Matching Items (44)

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Engineering a Human Induced Pluripotent Stem Cell (hiPSC)-Based Model of Alzheimer's Disease

Description

Alzheimer’s Disease (AD) affects over 5 million individuals in the U.S. and has a direct cost estimated in excess of $200 billion per year. Broadly speaking, there are two forms of AD—early-onset, familial AD (FAD) and late-onset-sporadic AD (SAD). Animal

Alzheimer’s Disease (AD) affects over 5 million individuals in the U.S. and has a direct cost estimated in excess of $200 billion per year. Broadly speaking, there are two forms of AD—early-onset, familial AD (FAD) and late-onset-sporadic AD (SAD). Animal models of AD, which rely on the overexpression of FAD-related mutations, have provided important insights into the disease. However, these models do not display important disease-related pathologies and have been limited in their ability to model the complex genetics associated with SAD.

Advances in cellular reprogramming, have enabled the generation of in vitro disease models that can be used to dissect disease mechanisms and evaluate potential therapeutics. To that end, efforts by many groups, including the Brafman laboratory, to generated patient-specific hiPSCs have demonstrated the promise of studying AD in a simplified and accessible system. However, neurons generated from these hiPSCs have shown some, but not all, of the early molecular and cellular hallmarks associated with the disease. Additionally, phenotypes and pathological hallmarks associated with later stages of the human disease have not been observed with current hiPSC-based systems. Further, disease relevant phenotypes in neurons generated from SAD hiPSCs have been highly variable or largely absent. Finally, the reprogramming process erases phenotypes associated with cellular aging and, as a result, iPSC-derived neurons more closely resemble fetal brain rather than adult brain.

It is well-established that in vivo cells reside within a complex 3-D microenvironment that plays a significant role in regulating cell behavior. Signaling and other cellular functions, such as gene expression and differentiation potential, differ in 3-D cultures compared with 2-D substrates. Nonetheless, previous studies using AD hiPSCs have relied on 2-D neuronal culture models that do not reflect the 3-D complexity of native brain tissue, and therefore, are unable to replicate all aspects of AD pathogenesis. Further, the reprogramming process erases cellular aging phenotypes. To address these limitations, this project aimed to develop bioengineering methods for the generation of 3-D organoid-based cultures that mimic in vivo cortical tissue, and to generate an inducible gene repression system to recapitulate cellular aging hallmarks.

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2018-05

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Translational Regulation using Artificial Introns and ncRNAs

Description

Synthetic biology is an emerging engineering disciple, which designs and controls biological systems for creation of materials, biosensors, biocomputing, and much more. To better control and engineer these systems, modular genetic components which allow for highly specific and high dynamic

Synthetic biology is an emerging engineering disciple, which designs and controls biological systems for creation of materials, biosensors, biocomputing, and much more. To better control and engineer these systems, modular genetic components which allow for highly specific and high dynamic range genetic regulation are necessary. Currently the field struggles to demonstrate reliable regulators which are programmable and specific, yet also allow for a high dynamic range of control. Inspired by the characteristics of the RNA toehold switch in E. coli, this project attempts utilize artificial introns and complementary trans-acting RNAs for gene regulation in a eukaryote host, S. cerevisiae. Following modification to an artificial intron, splicing control with RNA hairpins was demonstrated. Temperature shifts led to increased protein production likely due to increased splicing due to hairpin loosening. Progress is underway to demonstrate trans-acting RNA interaction to control splicing. With continued development, we hope to provide a programmable, specific, and effective means for translational gene regulation in S. cerevisae.

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2018-05

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Plasmid Design for Making a HEK293t Reporter Cell Line to Study Gene Expression Dynamics

Description

Cell fate is a complex and dynamic process with many genetic components. It has often been likened to “multistable” mathematical systems because of the numerous possible “stable” states, or cell types, that cells may end up in. Due

Cell fate is a complex and dynamic process with many genetic components. It has often been likened to “multistable” mathematical systems because of the numerous possible “stable” states, or cell types, that cells may end up in. Due to its complexity, understanding the process of cell fate and differentiation has proven challenging. A better understanding of cell differentiation has applications in regenerative stem cell therapies, disease pathologies, and gene regulatory networks.
A variety of different genes have been associated with cell fate. For example, the Nanog/Oct-4/Sox2 network forms the core interaction of a gene network that maintains stem cell pluripotency, and Oct-4 and Sox2 also play a role in the tissue types that stem cells eventually differentiate into. Using the CRISPR/cas9 based homology independent targeted integration (HITI) method developed by Suzuki et al., we can integrate fluorescent tags behind genes with reasonable efficiency via the non-homologous end joining (NHEJ) DNA repair pathway. With human embryonic kidney (HEK) 293T cells, which can be transfected with high efficiencies, we aim to create a three-parameter reporter cell line with fluorescent tags for three different genes related to cell fate. This cell line would provide several advantages for the study of cell fate, including the ability to quantitatively measure cell state, observe expression heterogeneity among a population of genetically identical cells, and easily monitor fluctuations in expression patterns.
The project is partially complete at this time. This report discusses progress thus far, as well as the challenges faced and the future steps for completing the reporter line.

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2019-05

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Quorum-Sensing Crosstalk-Driven Synthetic Circuits: From Unimodality to Trimodality

Description

Widespread quorum-sensing (QS) enables bacteria to communicate and plays a critical role in controlling bacterial virulence. However, effects of promiscuous QS crosstalk and its implications for gene regulation and cell decision-making remain largely unknown. Here we systematically studied the crosstalk

Widespread quorum-sensing (QS) enables bacteria to communicate and plays a critical role in controlling bacterial virulence. However, effects of promiscuous QS crosstalk and its implications for gene regulation and cell decision-making remain largely unknown. Here we systematically studied the crosstalk between LuxR/I and LasR/I systems and found that QS crosstalk can be dissected into signal crosstalk and promoter crosstalk. Further investigations using synthetic positive feedback circuits revealed that signal crosstalk significantly decreases a circuit’s bistable potential while maintaining unimodality. Promoter crosstalk, however, reproducibly generates complex trimodal responses resulting from noise-induced state transitions and host-circuit interactions. A mathematical model that integrates the circuit’s nonlinearity, stochasticity, and host-circuit interactions was developed, and its predictions of conditions for trimodality were verified experimentally. Combining synthetic biology and mathematical modeling, this work sheds light on the complex behaviors emerging from QS crosstalk, which could be exploited for therapeutics and biotechnology.

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2014-12-18

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Data-Based Reconstruction of Complex Geospatial Networks, Nodal Positioning, and Detection of Hidden Nodes

Description

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified.

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2016-01-06

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A Geometrical Approach to Control and Controllability of Nonlinear Dynamical Networks

Description

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.

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Date Created
2016-04-14

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Gene Networks of Fully Connected Triads With Complete Auto-Activation Enable Multistability and Stepwise Stochastic Transitions

Description

Fully-connected triads (FCTs), such as the Oct4-Sox2-Nanog triad, have been implicated as recurring transcriptional motifs embedded within the regulatory networks that specify and maintain cellular states. To explore the possible connections between FCT topologies and cell fate determinations, we employed

Fully-connected triads (FCTs), such as the Oct4-Sox2-Nanog triad, have been implicated as recurring transcriptional motifs embedded within the regulatory networks that specify and maintain cellular states. To explore the possible connections between FCT topologies and cell fate determinations, we employed computational network screening to search all possible FCT topologies for multistability, a dynamic property that allows the rise of alternate regulatory states from the same transcriptional network. The search yielded a hierarchy of FCTs with various potentials for multistability, including several topologies capable of reaching eight distinct stable states. Our analyses suggested that complete auto-activation is an effective indicator for multistability, and, when gene expression noise was incorporated into the model, the networks were able to transit multiple states spontaneously. Different levels of stochasticity were found to either induce or disrupt random state transitioning with some transitions requiring layovers at one or more intermediate states. Using this framework we simulated a simplified model of induced pluripotency by including constitutive overexpression terms. The corresponding FCT showed random state transitioning from a terminal state to the pluripotent state, with the temporal distribution of this transition matching published experimental data. This work establishes a potential theoretical framework for understanding cell fate determinations by connecting conserved regulatory modules with network dynamics. Our results could also be employed experimentally, using established developmental transcription factors as seeds, to locate cell lineage specification networks by using auto-activation as a cipher.

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2014-07-24

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Polyclonal Antibody Production for Membrane Proteins via Genetic Immunization

Description

Antibodies are essential for structural determinations and functional studies of membrane proteins, but antibody generation is limited by the availability of properly-folded and purified antigen. We describe the first application of genetic immunization to a structurally diverse set of membrane

Antibodies are essential for structural determinations and functional studies of membrane proteins, but antibody generation is limited by the availability of properly-folded and purified antigen. We describe the first application of genetic immunization to a structurally diverse set of membrane proteins to show that immunization of mice with DNA alone produced antibodies against 71% (n = 17) of the bacterial and viral targets. Antibody production correlated with prior reports of target immunogenicity in host organisms, underscoring the efficiency of this DNA-gold micronanoplex approach. To generate each antigen for antibody characterization, we also developed a simple in vitro membrane protein expression and capture method. Antibody specificity was demonstrated upon identifying, for the first time, membrane-directed heterologous expression of the native sequences of the FopA and FTT1525 virulence determinants from the select agent Francisella tularensis SCHU S4. These approaches will accelerate future structural and functional investigations of therapeutically-relevant membrane proteins.

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Date Created
2016-02-24

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Engineering of a Synthetic Quadrastable Gene Network to Approach Waddington Landscape and Cell Fate Determination

Description

The process of cell fate determination has been depicted intuitively as cells travelling and resting on a rugged landscape, which has been probed by various theoretical studies. However, few studies have experimentally demonstrated how underlying gene regulatory networks shape the

The process of cell fate determination has been depicted intuitively as cells travelling and resting on a rugged landscape, which has been probed by various theoretical studies. However, few studies have experimentally demonstrated how underlying gene regulatory networks shape the landscape and hence orchestrate cellular decision-making in the presence of both signal and noise. Here we tested different topologies and verified a synthetic gene circuit with mutual inhibition and auto-activations to be quadrastable, which enables direct study of quadruple cell fate determination on an engineered landscape. We show that cells indeed gravitate towards local minima and signal inductions dictate cell fates through modulating the shape of the multistable landscape. Experiments, guided by model predictions, reveal that sequential inductions generate distinct cell fates by changing landscape in sequence and hence navigating cells to different final states. This work provides a synthetic biology framework to approach cell fate determination and suggests a landscape-based explanation of fixed induction sequences for targeted differentiation.

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2017-04-11

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Approaches to Minimum d-Degree Arrangement

Description

Many systems in the world \u2014 such as cellular networks, the post service, or transportation pathways \u2014 can be modeled as networks or graphs. The practical applications of graph algorithms generally seek to achieve some goal while minimizing some cost

Many systems in the world \u2014 such as cellular networks, the post service, or transportation pathways \u2014 can be modeled as networks or graphs. The practical applications of graph algorithms generally seek to achieve some goal while minimizing some cost such as money or distance. While the minimum linear arrangement (MLA) problem has been widely-studied amongst graph ordering and embedding problems, there have been no developments into versions of the problem involving degree higher than 2. An application of our problem can be seen in overlay networks in telecommunications. An overlay network is a virtual network that is built on top of another network. It is a logical network where the links between nodes represent the physical paths connecting the nodes in the underlying infrastructure. The underlying physical network may be incomplete, but as long as it is connected, we can build a complete overlay network on top of it. Since some nodes may be overloaded by traffic, we can reduce the strain on the overlay network by limiting the communication between nodes. Some edges, however, may have more importance than others so we must be careful about our selection of which nodes are allowed to communicate with each other. The balance of reducing the degree of the network while maximizing communication forms the basis of our d-degree minimum arrangement problem. In this thesis we will look at several approaches to solving the generalized d-degree minimum arrangement d-MA problem where we embed a graph onto a subgraph of a given degree. We first look into the requirements and challenges of solving the d-MA problem. We will then present a polynomial-time heuristic and compare its performance with the optimal solution derived from integer linear programming. We will show that a simple (d-1)-ary tree construction provides the optimal structure for uniform graphs with large requests sets. Finally, we will present experimental data gathered from running simulations on a variety of graphs to evaluate the efficiency of our heuristic and tree construction.

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Date Created
2016-05