Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2008. / Includes bibliographical references (p. 141-150). / The principles of biochemical kinetics and system engineering are applied to explain memory-related neuroscientific phenomena. Amyloid fibrillation and synaptic plasticity have been our focus of research due to their significance. The former is related to the pathology of many neurodegenerative diseases and the later is regarded as the principal mechanism underlying learning and memory. Claimed to be the number one cause of senile dementia, Alzheimer's disease (AD) is one of the disorders that involve misfolding of amyloid protein and formation of insoluble fibrils. Although a variety of time dependent fibrillation data in vitro are available, few mechanistic models have been developed. To bridge this gap we used chemical engineering concepts from polymer dynamics, particle mechanics and population balance models to develop a mathematical formulation of amyloid growth dynamics. A three-stage mechanism consisting of natural protein misfolding, nucleation, and fibril elongation phases was proposed to capture the features of homogeneous fibrillation responses. While our cooperative laboratory provided us with experimental findings, we guided them with experimental design based on modeling work. It was through the iterative process that the size of fibril nuclei and concentration profiles of soluble proteins were elucidated. The study also reveals further experiments for diagnosing the evolution of amyloid coagulation and probing desired properties of potential fibrillation inhibitors. Synaptic plasticity at various time ranges has been studied experimentally to elucidate memory formation mechanism. By comparison, the theoretical work is underdeveloped and insufficient to explain some experiments. To resolve the issue, we developed models for short-term, long-term, and spike timing dependent synaptic plasticity, respectively. / (cont.) First, presynaptic vesicle trafficking that leads to the release of glutamate as neurotransmitter was taken into account to explain short-term plasticity data. Second, long-term plasticity data lasting for hours after tetanus stimuli has been matched by a calcium entrapment model we developed. Model differentiation was done to demonstrate the better performance of calcium entrapment model than an alternative bistable theory in fitting graded long-term potentiation responses. Finally, to decipher spike timing dependent plasticity (STDP), we developed a systematic model incorporating back propagation of action potential, dual requirement of NMDA receptors, and calcium dependent plasticity. This built model is supported by five different types of STDP experimental data. The accumulation of amyloid beta has been found to disrupt the sustainable modification of long-term synaptic plasticity which might explain the inability of AD patients to form new memory at early stage of the disease. Yet the linkage between the existence of amyloid beta species and failure of long-term plasticity was unclear. We suggest that the abnormality of calcium entrapment function caused by amyloid oligomers is the intermediate step that eventually leads to memory loss. Unsustainable calcium level and decreased postsynaptic activities result into the removal or internalization of alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors. The number of AMPA receptors as the indicators of synaptic strength may result into disconnection between neurons and even neuronal apoptosis. New experiments have been suggested to validate this hypothesis and to elucidate the pathology of Alzheimer's disease. / by Chuang-Chung (Justin) Lee. / Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1999. / Includes bibliographical references. / by Venkatraghavan Ganesan. / S.M.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2014. / 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 87-91). / The United Nations estimates that over 35 million people are afflicted with HIV/AIDS in the world. Highly active antiretroviral treatments (HAART) that use a combination of drugs that target the virus at different stages of its life cycle are effective at reducing the HIV plasma levels below levels detectable by the most sensitive assays. However, upon termination of HAART, HIV RNA transcripts are measurable in the blood after 2-3 weeks. This relapse is attributed to the presence of a reservoir of latently infected cells, such as resting CD4+ T-cells. The latent reservoir in resting memory CD4+ T-cells has been estimated to decay with a half-life of as long as 44 months, thus hindering the eradication of HIV. Current knowledge of latent reservoirs came from the isolation of possible reservoir populations by cell surface markers and querying each population for the presence of HIV RNA. These measurements do not have single cell resolution so the exact frequencies of latently infected cells are not known. In this thesis, we developed and optimized a method to detect cellular transcripts of single cells in an array of nanowells. The limit of detection of the assay was approximately 1.4 copies of DNA in a 125 pL well (18.6 fM) with a false positive rate as low as 4.6x10-5. Combining this assay along with image-based cytometry and microengraving, we generated a multivariate dataset on single cells to understand the relationships between cell phenotype, transcribed genes, and secreted products. We showed that gene expression could not be a surrogate measure for antibody secretion. We were also able to detect rare cells in a population at a frequency as low as 1 in 10,000. We then applied the technology to samples from a patient on HAART for more than 1.5 years. We were able to detect an infection rate of 1:3000 cells that had low levels of HIV RNA in bulk. / by Yuan Gong. / Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2006. / Includes bibliographical references. / Cell motility is often governed by growth factor receptor and integrin adhesion receptor interactions with the extracellular environment followed by collaborative intracellular signaling. While integrin ligands are necessarily bound within the extracellular matrix to permit force transduction by the cell, the canonical view of growth factors is of soluble molecules freely diffusible and internalizable by the cell. Recent evidence suggests that in some cases growth factor receptor ligands may be embedded in the extracellular matrix and may signal primarily from the cell surface. By altering the trafficking of receptor and ligand, the potential exists to change the spatiotemporal distribution of signaling within the cell. These changes in the magnitude, duration, and spatial localization of specific signals influence the biophysical regulation of cell migration, which is a multi-step process directed by a number of timely and spatially coordinated signaling events. In this work, we develop a polymer incorporating both growth factors and adhesion molecules with nanoscale spatial ligand control to model matrix-embedded ligand presentation. / (cont.) The polymer is a poly(methyl methacrylate)-poly(ethylene oxide) (PMMA-g-PEO) comb copolymer that displays the ligands via 2-3 nm molecular tethers. Two forms of the polymer are employed, one in which epidermal growth factor (EGF) is tethered amidst an adhesive background of adsorbed fibronectin (FN) and a second in which EGF and a FN-like PHSRN-RGD peptide (SynKRGD) are simultaneously tethered. The surface densities of each ligand are independently controlled during their incorporation into the polymer. Thus, variation in substrate adhesiveness is achieved by adsorbing different densities of FN or by covalently tethering different densities of SynKRGD. With these substrates, we use a model cell line of NR6 fibroblasts expressing the wild-type human EGF receptor (EGFR) to observe the effects of surface-tethered EGF on signaling, adhesion, and migration compared to the traditional soluble EGF presentation. Using the EGF-FN substrates, we determine that tethered EGF signals EGFR primarily at the cell-substrate interface. Tethered EGF, like soluble EGF, elicits enhanced migration speed with a biphasic dependence on FN density. However, the peak cell speed for tethered EGF is achieved at an order of magnitude greater FN density. Quantification of cell spread area suggests that tethered EGF reduces cell-substrate adhesion strength relative to soluble EGF. / (cont.) Although we are unable to conclusively attribute the biphasic curve shift on EGF-FN substrates to a specific signaling mechanism, we do observe a dependence of focal adhesion kinase (FAK) signal strength on substrate adhesiveness. Further investigation of EGFR phosphorylations and downstream motility-relevant signals using the EGF-SynKRGD substrates reveals characteristics of tethered EGF signal transduction that are quantitatively distinct from soluble EGF and concomitantly influenced by integrin-mediated adhesion. These results underscore the complex synergy between EGFR and integrins while demonstrating the significance of spatial ligand presentation in regulating cell behavior. / by Llewellyn B. Richardson, III. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, February 2013. / Cataloged from PDF version of thesis. "February 2013." / Includes bibliographical references. / The sequence diversity of glycoprotein gp120 of the envelope spike of Human Immunodeficiency Virus (HIV) allows the virus to escape from antibody selection pressure. Certain conserved epitopes, like the CD4 binding site, are required for viral fitness and antibodies against these epitopes are able to neutralize HIV from multiple clades. Passive immunization experiments suggest that eliciting such broadly reactive antibodies by vaccination may provide protection, but so far this has proven impossible. In this thesis, we establish a yeast surface display system for the development of gp120-based molecules for antibody characterization and immunogen design. A stripped core gp120 is constructed that retains the correct presentation of the CD4 binding site. Epitopes of several CD4 binding site-directed antibodies, including the gold standard antibody VRC01, are mapped with yeast displayed mutant libraries. A panel of immunogens that share the epitope defined by VRC01 but are diverse elsewhere on their surfaces is designed. Mice immunized sequentially with the diverse immunogens elicit an antibody response that is focused entirely on the VRC01 epitope. The serum cross-reacts with gp120 from multiple clades. Monoclonal antibodies from these mice are isolated and characterized. / by Jordi Mata-Fink. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1994. / Includes bibliographical references (leaves 254-269). / by Yu-Hsiang David Chang. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2006. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Includes bibliographical references (p. 351-380). / A methodology for the optimal design and operation of microfabricated fuel cell systems is proposed and algorithms for relevant optimization problems are developed. The methodology relies on modeling, simulation and optimization at three levels of modeling detail. The first class of optimization problems considered are parametric mixed-integer linear programs and the second class are bilevel programs with nonconvex inner and outer programs; no algorithms exist currently in the open literature for the global solution of either problem in the form considered here. Microfabricated fuel cell systems are a promising alternative to batteries for manportable power generation. These devices are potential consumer products that comprise a more or less complex chemical process, and can therefore be considered chemical products. With current computational possibilities and available algorithms it is impossible to solve for the optimal design and operation in one step since the devices considered involve complex geometries, multiple scales, time-dependence and parametric uncertainty. Therefore, a methodology is presented based on decomposition into three levels of modeling detail, namely system-level models for process synthesis, / (cont.) intermediate fidelity models for optimization of sizes and operation, and detailed, computational fluid dynamics models for geometry improvement. Process synthesis, heat integration and layout considerations are addressed through the use of lumped algebraic models, general enough to be independent of detailed design choices, such as reactor configuration and catalyst choice. Through the use of simulation and parametric mixed-integer optimization the most promising process structures along with idealized layouts are selected among thousands of alternatives. At the intermediate fidelity level space-distributed models are used, which allow optimization of unit sizes and operation for a given process structure without the need to specify a detailed geometry. The resulting models involve partial differential-algebraic equations and dynamic optimization is employed as the solution technique. Finally, the use of detailed two- and three-dimensional computational fluid dynamics facilitates geometrical improvements as well as the derivation and validation of modeling assumptions that are employed in the system-level and intermediate fidelity models. Steady-state case studies are presented assuming a constant power demand; / (cont.) the methodology can be also applied to transient considerations and the case of variable power demand. Parametric programming provides the solution of an optimization problem, the data of which depend on one or many unknown real-valued parameters, for each possible value of the parameter(s). In this thesis mixed-integer linear programs are considered, i.e., optimization programs with affine functions involving real- and integervalued variables. In the first part the multiparametric cost-vector case is considered, i.e., an arbitrary finite number of parameters is allowed, that influence only the coefficients of the objective function. The extension of a well-known algorithm for the single-parameter case is presented, and the algorithm behavior is illustrated on simple examples with two parameters. The optimality region of a given basis is a polyhedron in the parameter space, and the algorithm relies on progressively constructing these polyhedra and solving mixed-integer linear programs at their vertices. Subsequently, two algorithmic alternatives are developed, one based on the identification of optimality regions, and one on branch-and-bound. In the second part the single-parameter general case is considered, / (cont.) i.e., a single parameter is allowed that can simultaneously influence the coefficients of the objective function, the right-hand side of the constraints, and also the coefficients of the matrix. Two algorithms for mixed-integer linear programs are proposed. The first is based on branch-and-bound on the integer variables, solving a parametric linear program at each node, and the second is based on decomposition of the parametric optimization problem into a series of mixed-integer linear and mixed-integer nonlinear optimization problems. For the parametric linear programs an improvement of a literature algorithm for the solution of linear programs based on rational operations is presented and an alternative based on predictor-continuation is proposed. A set of test problems is introduced and numerical results for these test problems are discussed. The algorithms are then applied to case studies from the man-portable power generation. Finally extensions to the nonlinear case are discussed and an example from chemical equilibrium is analyzed. Bilevel programs are hierarchical programs where an outer program is constrained by an embedded inner program. / (cont.) Here the co-operative formulation of inequality constrained bilevel programs involving real-valued variables and nonconvex functions in both the inner and outer programs is considered. It is shown that previous literature proposals for the global solution of such programs are not generally valid for nonconvex inner programs and several consequences of nonconvexity in the inner program are identified. Subsequently, a bounding algorithm for the global solution is presented. The algorithm is rigorous and terminates finitely to a solution that satisfies e-optimality in the inner and outer programs. For the lower bounding problem, a relaxed program, containing the constraints of the inner and outer programs augmented by a parametric upper bound on the optimal solution function of the inner program, is solved to global optimality. For the case that the inner program satisfies a constraint qualification, a heuristic for tighter lower bounds is presented based on the KKT necessary conditions of the inner program. The upper bounding problem is based on probing the solution obtained in the lower bounding procedure. Branching and probing are not required for convergence but both have potential advantages. / (cont.) Three branching heuristics are described and analyzed. A set of test problems is introduced and numerical results for these test problems and for literature examples are presented. / by Alexander Mitsos. / Ph.D.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1901. / MIT copy bound with: A study of the Becchi test for cotton-seed oil / Charles H. Dennison. / B.S.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1997. / Includes bibliographical references (p. 239-245). / by Berit Sagli Ahmad. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 315-331). / Optimization models in the chemical process industries often include uncertain model parameters due to uncertainties in market forces and the environment, use of reduced-order and surrogate process models, and difficulty in measuring parameters accurately. Optimal solutions to formulations that simply ignore uncertainties in the model parameters can be economically worthless or even disastrous in safety-critical applications. Rigorously accounting for uncertainties in optimization models arising out of the process industries is usually computationally prohibitive because of their inherent nonconvex and combinatorial nature. This thesis develops branch-and-bound (B&B) algorithms and a software framework for the scalable solution of a rich class of optimization problems under parametric uncertainty called two-stage stochastic programs, which finds several applications within the petrochemical, pharmaceutical, and energy industries. Additionally, the convergence rates of broad classes of B&B algorithms for constrained optimization problems are analyzed to determine favorable characteristics of such algorithms that can help mitigate the cluster problem in constrained optimization. Two-stage stochastic programming formulations assume that a finite number of scenarios of the uncertain parameters may be realized, and provide a suitable framework for modeling applications with economic objectives. General-purpose B&B algorithms for two-stage stochastic programs suffer from a worst-case exponential increase in solution times with the number of scenarios, which makes the solution of practical applications impractical. This thesis presents a decomposable B&B algorithm for the efficient solution of large-scenario instances of a broad class of two-stage stochastic programs. Furthermore, this thesis details a software framework, named GOSSIP, that was developed for solving such problems. GOSSIP, which implements state-of-the-art decomposition techniques for the global solution of two stage stochastic programs, is shown to perform favorably on a diverse set of test problems from the process systems engineering literature, and is a step towards the efficient solution of two-stage stochastic programming applications from the chemical process industries. Branch-and-bound algorithms that do not possess good convergence properties suffer from the so-called cluster problem wherein a large number of boxes are visited in the vicinity of global optimizers. While the convergence rates of B&B algorithms for unconstrained problems and the cluster problem in unconstrained optimization had been well-studied prior to this thesis, the analyses for constrained problems were lacking, and are the focus of the second part of this thesis. This section of the thesis begins by developing a notion of convergence order for bounding schemes for B&B algorithms, establishes conditions under which first-order and second-order convergent bounding schemes may be sufficient to mitigate the cluster problem, and determines sufficient conditions for widely applicable bounding schemes to possess first-order and second-order convergence. In addition, this section analyzes the convergence orders of some reduced-space B&B algorithms in the literature and establishes that such algorithms may suffer from the cluster problem if domain reduction techniques are not employed. Determining sufficient conditions on the domain reduction techniques to be able to mitigate the above cluster problem can help identify efficient reduced-space B&B algorithms for solving two-stage stochastic programs. / by Rohit Kannan. / Ph. D.