Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, June 2011. / "April 2011." Cataloged from PDF version of thesis. / Includes bibliographical references (p. 181-189). / Geological sequestration has been proposed as a way to remove CO₂ from the atmosphere by injecting it into deep saline aquifers. Detecting leaks to the atmosphere will be important for ensuring safety and effectiveness of storage. However, a standard set of tools for monitoring does not yet exist. The basic problem for leak detection - and eventually for the inverse problem of determining where and how big a leak is given measurements - is to detect shifts in the mean of atmospheric CO₂ data. Because the data are uncertain, statistical approaches are necessary. The traditional way to detect a shift would be to apply a hypothesis test, such as Z- or t-tests, directly to the data. These methods implicitly assume the data are Gaussian and independent. Analysis of atmospheric CO 2 data suggests these assumptions are often poor. The data are characterized by a high degree of variability, are non-Gaussian, and exhibit obvious systematic trends. Simple Z- or t-tests will lead to higher false positive rates than desired by the operator. Therefore Bayesian methods and methods for handling autocorrelation will be needed to control false positives. A model-based framework for shift detection is introduced that is capable of coping with non-Gaussian data and autocorrelation. Given baseline data, the framework estimates parameters and chooses the best model. When new data arrive, they are compared to forecasts of the baseline model and testing is performed to determine if a shift is present. The key questions are, how to estimate parameters? Which model to use for detrending? And how to test for shifts? The framework is applied to atmospheric CO₂ data from three existing monitoring sites: Mauna Loa Observatory in Hawaii, Harvard Forest in central Massachusetts, and a site from the Salt Lake CO₂ Network in Utah. These sites have been chosen to represent a spectrum of possible monitoring scenarios. The data exhibit obvious trends, including interannual growth and seasonal cycles. Several physical models are proposed for capturing interannual and seasonal trends in atmospheric CO₂ data. The simplest model correlates increases in atmospheric CO₂ with global annual emissions of CO₂ from fossil fuel combustion. Solar radiation and leaf area index models are proposed as alternative ways to explain seasonality in the data. Quantitative normality tests reject normality of the CO₂ data and the seasonal models proposed are nonlinear. A simple reaction kinetics example demonstrates that nonlinearity in the detrending model can lead to non-Gaussian posterior distributions. Therefore Bayesian methods estimation will be necessary. Here, nonlinear least squares is used to reduce computational effort. A Bayesian method of model selection called the deviance information criterion (DIC) is introduced as a way to avoid overfitting. DIC is used to choose between the proposed models and it is determined that a model using a straight line to represent emissions driven growth, the solar radiation model and a 6-month harmonic term does the best job of explaining the data. Improving the model is shown to have two important consequences: reduced variability in the residuals and reduced autocorrelation. / (cont.) Variability in the residuals translates into uncertainty in CO₂ forecasts. Thus by reducing the spread of the residuals, improving the model increases the signal to noise ratio and improves the ability to detect shifts. A least squares example using CO₂ data from Mauna Loa is used to illustrate the effect of autocorrelation due to systematic seasonal variability on the ability to detect. The issue is that ordinary least squares tends to underestimate uncertainty when data are serially correlated, implying high false positive rates. Improving the model reduces autocorrelation in the residuals by eliminating systematic trends. Because the data exhibit gaps, Lomb periodograms are used to test the residuals for systematic signals. The model chosen by DIC removes all of the growing and seasonal trends originally present at the 5% level of significance. Thus improving the model is a way to reduce autocorrelation effects on false positives. A key issue for future monitoring sites will be demonstrating the ability to detect shifts in the absence of leaks. The urban weekend weekday effect on atmospheric CO₂ is introduced to illustrate how this might happen. A seasonal detrending model is used to remove systematic trends in data at Mauna Loa, Harvard Forest and Salt Lake. Residuals indicate the presence of positive shifts at the latter sites, as expected, with the magnitude of the shift being larger at the urban site than the rural one (~ 8 ppm versus ~ 1 ppm). Normality tests indicate the residuals are non-Gaussian, so a Bayesian method based on Bayes factors is proposed for determining the amount of data needed to detect shifts in non-Gaussian data. The method is demonstrated on the Harvard and Salt Lake CO₂ data. Results obtained are sensitive to the form of the error distribution. Empirical distributions should be used to avoid false positives. The weekend weekday shift in CO₂ is detectable in 48-120 samples at the urban site. More samples are required at the rural one. Finally, back-of-the-envelope calculations suggest the weekend weekday shift in emissions detected in Salt Lake is - 0(0.01) MtCO₂km- yr- 1. This is the equivalent of 1% of 1 MtCO₂ stored belowground leaking over an area of 1 km2 The framework developed in this thesis can be used to detect shifts in atmospheric CO₂ (or other types of) data after data is already available. Further research is needed to address questions about what data to collect. For example, what sensors should be used, where should they be located, and how frequently should they be sampled? Optimal monitoring network design at a given location will require balancing the need to gather more information (for example, by adding sensors) against operational constraints including cost, safety, and regulatory requirements. / by Arman S. Haidari. / Ph.D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2002. / Includes bibliographical references. / Surfactants frequently come in contact with the skin in the form of personal care products, where they are used to improve the wetting and oil solubilizing qualities of the products. Surfactants are also known to induce skin irritation by damaging the barrier properties of the stratum corneum, the outer layer of the skin, and denaturing proteins in the epidermis and the dermis. The goal of this thesis has been to understand the relationship between the physicochemical properties of surfactant solutions and their skin irritation potential. In vitro tests were developed to measure: (1) the effect of surfactants on the barrier properties of the skin, (2) the concentration of surfactant in the skin, and (3) surfactant-induced protein denaturation, all of which can be related to skin irritation. The physicochemical properties of the surfactant solution, specifically, the concentration of the surfactant monomers (unmicellized surfactant), the composition of the surfactant monomers, and the size and shape of the surfactant micelles, were related to the results of these tests. An in vitro skin irritation test was developed that measures the electrical conductivity of pig skin to quantify the reduction in the barrier properties of the skin, or the skin damage, induced by surfactant solutions. Skin conductivity was found to be directly related to the transdermal water permeability, directly relating the skin conductivity to in vitro skin irritation. Skin conductivity was used to measure the in vitro skin irritation potential of mixtures of the anionic surfactant sodium dodecyl sulfate (SDS) and the nonionic surfactant dodecyl hexa(ethylene oxide) (C12E6), and a relationship was observed between the surfactant monomer concentration and / (cont.) the skin conductivity. The in vitro skin irritation test correctly ranked the in vivo irritation potential of three mild commercial soap bars-Dove, Lever 200, and Ivory. In order to understand the relationship between the micelle concentration and the surfactant-induced damage to the skin, a method was developed to measure the penetration of 14C-radiolabeled SDS surfactant into pig skin. It was found that both monomeric and micellar SDS are able to penetrate into the skin, and that the contribution of the micellar SDS to the concentration of SDS in the skin is comparable to the contribution of the monomeric SDS. SDS penetration into the skin was also measured in the presence of poly(ethylene oxide) (PEO), which forms PEO-bound SDS micelles, and C12E6, which forms SDS/C12E6 mixed micelles. In mixtures of PEO-bound and free SDS micelles, the PEO-bound SDS micelles were found not to penetrate into the skin while the free SDS micelles were found to penetrate. Mixing SDS with C12E6 led to a reduction in the penetration of SDS into the skin by reducing the SDS monomer concentration, as well as by reducing, or preventing altogether, the penetration of micellar SDS. The hydrodynamic radii of the free SDS micelles (21 A), the PEO-bound SDS micelles (25 A), and the SDS-C12E6 mixed micelles (24-30 A) were measured using dynamic light scattering. Based on these results, a new model of surfactant penetration into the skin was proposed, in which the penetration of micellar surfactant into the skin is limited by the size of the micelles... / by Peter Nathaniel Moore. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1992. / Includes bibliographical references. / by Yizu Zhu. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2003. / Includes bibliographical references. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / EGFR and HER2 expression levels have distinguished themselves as important factors in contributing to various types of cancers including breast and ovarian cancers, but quantitative linkages between receptor expression levels and aberrant cell behaviors are not well understood. The ability to interpret and predict cell responses in a multi-parameter space will be vital in efforts to manipulate cell behavior for therapeutic purposes. HER2 acts as a co-receptor of the EGFR family of receptor tyrosine kinases. HER2 does not bind any known ligand, but plays an active signaling role following heterodimerization with a ligand-bound EGFR family receptor. EGFR family receptors undergo a dynamic process termed trafficking in which receptors and ligands are internalized and then either recycled to the surface or targeted for degradation. Trafficking is intimately connected to cell signaling by controlling the quantity and location of ligand-receptor complexes and is sensitive to disruption via the overexpression of the receptors involved. In this work, we quantitatively establish the role of HER2 and heterodimerization in EGFR trafficking and signaling. A hierarchy of mathematical models describing the trafficking behavior of EGFR and HER2 was developed at various levels of mechanistic detail. At the macroscopic level the trafficking of EGFR and HER2 fall into two regimes, one whose downregulation is sorting-limited (EGFR) and one whose downregulation is internalization-limited (HER2). / (cont.) Subordinate models yield mechanistic detail into the endocytic and endosomal sorting processes supporting the notions that heterodimers internalize as single entities and that HER2 is able to disrupt EGFR sorting through a competitive mechanism. The development of a comprehensive model of EGFR and HER2 trafficking enables the predictions of the quantity and distribution of various receptor species, including homo- and heterodimers as a function of time. Point by point comparison with ERK signaling data for different HER2 expressing cell clones allows the calculation of the signal generated per activated HER2 and per activated EGFR. These results suggest that EGFR and HER2 do not differ significantly in their ERK signaling ability and that HER2-mediated differences in ERK signaling can entirely be explained by interactions at the level of receptor trafficking. / by Bart S. Hendriks. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2002. / Vita. / Includes bibliographical references (leaves 130-140). / Antibodies have been used as cancer therapeutics for several decades. One area in which this therapy may be improved is the retention time of antibody in the tumor relative to normal tissue. In this Thesis, we have attempted to elucidate the mechanisms that are most influential to improving antibodies as cancer therapeutics. Carcinoembryonic antigen (CEA) has long been identified as a tumor-associated antigen. CEA is also quite stable, with a cell-surface shedding half-life of approximately 7 days. Directed evolution methodology has been utilized to design an antibody fragment with properties that would improve tumor retention. Specifically, antibody engineering methods were used to produce a humanized, extremely high affinity and stable single chain antibody fragment (scFv) against CEA. Several mutant scFv libraries were constructed and screened against soluble CEA with yeast surface display and fluorescent activated cell sorting (FACS). A series of antibodies were engineered that span three orders of magnitude in off-rate improvement. These antibody fragments show excellent stability at physiologically relevant temperatures. In addition, soluble protein expression levels were greatly improved. The final product has a dissociation half-life of approximately 7 days, currently the longest engineered half-life of an scFv against a tumor-associated antigen. Binding and diffusion in micrometastases was also modeled to gain an improved understanding of the quantitative interplay among the rate processes of diffusion, binding, degradation, and plasma clearance in tumor microspheroids. / (cont.) Modeling studies illuminated the importance of targeting stable tumor-associated antigens. The elimination rate of the antigen was of critical importance to the change in the therapeutic effect of antibodies with increasing affinity. The significance of this result in the context of previous experimental studies will be discussed. By affinity maturing an antibody with a dissociation half-life equal to the turnover half-life of the antigen, we have engineered an antibody with effectively irreversible binding to CEA. Differences in retention for the series of scFvs will thus be dominated by the off-rate of the antibody and not the half-life of CEA. With this in mind, the molecules designed in this study can be used to reconcile the issue of affinity's impact on efficacy in tumor therapy. / by Christilyn Paula Graff. / Ph.D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2000. / Includes bibliographical references. / Controlling the spatial distribution of cells in two and three dimensions may be important in the design of advanced tissue engineering scaffolds and other biomedical applications. In this thesis, the concept of biophysical sorting was applied as a method to control the spatial distribution of cells. This approach relies on a self-assembly process that is dependent, in part, on the intrinsic adhesivity of cells. A model system was developed using a simple patterning technique to prepare surfaces with alternating regions that supported variable cell response. First, the influence of certain biophysical parameters that may govern multicellular assembly of a single cell type on patterned surfaces was quantitatively investigated. For surfaces patterned with small features that allow cells to sample surrounding regions through membrane protrusions, it was found that a dynamic equilibrium distribution of cells correlated with differen~es in cell-substratum adhesion strength. The approach to that distribution, however, could be kinetically limited by the inability of the individual cells to sample adjacent areas of the patterned surface. This kinetic limitation was studied on surfaces with increasingly large feature sizes, and found that a simple diffusion model of migration may not completely describe the present system. Other effects such as contact inhibited motility and an induction time for migration may also influence multicellular assembly. The potential of multicellular assembly to simultaneously control the distribution of two cell types was also investigated. First, the multicellular assembly of each cell type was studied in isolation. Co-culture experiments indicated that, in addition to the factors that govern the assembly of a single cell type, sorting of two cell types depended on cell density. Images of high cell density co-cultures suggest that incomplete biophysical separation was achieved. / by Jennifer T. Fujii. / Ph.D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2000. / Includes bibliographical references (leaves 131-141). / The key accomplishment of this work is the demonstration of spatial control over architecture and surface chemistry in three-dimensional tissue engineering scaffolds. Tissues are characterized by a well-defined three-dimensional arrangement of cells. Spatial control of scaffold elements may be used to encourage the organization of cells into conformations resembling those of native tissue. Patterned scaffolds can be used to explore the healing process and then to design scaffolds with improved healing properties. Patterned architectures were fabricated from hydroxyapatite (HA), biodegradable polyesters (PLLA & PLLGA). and composites of degradable polyesters with bone (rat, bovine & human) using the Three-Dimensional Printing™ process. Two extremes in scaffold design were explored: l) dense structures for strength but with large (600 μm) channels for tissue and vasculature ingrowth. and 2) porous structures with room for cell attachment and growth. Porous structures fabricated from PLLGA and rat bone were implanted subcutaneously on the backs of rats. A typical inflammatory response was observed indicating an acceptable level of biocompatibility for 3DPTM fabricated devices. Dense PLLGA devices fabricated by 3DP™ were shown to still contain significant amounts of chloroform (-5 wt%) after conventional vacuum drying. Liquid C02 extraction was demonstrated to be capable of reducing chloroform in these devices to levels below 50 ppm. Drying was modeled as a diffusion process and diffusion coefficients were estimated for both a batch and a continuous-flow extraction system as 2.47x 10·4 and 3. 18 x10-4 cm2/min. respectively. The model predicts that 1.5 and 9 hours of extraction are needed to reach chloroform levels of <50 ppm in l & 3 mm thick PLLGA bars. respectively. Scaffolds with patterned surface chemistry were fabricated by printing Pluronic® F 127. a surfactant molecule containing PEO chains, in selected locations. Spatial control of MG-63 cell adhesion and morphology was demonstrated on patterned PLLGA surfaces and porous scaffolds. Cell numbers were reduced on Pluronic® modified regions and those attached were less spread and present only in the lower regions of the scaffold. The MG-63 osteosarcoma derived cell line was used to develop assays for measuring cell adhesion. differentiation. and migration in 30 scaffolds. The adhesion, migration and differentiation of rat osteoblasts was then systematically analyzed on nonpatterned scaffolds fabricated with different concentrations of Pluronic® (0, 0.0 I, 0.1 & 0.5% ). Adhesion and migration of rat osteoblasts decreased with increasing Pluronic® concentration. Although measurements were not statistically different. differentiation was judged to increase with Pluronic® concentration because proliferation decreased, alkaline phosphatase activity increased. and cells appeared less fibroblastic and had more microvilli. No significant differences in rat oste0blast behavior were seen on patterned scaffolds fabricated by printing one side with 0.5% Pluronic®. The hypothesis that Pluronic® migrates to the non-Pluronic® side is supported by the fact that Pluronic® is present in the washes generated during the salt-leaching step of fabrication. / by Wendy S. Koegler. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, February 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 301-318). / Developing successful prophylactic and therapeutic strategies against infections of RNA viruses like HIV requires a combined understanding of the evolutionary constraints of the virus, as well as of the immunologic determinants associated with effective viremic control. Recent technologies enable viral and immune parameters to be measured at an unprecedented scale and resolution across multiple patients, and the resulting data could be harnessed towards these goals. Such datasets typically involve a large number of parameters; the goal of analysis is to infer underlying biological relationships that connect these parameters by examining the data. This dissertation combines principles and techniques from the physical and the computational sciences to "reduce the dimensionality" of such data in order to reveal novel biological relationships of relevance to vaccination and therapeutic strategies. Much of our work is concerned with HIV. 1. How can collective evolutionary constraints be inferred from viral sequences derived from infected patients? Using principles of Random Matrix Theory, we derive a low dimensional representation of HIV proteins based on circulating sequence data and identify independent groups of residues within viral proteins that are coordinately linked. One such group of residues within the polyprotein Gag exhibits statistical signatures indicative of strong constraints that limit the viability of a higher proportion of strains bearing multiple mutations in this group. We validate these predictions from independent experimental data, and based on our results, propose candidate immunogens for the Caucasian American population that target these vulnerabilities. 2. To what extent do mutational patterns observed in circulating viral strains accurately reflect intrinsic fitness constraints of viral proteins? Each strain is the result of evolution against an immune background, which is highly diverse across patients. Spin models constructed to reproduce the prevalence of sequences have tested positively against intrinsic fitness assays (where immune selection is absent). Why "prevalence" should correlate with "replicative fitness" in the case of such complex evolutionary dynamics is conceptually puzzling. We combine computer simulations and analytical theory to show that the prevalence can correctly reflect the fitness rank order of mutant viral strains that are proximal in sequence space. Our analysis suggests that incorporating a "phylogenetic correction" in the parameters might improve the predictive power of these models. 3. Can cellular phenotypes be discovered in an unbiased way from high dimensional protein expression data in single cells? Mass cytometry, where > 40 protein parameters can be quantitated in single cells affords a route, but analyzing such high dimensional data can be challenging. Traditional "gating approaches" are unscalable, and computational methods that account for multivariate relationships among different proteins are needed. High-dimensional clustering and principal component analysis, two approaches that have been explored so far, suffer from important limitations. We propose a computational tool rooted in nonlinear dimensionality reduction which overcomes these limitations, and automatically identifies phenotypes based on a two-dimensional distillation of the cellular data; the latter feature facilitates unbiased visualization of high dimensional relationships. Our tool reveals a previously unappreciated phenotypic complexity within murine CD8+ T cells, and identifies a novel phenotype that is conflated by traditional approaches. 4. Antigen-specific immune cells that mediate efficacious antiviral responses in infections like HIV involve complex phenotypes and typically constitute a small fraction of the population. In such circumstances, seeking correlative features in bulk expression levels of key proteins can be misleading. Using the approach introduced in 3., we analyze multiparameter flow cytometry data of CD4+ T-cell samples from 20 patients representing diverse clinical groups, and identify cellular phenotypes whose proportion in patients is strongly correlated with quantitative clinical parameters. Many of these correlations are inconsistent with bulk signals. Furthermore, a number of correlative phenotypes are characterized by the expression of multiple proteins at individually modest levels; such subsets are likely be missed by conventional gating strategies. Using the in-patient proportions of different phenotypes as predictors, a cross-validated, sparse linear regression model explains 87 % of the variance in the viral load across the twenty patients. Our approach is scalable to datasets involving dozens of parameters. / by Karthik Shekhar. / Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1990. / Includes bibliographical references (v. 2, leaves 185-191). / by Gregory Charles Rutledge. / Ph.D.

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1988. / Includes bibliographical references. / by John Christos Calandranis. / Sc.D.