苹果淫院

Description

The nuclear membrane protein, lamin A/C, is known to be a dominant component in nuclear stiffness, and changes in nuclear mechanics are suspected to relate to cancer metastasis, however, the connection between nuclear stiffness and cell migration remain unclear. This project will examine these relationships by varying the expression of lamins, and measuring the resulting changes in cell movement (measured by particle image velocimetry), contractility (as measured by traction force microscopy) and migration through fibrous 3D matrices (using confocal microscopy). This work will be performed with minimally 1 PhD candidate in direct supervision, and it is anticipated that contributions will lead to coauthorship on a manuscript.

Tasks per student

鈥� Performing cell culture techniques such as culturing and subculturing cells, transfection of cells 鈥� Fabrication of PDMS substrates 鈥� Traction force microscopy 鈥� Fabrication of 3D collagen matrix 鈥� Cell migration assays

听

Number of positions

2

Academic Level

Year 3

Description

The sensing and generation of cellular forces are essential aspects of life. Traction Force Microscopy (TFM) has emerged as a standard broadly applicable methodology to measure cell contractility and its role in cell behavior. While TFM platforms have enabled diverse discoveries, their implementation remains limited in part due to various constraints, such as complex substrate fabrications, the need to detach cells to measure null force images, followed by complex imaging and analysis, limited cell number, and the unavailability of cells for post-processing. Here we introduce a reference-free technique to measure cell contractile work in real-time, with basic substrate fabrication methodologies, simple imaging, high-throughput potential and analysis with the availability of the cells for post-processing. In this technique, we confine the cells on fluorescent adhesive protein micropatterns of a known area on compliant silicone substrates and use the cell deformed pattern area to calculate cell contractile work. We already validated this approach by comparing this Pattern-based Contractility Screening (PaCS) to conventional bead-displacement TFM and show quantitative agreement between the methodologies. Now we want to develop PaCS as a high throughput technology that measures more than 1000 cells at once.

Tasks per student

Tasks: 1. Print 10000 adhesive protein micropatterns on soft silicon substrates (~2-20kPa) and confine different cell types on these patterns. 3. Image 10000 micropatterns using a fluorescent microscope. 4. Develop software to filter and analyze micropatterns to measure cell contractile work. Once fully developed PaCS will be commercialized as a high-throughput drug screening technology with potential applications in both industry and academia.

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Number of positions

1

Academic Level

Year 3

Description

The Epithelial-to-Mesenchymal Transition (EMT) is a key transformation in cancer metastasis. During EMT, tightly packed epithelial cells become more invasive and motile mesenchymal cells. This biophysical change is believed to contribute to the increased tumor cell motility in cancer metastasis. Cancer cells are known to undergo various biomechanical changes during this transition, yet a comprehensive and cohesive quantification of these changes is lacking. Our lab and others have previously shown that as epithelial cells acquire more migratory and invasive mesenchymal characteristics, they apply more forces on their surroundings and do more work. However, how this increased external cell contractility orchestrates internal mechanical changes during EMT to promote migration is not well understood. Here we will simultaneously measure internal cell forces, external cell contractility, cell-cell forces, and cell movement. This will be done to a confluent monolayer at varying stages of EMT, offering an unprecedented mechanical perspective on this transition.

Tasks per student

Tasks: 1. Transfect NMuMG epithelial cells with alpha-actinin FRET tension sensors. 2. Perform Traction Force Microscopy and FRET simultaneously on confluent NMuMG epithelial cells during their TGF 尾 induced transition to isolated mesenchymal stem cells 3. Quantify changes in cell internal tension (FRET) and contractile work done by the cells (TFM) during EMT. This will provide us with physical cytoskeletal transitions induced during EMT and provide us with more therapeutic leads to tackle EMT dependent cancer metastasis.

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Number of positions

2

Academic Level

Year 3

Description

Cannabinoids are valuable natural products well known for their psychoactivity in humans. Yet, many members of this group exhibit other important pharmacological properties, such as analgesic, anti-inflammatory, and anti-convulsive. Medicinally attractive cannabinoids are produced in plants in low amounts and extracted as mixture with the phychoactive compounds, thus limiting their use the therapeutic agents. Recently, the cannabinoid biosynthetic pathway was elucidated and stepwise reconstructed in yeast. Consequently, production of desired cannabinoids is now possible by engineering their corresponding biosynthetic steps in this host. However, current yields are low and prevent further applications. To address this challenge, we propose to increase the level of cannabinoid building blocks: malonyl-CoA, hexanoyl-CoA, and geranyl diphosphate. In yeast, these metabolites are essential components for the synthesis of fatty acids and sterols, respectively. Therefore, intracellular accumulation of malonyl-CoA and geranyl diphosphate is tightly regulated to very low levels, while hexanoyl-CoA is very poor, limiting sustainable production of derived compounds in microbes. To bypass the competition for malonyl-CoA and geranyl diphosphate between yeast native metabolism and cannadinoid biosynthetic pathway, improve hexanoyl-CoA availability and alleviate metabolic crosstalk we envision to redirect metabolic fluxes, engineer orthogonal biosynthetic pathways, and compartmentalize competitive heterologous steps.

Tasks per student

In this project, the student will design a rational strategy to increase the level of malonyl-CoA, hexanoyl-CoA, and geranyl diphosphate in yeast and engineer yeast as a dedicated chassis for production of cannabinoid building blocks. This strategy include: (1) Identification of critical nodes for intervention in yeast metabolism (2) Selection and design of parts and modules to be used in the metabolic engineering approach, (3) Design and development of tools to engineer yeast cells.

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Number of positions

1

Academic Level

No preference

Description

Electrochemical biosensors- transforming a biochemical phenomena into an electrical signal- own outstanding advantages such as easy instrumentation, high clinically relevant sensitivity, possibility of miniaturization and low cost. We combine bottom-up and top-down fabrication techniques to develop advanced nanostructured platforms for electrochemical detection of pathogens (such as SARS-CoV2) and cancer biomarkers. In order to implement the platforms for point-of-care applications we aim to fabricate a fully functional and fully integrated electrochemical biosensor devices via a three-dimensional (3D)-printing approach to provide faster results in a portable fashion. 3D printing offers significant advantages in terms of scalability, equipment size, prototyping and manufacturing speed. The first objective of the project is to design and fabricate an electrochemical portable device equipped with three conductive electrodes (working, counter, and reference) printed from a conductive filament and an electrode holder printed from a non-conductive filament. The second objective is to implement the device for electrochemical detection of heat-inactivated SARS-CoV2 using developed protocols in Mahshid Lab. The 3D printed electrochemical device will offer a wide range of application towards universal point鈥恛f鈥恈are systems.

Tasks per student

design of the holder using CAD or Solidworks fabrication of the holder using the 3D printer validation of the device with established electrochemical assays preparing solutions and reagents for the experiments.

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Number of positions

3

Academic Level

Year 3

Description

The exceptional capacity of the brain to process complex stimuli arises largely from the presence of intricate interactions between different regions. Therefore, understanding connectivity holds one of the major keys for understanding brain function in health and disease. To this end, functional magnetic resonance imaging (fMRI), which provides excellent spatial resolution is viewed as the gold standard. However, the fMRI signal is indirectly related to the underlying neural activity (through neurovascular coupling mechanisms) and it is influenced by motion and systemic physiological fluctuations (heart rate, respiration, arterial gases). One of the most promising approaches to understand these relations is to use neuroimaging modalities that complement each other. In this context, our group collects and analyzes multimodal neuroimaging data (fMRI, simultaneous EEG-fMRI, fNIRS) during resting-state conditions, as well as during a variety of physiological and sensory tasks (CO2 inhalation, breath holds, eyes open/closed, visual/auditory stimuli). In the present project, we will investigate the regional variability of physiological response functions (PRFs), which quantify the dynamic effects of physiological fluctuations on the fMRI signal, as well as the hemodynamic response function (HRF), which quantifies neurovascular coupling mechanisms. To achieve this, we will use fMRI, EEG-fMRI and EEG-fNIRS data collected at our lab, as well as publicly available data. Finally, we will specifically investigate the effects of aging on the characteristics of PRFs and HRF using the Human Connectome Project (HCP) database. Building on our previous work, we will use systems identification and signal processing techniques that are tailored to the question of interest. The proposed work yields promise for identifying more robust biomarkers for earlier/more accurate diagnosis of brain disorders as well as targets for therapeutic interventions (e.g. noninvasive brain stimulation).

Tasks per student

The students will preprocess and analyze the experimental data, using signal processing and systems identification methods. If COVID restrictions permit it, they will also help in the collection of experimental data at 苹果淫院鈥檚 General Hospital. The aim will be to better understand how physiological and neural signals are coupled with the fMRI signal, e.g. how does EEG signal power in different frequency bands affect the slow fluctuations observed with the fMRI BOLD signal and how is this coupling varying regionally?

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Number of positions

2

Academic Level

Year 3

Description

This is a creative open-ended project that aims at building and promoting an overarching understanding of design solutions in the context of evolutionary adaptations of living organisms. The project and its finished product 鈥� Nature鈥檚 Design Toolbox 鈥� will illustrate, analyze and systematize various engineering design solutions at both developmental and complex levels, considering the nontrivial and parsimonious character of biological adaptations. This project will involve astute processing of an immense volume of information, and therefore will require two students who will constantly discuss their findings and progress with each other, and will cross-validate their decisions. Consequently, this project description, tasks and deliverables apply to both students. Each student will have access to the didactic resources of 苹果淫院鈥檚 Bioengineering BIEN 200 course accrued during the Fall 2020 term, which include 51 reviews on design solutions in living organisms. Students will inspect the reviews and will parse engineering descriptors for the Nature鈥檚 Design Toolbox database that they will build. The Nature鈥檚 Design Toolbox will be an interactive searchable inventory of life form designs, organized as a multi-dimensional matrix using the following parameters: discipline (physics, chemistry, mathematics); problem (e.g., insulation, camouflage, resilience, etc.); solution (e.g., spinodal decomposition, pre-stress, positive feed-back loop, etc.); limitations (e.g., weight, speed of reaction, temperature, etc.), animal clade, habitat, trophic level, and others. The Nature鈥檚 Design Toolbox will institute a new resource for undergraduate and graduate student research training and inspiration in biological engineering and biomimetic design.

Tasks per student

Define the problem and scope of an adaptation Identify limitations imposed by evolutionary pressure and environmental factors Identify and define the design solution Identify 鈥渟pandrels鈥� as non-adaptational byproduct features Format BIEN 200 didactic materials as a hyperlinked and expandable database

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Number of positions

2

Academic Level

Year 3

Description

Background: The shape of, and the physico-chemical properties on the protein molecular surfaces govern the specific molecular interactions in protein-ligand complexes. Therefore, studies as diverse as those on protein folding, protein conformational stability, inter- and intra- protein interactions, molecular recognition and docking; as well as applications-orientated ones, such as drug design, protein and peptide solubility, crystal packing, and enzyme catalysis, benefit from an accurate and precise representation of the molecular surfaces. Furthermore, for large, intricate protein complexes, such as ion-channels, mechano-sensitive channels, or molecular chaperones, where the biomolecular functionality occurs on the inner molecular surface of the complex, makes the precision of the representation of molecular surfaces even more imperative. Objectives: Despite the urgent and important need for precision hydrophobicity, which is critical to biomolecular processes is rarely represented with precision, i.e., at atom-level, but often at aminoacid level. More importantly, while hydrophobicity is universal, e.g., applicable to proteins, DNA/RNA, lipids and sugars, it is almost never represented as such in present research, despite biomolecular processes involving all. To this end, the project aims to design a set of universal atomic hydrophobicity databases, and associated software to be used primarily in drug discovery, molecular biology studies and synthetic biology.

Tasks per student

The first major task of the project is to design a set of atomic hydrophobicities either from empirical data, or better from combinatorial molecular dynamics simulations. The second major task of the project is to design (or upgrade an existing) software program able to represent and quantify properties on any biomolecular surface. A third, possible design task is to demonstrate the superior advantage of this approach for a case study, e.g., Covid19 treatment.

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Number of positions

2

Academic Level

Year 3

Description

Background: Many mathematical and real-life problems, e.g., 鈥渢ravel salesman problem鈥� (TSP), protein structure, cryptography, cannot be solved, if reasonably large, by the present computers, which process the information sequentially (albeit with extreme precision and speed). These mathematical problems can be solved if (i) they are translated into a graph; (ii) this graph is translated into a design of a microfluidic network; and (iii) the fabricated microfluidic structure is explored by individual biological agents, e.g., microorganisms, which act as simple CPUs. Objectives: The project aims to assess the individual and collective 鈥榗omputational power鈥� of individual biological agents in optimally partitioning the available space and taking optimal decisions. The project involves, tentatively, the following modules: (i) translate the problem of interest in a graph; (ii) fabrication of the network equivalent to the graph encoding the mathematical problem; (iii) exploration of the microfluidic network by agents, e.g., bacteria; (iv) readout of the bio-computed solutions.

Tasks per student

The first major task of the project is to design a microfluidics network which will encode a mathematical problem, such as Travel Salesman鈥檚 Problem, with full consideration to fabrication, materials and scaling problems. The second major task of the project is to design the operation of such a computer, as tailored to various biological elements, e.g., bacteria, Euglena, paramecium, etc.

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Number of positions

2

Academic Level

No preference

Description

This is an image processing module of a project on dental enamel microcrack toughening using crosslinked mineral-binding protein (osteopontin), co-supervised by Dr. Reznikov (Bioengineering) and Dr. McKee (Dentistry). Samples of extracted human teeth will be scanned using micro-computed tomography before crosslinking treatment. Cracks comprising about 0.5% of the enamel volume will have their collective surface area quantified, along with specimen geometry. This information will be used for normalization of the toughening effect of the experimental treatment, to be assessed using a 3-point bending test of crosslink-treated and control specimens. Crack volume/area calculation will require unbiased and accurate segmentation of the cracks and tooth tissue on a reconstructed 3D image. Crack segmentation will be accomplished using a deep learning algorithm. Considering the unbalanced character of the segmentation target classes (i.e., relatively small crack volume with respect to specimen volume), we will design and train a customized convolutional neural network capable of identification of sparse features in a 3D image. This project module requires knowledge of digital image processing, basics of computer vision, understanding of loss functions, and familiarity with hyperparameter optimization for training of deep learning models. For normalization of the 3-point bending experimental results, the segmented images of the tooth specimens will be modeled as irregular-shaped beams. Understanding of beam theory and crack propagation theory is a strong asset.

Tasks per student

Reconstruct 3D images Create expertly-labeled training dataset (ground truth) Optimize convolutional neural network hyperparameters and loss function Train different CNNs and analyze their performance Segment experimental data (up to 50 scans) Construct digital models of scanned specimens

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Number of positions

1

Academic Level

Year 3

Description

Natural load-bearing structures often have reticulate or biphasic architecture to minimize weight and to allow for multiple functions to proceed concurrently (e.g., tree bark, bee honeycomb, vertebrate trabecular bone, sea urchin test, plant leaf vasculature). Natural reticulate structures are quasi-regular, meaning that they do not possess perfect symmetry and long-range order, but still have short-range order. For any element of such a structure, within a certain radius, there is a high probability of finding a number of nearest neighbors. For example, within a range of 50 micrometres around the center of any tree bark phloem cell, between 5 to 7 nearest neighbors can be expected. Natural reticulate structures may thus be better described by limited randomness rather than by symmetry operators. Any reticulate solid morphology can be represented as a graph (or skeleton) where the thickness of connections and the size of nodes do not matter, but the topology of the graph does 鈥� this being how many neighboring elements is each element connected to, and how are the connections related to each other. This study focuses on topological parameters of graphs, and it aims to establish to what extent randomness is tolerated or favored by natural reticulate structures in 3D. In this study, natural reticulate structures will be scanned using X-ray tomography, and their graphs will be analyzed. Artificial 3D lattices will be constructed in silico with a variable, quantifiable extent of randomness. The convergence of natural and simulated built-in lattice randomness will be described.

Tasks per student

Produce graphs of 3D images of natural reticulate structures Design simulated 2D and 3D lattices having a measurable degree of randomness Analyze graphs

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Number of positions

1

Academic Level

No preference

Description

In order to bind with the appropriate receptor of a cell in the respiratory tract and initiate infection, respiratory viruses must first pass through the mucus barrier lining the airway. Further, this must be achieved prior to clearance of the virus through mucociliary transport, suggesting a limiting timescale for viral transport through this physiologically important viscoelastic hydrogel. Here, we will study this phenomenon by analyzing numerically-generated pathogen trajectories through different simulated media. First, we will assume homogeneous environments and normal diffusion, and develop the required statistical analysis to predict first passage times and trajectory lengths for the simulated pathogens. Next, we will incorporate previously obtained microrheological data in order to consider how the actual physicochemical properties of mucin barriers alter pathogen transport.

Tasks per student

Adapt numerical code for trajectory generation and statistical analysis. Produce a next generation version of the code that uses experimental data as inputs to study particle transport in heterogeneous viscoelastic media.

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Number of positions

1

Academic Level

No preference

Description

In previous work, we developed a homogeneous epidemiological model to characterize the timing and burden of future Covid-19 cases depending on the nature of the immune response to SARS-CoV-2 infection (i.e. whether primary infections provide life-long or only partial protection from re-infection and transmission) as well as the implementation of nonpharmaceutical interventions (NPIs) and the eventual availability of a vaccine. As the pandemic unfolds, however, clear heterogeneities in the spreading dynamics and pathophysiology of Covid-19 have been observed. For instance, despite observations of a relatively highly seroprevalence of anti-SARS-CoV-2 IgG among blood donors in Kenya, reported numbers of cases and deaths in this country have remained low, supporting an impression of attenuated Covid-19 disease in Africa. Additionally, the severity of Covid-19 (which ranges from asymptomatic infections to fatal cases) is believed to be strongly linked to an individual鈥檚 age and the existence of comorbidities. In this project, we will incorporate various aspects related to demographic variation (including age structure) into the previously formulated epidemiological model. We will then seek to resolve differences in local epidemiological trajectories using this framework.

Tasks per student

Adapt existing code for case projections of Covid-19. Produce a next generation version of the code that incorporates the demographic variation described above.

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Number of positions

1

Academic Level

No preference

Description

The cell is the fundamental unit of life, yet the inner workings of the cell are far more complex than we ever imagined. Without a good model of the cell, it is difficult to develop new drugs to repair diseased cells, or build new cells to produce much-needed chemicals and materials. A key step towards building a working model of the cell is to map the complex network of interactions between thousands of tiny molecular machines in the cell called proteins. This project will focus on computer modeling of protein structures and networks. Various experimental and computational datasets on protein structures and networks will be integrated and visualized. The resulting integrated protein structures and networks will then be annotated with evolutionary and disease properties, with the aim to understand how protein structures and networks evolve, and how disruptions in protein structures and networks lead to disease.

Tasks per student

Literature review. Becoming familiar with publicly-available datasets on protein structures and networks. Becoming familiar with existing computational tools on modeling protein structures and networks. Computer programming.

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Number of positions

2

Academic Level

Year 3

Description

The motor proteins kinesin and dynein move along microtubules to transport cargoes and organize microtubules in the cell. Our goal is to understand how multiple motor proteins operate in teams, and how they are regulated to perform complex functions like cell division and directed transport. Through extending single-molecule techniques to native organelles and living cells, we have developed advanced microscopy tools to measure the regulation, motility, and forces exerted by motor proteins with unprecedented resolution, and to manipulate the system by applying external forces to the cargoes through optical tweezers and controlling motor activity using optogenetics. We will image and manipulate ensembles of kinesin and dynein as they transport native cargoes in reconstituted systems and living cells to understand how kinesin and dynein motors interact, how they are controlled to direct intracellular trafficking and cell division, and how motor proteins are misregulated in neurodegenerative disease and cancer.

Tasks per student

Student 1: Express and purify proteins and organelles. Perform single-molecule in vitro motility assays. Analyze images. Student 2: Develop micro patterned microtubule arrays to reconstitute spindle assembly in vitro.

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Number of positions

2

Academic Level

No preference

Description

The project aims at the development of (bio)materials and systems with improved optical and electrical properties for broad applicability to biosensor development, lab on a chip systems, and detection avoidance.

Tasks per student

The student will perform material synthesis and systems integration. The student will also perform experiments, collect and analyze data, prepare reports and presentations.

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Number of positions

2

Academic Level

No preference