit that can perform the activities of sinusoidal signal generation using Direct Digital Synthesis and phase angle extraction of the detection luminescence signal from the sensor films using Discrete Fourier Transform. For O2 sensing, the sensors operate on the measurement of excited-state emission intensity of O2-sensitive luminophores tris(4,7-diphenyl-1,10- phenanthroline) ruthenium(II) ([Ru(dpp)3]2+) encapsulated in sol-gel derived xerogel thin-films. For the compact luminescent sensor microsystem, we incorporate a polymeric optical filter that is made with polydimethylsiloxane (PDMS) that is mixed with color die Sudan-II. The surface of the PDMS filter is molded to incorporate arrays of pyramidal microstructures that serve to focus the optical sensor signals on to the photodetectors. The xerogel sensor arrays are contact printed on top of the PDMS pyramidal lens-like microstructures. The CMOS imager uses a 32x32 (1024 elements) array of active pixel sensors and each pixel includes a high-gain phototransistor to convert the detected optical signals into electrical currents. Correlated double sampling circuit, pixel address, digital control and signal integration circuits are also implemented on-chip. The CMOS imager data is read out as a serial coded signal. The developed CMOS sensor microsystems provide a useful platform for the development of miniaturized, analytically reliable, and accurate optical chemical gaseous and aqueous sensors.
Dans cette thèse, nous présentons deux systèmes pour détecter l'oxygène gazeux (O2). Tout d'abord, nous décrivons un microsystème compact à senseur luminescent qui est basée sur l'intégration directe d'éléments de senseur avec un filtre optique polymère qui est placé sur un imageur circuits intégrés (CI) à faible énergie de type Complementary metal oxide semi-conductor (CMOS). Le second système est un système portatif qui permet de détecter la différence de phase fluorométrique. Ce système est basé sur un circuit intégré à puce unique qui permet de générer des signaux sinusoïdal en utilisant la synthèse directe de signaux digitaux et l'extraction de l'angle de phase du signal luminescent, provenant des films du senseur, en utilisant des transformées de Fourier discrète sur ce signal. Pour la détection du dioxygène, les senseurs mesure l'intensité d'émission des luminophores tris (4,7-diphényl-1, 10 - phénanthroline) ruthénium (II) ([Ru(dpp)3]2+) à l'état excité encapsulés dans des sol-gel provenant de micro film xérogel. Le microsystème compact à senseur luminescent comprend un filtre optique polymère à base de polydiméthylsiloxane (PDMS), qui est mélangée avec le colorant Soudan-II. La surface du filtre PDMS est moulée pour ainsi incorporer les réseaux de microstructures pyramidales qui servent à concentrer les signaux des senseurs optiques sur les photodétecteurs. Les réseaux de senseur à base de xérogel sont imprimés par contact sur le dessus des microstructures PDMS pyramidales qui agissant comme des lentilles. L'imageur CMOS utilise une matrice de 32x32 (1024 éléments) servant de pixels actifs et chaque un de ces pixels comporte un phototransistor à gain élevé pour convertir les signaux détectés optiques en courants électriques. La corrélation de circuit d'échantillonnage double, l'adresse de pixel, et les circuits de commande numérique d'intégration de signaux sont également résolue par la puce. Les données sont lues par l'imageur en tant que signaux codé en série. Les capteurs CMOS fournissent une plateforme utile pour le développement des systèmes miniaturisés pour l'analyse fiable et précis des composantes chimiques gazeuse et aqueuse par des moyens optiques.

BiOWiSHTM – Aqua is a blend of preserved multi-bacteria culture with the capability of denitrification. If an anaerobic nitrate rich activation procedure is used instead of the standard aerobic activation procedure, the denitrification rate is increased by 28 percent under the conditions of 30°C, 1C:1N, 200mg/L of carbon, and 200mg/L nitrogen.

The potential value of the chitin biomass (e.g. food waste) is recently considered being ignored by landfill. Chitin can be a potential cheap carbon source for converting into value-added chemicals by microorganisms. Serratia marcescens is a chitinolytic bacterium that harbors endogenous chitinase systems. With goals of characterzing S. marcescens chitinolytic capabilities and applying S. marcescens to chemical production from chitin, my dissertation main content includes five chapters: 1) Chapter 1 highlights background information of chitin source, S. marcescens and potential metabolic engineering targets using chitin as a substrate; 2) Chapter 2 demonstrates that ChiR is a key regulator in regulating 9 chitinase-related genes in S. marcescens Db11 and manipulation of chiR can be a useful and efficient genetic target to enhance chitin utilization; 3) Chapter 3 reports the production of N-acetylneuraminic acid (Neu5Ac) from chitin by a bottom-up approach of engineering the nonconventional chitinolytic bacterium, Serratia marcescens, including native constitutive promoter characterization and transcriptional and translational pathway balancing; 4) Chapter 4 describes improvement of S. marcescens chitinolytic capability by an adaptive evolution approach; 5) Chapter 5 elucidates S. marcescens intracellular metabolite profile using a constraint-based genome-scale metabolic model (iSR929) based on genomic annotation of S. marcescens Db11. Overall, the dissertation work is the first report of demonstrating the concept of chitin-based CBP using S. marcescens and the computational model and genetic molecular tools developed in this dissertation are valuable but not limited to design-build-test of S. marcescens for contributing to the field of biological science and metabolic engineering applications.

A biosensor is an analytical device integrating a biological element and a physicochemical transducer that convert a biological response into a measurable signal. The advantages of biosensors include low cost, small size, quick, sensitivity and selectivity greater than the conventional instruments. Biosensors have a wide range of applications ranging from clinical diagnostics through to environmental monitoring, agriculture industry, et al. The different types of biosensors are classified based on the sensor device as well as the biological material. Biosensors can be broadly classified into (piezoelectric, etc.), electrochemical biosensors (potentiometric, amperometric, etc.), and optical types of biosensors (fiber optics, etc.). Here, we introduce a novel microfluidics-integrated biosensor platform system that can be flexibly adapted to form individual biosensors for different applications. In this dissertation, we present five examples of different emerging areas with this biosensor system including anti-cancer drug screening, glucose monitoring, heavy metal elements measurement, obesity healthcare, and waterborne pathogen DNA detection. These micro-biosensors have great potential to be further developed to emerging portable sensing devices especially for the uses in the developing and undeveloped world. At the last chapter, Raman spectroscopy applied to assess gestational status and the potential for pregnancy complications is presented and discussed. This technique could significantly benefit animal reproduction.

Biocatalysts offer advantages over their chemical counterparts in terms of their high enantioselectivity and the opportunity to develop more environmentally friendly processes. However, the widespread adoption of biocatalytic processes is hampered by the long development times for enzymes with novel and sufficient activity and adequate stability under operating conditions. Protein engineering, while extremely useful for modifying the properties of protein catalysts in select cases, still cannot be performed rapidly enough for many applications. In order for biocatalysts to become a competitive alternative to chemical catalysts, new tools to make the tailoring of biocatalysts by protein engineering methods speedier and more efficient are necessary. The aim of this work was to develop methods to aid in the faster production of novel biocatalysts. Protein engineering involves two steps: the generation of diversity and the screening or selection of variants with the desired properties. Both of these must be targeted to create a faster protein engineering process. In the case of the former, this work sought to clone and overexpress some template enzymes which would create smaller, more manageable libraries of mutants with a higher likelihood of function by the manipulation of a few focused amino acid residues. For the latter, this work developed and validated a Monte-Carlo simulation model of pooling to increase screening throughput and created a set of vectors to aid in high-throughput screening by eliminating unwanted mutants from the assay procedure entirely.

Thesis (Ph. D.)--Chemistry and Biochemistry, Georgia Institute of Technology, 2006.
Allen M. Orville, Committee Member ; Sheldon W. May, Committee Member ; Jung H. Choi, Committee Member ; Mostafa A. El-Sayed, Committee Member ; Donald F. Doyle, Committee Chair.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 169-181).
The large-scale simulation of biochemical reaction networks in cells is important in pathway discovery in medicine, in analyzing complex cell function in systems biology, and in the design of synthetic biological circuits in living cells. However, cells can undergo many trillions of reactions over just an hour with multi-scale interacting feedback loops that manifest complex dynamics; their pathways exhibit non-modular behavior or loading; they exhibit high levels of stochasticity (noise) that require ex- pensive Gillespie algorithms and random-number generation for accurate simulations; and, they routinely operate with nonlinear statics and dynamics. Hence, such simulations are extremely computationally intensive and have remained an important bottleneck in computational biology over decades. By exploiting common mathematical laws between electronics and chemistry, this thesis demonstrates that digitally programmable analog integrated-circuit 'cytomorphic' chips can efficiently run stochastic simulations of complex molecular reaction networks in cells. In a proof-of-concept demonstration, we show that 0.35 [mu]m BiC- MOS cytomorphic gene and protein chips that interact via molecular data packets with FPGAs (Field Programmable Gate Arrays) to simulate networks involving up to 1,400 biochemical reactions can achieve a 700x speedup over COPASI, an efficient bio- chemical network simulator. They can also achieve a 30,000x speedup over MATLAB. The cytomorphic chips operate over five orders of magnitude of input concentration; they enable low-copy-number stochastic simulations by amplifying analog thermal noise that is consistent with Gillespie simulations; they represent non-modular load- ing effects and complex dynamics; and, they simulate zeroth, first, and second-order linear and nonlinear gene-protein networks with arbitrary parameters and network connectivity that can be flexibly digitally programmed. We demonstrate successful stochastic simulation of a p53 cancer pathway and glycolytic oscillations that are consistent with results obtained from conventional digital computer simulations, which are based on experimental data. We show that unlike conventional digital solutions, an increase in network scale or molecular population size does not compromise the simulation speed and accuracy of our completely parallel cytomorphic system. Thus, commonly used circuit improvements to future chips in our digital-to-analog converters, noise generators, and biasing circuits can enable further orders of magnitude of speedup, estimated to be a million fold for large-scale networks.
by Sung Sik Woo.
Ph. D.

Declining antibiotic discovery and flourishing antibiotic resistance have led to a modern antibiotic crisis which threatens to compromise our ability to treat infectious disease. Consequently, there is significant interest in developing new antibiotics with novel modes of action and chemical properties. Ribosomally synthesized and post-translationally modified peptides (RiPPs) are natural compounds with the appealing attributes of being derived directly from a genetic template while possessing numerous exotic chemical features that contribute to stability and antimicrobial activity. Abundant in nature, their diverse range of biological activities makes them excellent prospects for antibiotic development. Thiopeptides, a RiPP family rich in chemical complexity, represent a particularly promising example. Characterized by post-translationally formed sulfur- and nitrogen-containing heterocycles, more than 100 different thiopeptides have been identified from various cultivable bacterial producers, and the mining of genomic and metagenomic data promises to uncover many more chemical species that have eluded discovery by conventional means. These peptides are potent inhibitors of bacterial protein synthesis and have been shown effective against many drug-resistant pathogens. Despite these attractive properties, therapeutic applications have been limited by the lack of an efficient synthetic route and poor aqueous solubility. Both of these challenges would be greatly alleviated by a more complete understanding of thiopeptide biosynthesis and improved systems for analysis and engineering. Here we describe the characterization of a new thiopeptide gene cluster, which encodes the archetypal thiopeptide micrococcin P1. We describe the identification of the bioactive product and detail the mechanism of immunity in the producing strain. We also describe efforts to engineer this pathway for heterologous expression in Bacillus subtilis. Using this platform, we have been able to dissect this intricate biosynthetic pathway and parse the order and timing of the processing events involved in peptide maturation. The knowledge gained from these studies will inform future efforts to adapt thiopeptides for therapeutic use, and guide efforts to engineer unnatural compounds using the exotic enzymology employed by thiopeptide producing bacteria.

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.
Includes bibliographical references (leaf 52).
Isothermal titration calorimetry is a technique used to measure the enthalpy change associated with a molecular binding interaction. From these data, the binding constant for the reaction can be determined. In the scope of a larger project to design a high sensitivity instrument for collecting such data, the current methods in isothermal titration calorimetry were investigated. Further calorimetric experience was acquired by designing a large scale calorimetric device. Dilution reactions with dimethyl sulfoxide and water were conducted to measure the excess enthalpy of binding. The inaccuracy of these measurements necessitated the more careful design of an isoperibol calorimeter. This calorimeter was modeled was an arrangement of coupled thermal masses and capacitances in order to fully understand its transient response to a thermal input. Dilution reactions and a neutralization reaction with HCl and NH40H were performed on the system and the results were used to make recommendations for the design of the future high sensitivity device.
by Ovid Charles Amadi.
S.B.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 96-104).
Identification of effective drug targets to intervene, either as single agent therapy or in combination, is a critical question in drug development. As complexity of disease like cancer is revealed, it has become clear that a holistic network approach is needed to identify drug targets that are specially positioned to provide desired leverage on disease phenotypes. In this thesis we develop a computational framework to exhaustively evaluate target behaviors in biochemical network, either as single agent or combination therapies. We present our single target therapy work as a problem of identifying good places to intervene in a network. We quantify a relationship between how interventions at different places in network affect an output of interest. We use this quantitative relationship between target inhibited and output of interest as a metric to compare targets. In network analyzed here, most targets show a sub-linear behavior where a large percentage of targeted molecule needs to be inhibited to see a small change on output. The other key observation is that targets at the top of the network exerted relatively small control compared to the targets at the bottom of the network. In the combination therapy work we study how combination of drug concentrations affect network output of interest compared to when one of the drugs was given alone at equivalent concentrations. By adapting the definitions of additive, synergistic, and antagonistic combination behaviors developed by Ting Chao-Chou (Chou TC, Talalay P (1984), Advances in enzyme regulation 22: 27-55) for our system and systematically perturbing biochemical pathway, we explore the range of combination behaviors for all plausible combination targets. This holistic approach reveals that most target combinations show additive behaviors. Synergistic, and antagonistic behaviors are rare. Even when combinations are classified as synergistic or antagonistic, they show this behavior only in a small range of the inhibitor concentrations. This work is developed in a particular variant of the epidermal growth factor (EGF) receptor pathway for which a detailed mathematical model was first proposed by Schoeberl et al. Computational framework developed in this work is applicable to any biochemical network.
by Nirmala Paudel.
Ph. D.