Kinetic modeling of amyloid fibrillation and synaptic plasticity as memory loss and formation mechanisms

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

Economic Integration and Structure Change in Stock Market Dependence: Empirical Evidences of CEPA

[[abstract]]This study investigates dependence structure changes between the Hong Kong and Chinese stock markets as a result of the Closer Economic Partnership Arrangement (CEPA). Four copulas, Gaussian, student t, Gumbel, and Clayton are used to search for unknown dependence structure changes. This study presents two main findings. First, the dependence between the Hong Kong and Chinese stock markets increased significantly following the structure change that occurred on February2, 2005, about one year after CEPA took effect. Second, the distribution of dependence structure altered from Gumbel copula before the structure change to t copula after the structure change. CEPA’s effects not only changed the dependence parameters but also changed the dependence structure’s distribution.[[notice]]補正完畢[[booktype]]紙本[[booktype]]電子

Inferring genetic interactions via a nonlinear model and an optimization algorithm

<p>Abstract</p> <p>Background</p> <p>Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.</p> <p>Results</p> <p>An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in <it>S. cerevisiae</it>, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT.</p> <p>Conclusions</p> <p>GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory interactions (TIs) in yeast, and may be applied to infer TIs in other organisms. The predicted genetic interactions of a subnetwork of human T-cell apoptosis coincide with existing partial pathways, suggesting the potential of GASA on inferring biochemical pathways.</p

Tuning the Photocycle Kinetics of Bacteriorhodopsin in Lipid Nanodiscs

AbstractMonodisperse lipid nanodiscs are particularly suitable for characterizing membrane protein in near-native environment. To study the lipid-composition dependence of photocycle kinetics of bacteriorhodopsin (bR), transient absorption spectroscopy was utilized to monitor the evolution of the photocycle intermediates of bR reconstituted in nanodiscs composed of different ratios of the zwitterionic lipid (DMPC, dimyristoyl phosphatidylcholine; DOPC, dioleoyl phosphatidylcholine) to the negatively charged lipid (DOPG, dioleoyl phosphatidylglycerol; DMPG, dimyristoyl phosphatidylglycerol). The characterization of ion-exchange chromatography showed that the negative surface charge of nanodiscs increased as the content of DOPG or DMPG was increased. The steady-state absorption contours of the light-adapted monomeric bR in nanodiscs composed of different lipid ratios exhibited highly similar absorption features of the retinal moiety at 560 nm, referring to the conservation of the tertiary structure of bR in nanodiscs of different lipid compositions. In addition, transient absorption contours showed that the photocycle kinetics of bR was significantly retarded and the transient populations of intermediates N and O were decreased as the content of DMPG or DOPG was reduced. This observation could be attributed to the negatively charged lipid heads of DMPG and DOPG, exhibiting similar proton relay capability as the native phosphatidylglycerol (PG) analog lipids in the purple membrane. In this work, we not only demonstrated the usefulness of nanodiscs as a membrane-mimicking system, but also showed that the surrounding lipids play a crucial role in altering the biological functions, e.g., the ion translocation kinetics of the transmembrane proteins

Patient-oriented simulation based on Monte Carlo algorithm by using MRI data

<p>Abstract</p> <p>Background</p> <p>Although Monte Carlo simulations of light propagation in full segmented three-dimensional MRI based anatomical models of the human head have been reported in many articles. To our knowledge, there is no patient-oriented simulation for individualized calibration with NIRS measurement. Thus, we offer an approach for brain modeling based on image segmentation process with <it>in vivo </it>MRI T1 three-dimensional image to investigate the individualized calibration for NIRS measurement with Monte Carlo simulation.</p> <p>Methods</p> <p>In this study, an individualized brain is modeled based on <it>in vivo </it>MRI 3D image as five layers structure. The behavior of photon migration was studied for this individualized brain detections based on three-dimensional time-resolved Monte Carlo algorithm. During the Monte Carlo iteration, all photon paths were traced with various source-detector separations for characterization of brain structure to provide helpful information for individualized design of NIRS system.</p> <p>Results</p> <p>Our results indicate that the patient-oriented simulation can provide significant characteristics on the optimal choice of source-detector separation within 3.3 cm of individualized design in this case. Significant distortions were observed around the cerebral cortex folding. The spatial sensitivity profile penetrated deeper to the brain in the case of expanded CSF. This finding suggests that the optical method may provide not only functional signal from brain activation but also structural information of brain atrophy with the expanded CSF layer. The proposed modeling method also provides multi-wavelength for NIRS simulation to approach the practical NIRS measurement.</p> <p>Conclusions</p> <p>In this study, the three-dimensional time-resolved brain modeling method approaches the realistic human brain that provides useful information for NIRS systematic design and calibration for individualized case with prior MRI data.</p

Modulation of nucleosome-binding activity of FACT by poly(ADP-ribosyl)ation

Chromatin-modifying factors play key roles in transcription, DNA replication and DNA repair. Post-translational modification of these proteins is largely responsible for regulating their activity. The FACT (facilitates chromatin transcription) complex, a heterodimer of hSpt16 and SSRP1, is a chromatin structure modulator whose involvement in transcription and DNA replication has been reported. Here we show that nucleosome binding activity of FACT complex is regulated by poly(ADP-ribosyl)ation. hSpt16, the large subunit of FACT, is poly(ADP-ribosyl)ated by poly(ADP-ribose) polymerase-1 (PARP-1) resulting from physical interaction between these two proteins. The level of hSpt16 poly(ADP-ribosyl)ation is elevated after genotoxic treatment and coincides with the activation of PARP-1. The enhanced hSpt16 poly(ADP-ribosyl)ation level correlates with the dissociation of FACT from chromatin in response to DNA damage. Our findings suggest that poly(ADP-ribosyl)ation of hSpt16 by PARP-1 play regulatory roles for FACT-mediated chromatin remodeling

Application of Flexible Micro Temperature Sensor in Oxidative Steam Reforming by a Methanol Micro Reformer

Advances in fuel cell applications reflect the ability of reformers to produce hydrogen. This work presents a flexible micro temperature sensor that is fabricated based on micro-electro-mechanical systems (MEMS) technology and integrated into a flat micro methanol reformer to observe the conditions inside that reformer. The micro temperature sensor has higher accuracy and sensitivity than a conventionally adopted thermocouple. Despite various micro temperature sensor applications, integrated micro reformers are still relatively new. This work proposes a novel method for integrating micro methanol reformers and micro temperature sensors, subsequently increasing the methanol conversion rate and the hydrogen production rate by varying the fuel supply rate and the water/methanol ratio. Importantly, the proposed micro temperature sensor adequately controls the interior temperature during oxidative steam reforming of methanol (OSRM), with the relevant parameters optimized as well