42 research outputs found

    Stochastic modeling of intracellular signaling dynamics for the purpose of regulating endothelial cell migration

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 56-59).Effective control of cellular behaviors has serious implications in the study of biological processes and disease. However, phenotypic changes may be difficult to detect instantaneously and are usually associated with noticeable delay between input cue and output cellular response. Because of this, relying on detection of phenotypic behaviors for use in feedback control may lead to instability and decreased controller performance. In order to alleviate these issues, a new approach to regulating cell behaviors through control of intracellular signaling events is presented. Many cell behaviors are mediated by a network of intracellular protein activations that originate at the membrane in response to stimulation of cell surface receptors. Multiple protein signaling transductions occur concurrently through diverse pathways triggered by different extracellular cues. Cell behavior differs, depending on the chronological order of multiple signaling events. This thesis develops several modeling frameworks for an intracellular signaling network specific to endothelial cell migration in angiogenesis. Unlike previous works, the models developed in this thesis exploit the effect of signaling order on extracellular response. Our approach examines the transduction time associated with each pathway of a cascaded signaling network. Transduction times of multiple pathways are compared, and the probability that the multiple signaling events occur in a desired chronological order is evaluated. We begin our development with an input-output time-delay model derived from simulated data that is used to predict the optimal extracellular input intensity for a desired response. We then present a stochastic "pseudo-discrete" model of the signal transduction time. We conclude by presenting several control strategies for control of intracellular signaling events.by Michaëlle Ntala Mayalu.S.M

    Capillary characteristics in microfluidic experiments and computational simulation

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, February 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 119-128).Angiogenesis is crucial during many physiological processes, and is influenced by various biochemical and biomechanical factors. Models have proven useful in understanding the mechanisms of angiogenesis and the characteristics of the capillaries formed as part of the process. We have developed a 3D hybrid, agent-field model where individual cells are modeled as sprout-forming agents in a matrix field. Cell independence, cell-cell communication and stochastic cell response are integral parts of the model. The model simulations incorporate probabilities of an individual cell to transition into one of four states - quiescence, proliferation, migration and apoptosis. We demonstrate that several features such as continuous sprouts, cell clustering and branching that are observed in microfluidic experiments conducted under controlled conditions using few angiogenic factors can be reproduced by this model. We also identify the transition probabilities that result in specific sprout characteristics such as the length and number of continuous sprouts. We have used microfluidics to study cell migration and capillary morphogenesis. The experiments were conducted under different concentrations of VEGF and Ang I. We demonstrated that capillaries with distinct characteristics can be grown under different media conditions and that characteristics can be altered by changing these conditions. A two-channel microfluidic device fabricated in PDMS was used for all experiments. The rationale underlying the design of the experiments was twofold: the first goal was to generate reproducible and physiologically relevant results in a microfluidic device, and the second goal was to quantify the capillary characteristics and use them to estimate the transition parameters of the model. We developed stable, well-maintained sprouts by using human microvascular endothelial cells in 2.5 mg/ml dense collagen I gel and by using media supplemented with 40 ng/ml VEGF and 500 ng/ml Ang 1 for two days. It has been shown in many studies that VEGF acts as an angiogenic factor and Ang 1 acts as stabilizing factor. Here we showed that their roles are maintained in the 3D microenvironment, and the sprout characteristics obtained by using this baseline condition could be altered by changing the concentrations of these two growth factors in a systematic way. Sprout and cell characteristics obtained in the experiments and simulations were analyzed by adapting Decision Tree Analysis. This methodology provides us with a useful tool for discerning the impact of different growth factors on the process of cell migration or proliferation as they alter general sprout morphology. The imprints obtained via experiments and simulations were compared; by choosing appropriate values of the transition probabilities, the model generates capillary characteristics similar to those seen in experiments (R2 ~ 0.82- 0.99). Thus, this model can be used to cluster sprout morphology as a function of various influencing factors and, within bounds, predict if a certain growth factor will affect migration or proliferation as it impacts sprout morphology. This was demonstrated in the case of anti-angiogenic agent, PF4. We showed that at high concentration of PF4 (- 1000 ng/ ml), the transition to migration is more profoundly affected while at low concentrations of - 10 ng/ ml, PF4 does not have much of an effect on either migration or proliferation.by Anusuya Das.Ph.D

    Quantitative modeling and control of nascent sprout geometry in in vitro Angiogenesis

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 121-127).Nascent blood vessel growth in angiogenesis is a complex process involving cellular response to biochemical growth factors, degradation of the surrounding matrix, and coordinated migration of multiple endothelial cells up a growth factor gradient. Mechanistic understanding and quantitative modeling of the dominant dynamics involved in nascent vessel growth will enable new strategies for regulating vessel growth rate and geometry, and will have implications in controlling growth of complete vascular networks in many research areas, ranging from cancer treatment and wound healing to tissue engineering. In this thesis, we investigate the dynamics of nascent vessel growth in 3D microfluidic assays, formulate a quantitative process model based on our experimental characterization, and formulate a feedback approach to regulate growth. We begin by developing a new microfluidic assay consisting of a collagen gel scaffold with features to reduce assay-to-assay variability and increase experimental throughput. This high throughput assay reveals that there is an inverse relationship between nascent vessel elongation rate and diameter under diverse biochemical conditions. This finding is supported by immuno-fluorescent staining and biochemical inhibition studies, which give insight into the dominant mechanisms determining nascent vessel diameter. Based on our experimental characterization, we formulate a simple quantitative reaction-diffusion model that relates vessel diameter to elongation rate, and supports our understanding of the relevant dynamics. We conclude by formulating a model-based optimization approach for planning the optimal trajectory of elongation rate vs. time needed to obtain desired sprout geometry, and illustrate in simulation that model predictive feedback control can be used to correct for noise in the response of elongation rate to growth factor inputs.by Levi Benjamin Wood.Ph.D

    Mechano-sensing and cell migration: A 3D model approach

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    Cell migration is essential for tissue development in different physiological and pathological conditions. It is a complex process orchestrated by chemistry, biological factors, microstructure and surrounding mechanical properties. Focusing on the mechanical interactions, cells do not only exert forces on the matrix that surrounds them, but they also sense and react to mechanical cues in a process called mechano-sensing. Here, we hypothesize the involvement of mechano-sensing in the regulation of directional cell migration through a three-dimensional (3D) matrix. For this purpose, we develop a 3D numerical model of individual cell migration, which incorporates the mechano-sensing process of the cell as the main mechanism regulating its movement. Consistent with this hypothesis, we found that factors, such as substrate stiffness, boundary conditions and external forces, regulate specific and distinct cell movements

    An aptamer-based sensing platform for luteinising hormone pulsatility measurement

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    Normal fertility in human involves highly orchestrated communication across the hypothalamic-pituitary-gonadal (HPG) axis. The pulsatile release of Luteinising Hormone (LH) is a critical element for downstream regulation of sex steroid hormone synthesis and the production of mature eggs. Changes in LH pulsatile pattern have been linked to hypothalamic dysfunction, resulting in multiple reproductive and growth disorders including Polycystic Ovary Syndrome (PCOS), Hypothalamic Amenorrhea (HA), and delayed/precocious puberty. Therefore, assessing the pulsatility of LH is important not only for academic investigation of infertility, but also for clinical decisions and monitoring of treatment. However, there is currently no clinically available tool for measuring human LH pulsatility. The immunoassay system is expensive and requires large volumes of patient blood, limiting its application for LH pulsatility monitoring. In this thesis, I propose a novel method using aptamer-enabled sensing technology to develop a device platform to measure LH pulsatility. I first generated a novel aptamer binding molecule against LH by a nitrocellulose membrane-based in vitro selection then characterised its high affinity and specific binding properties by multiple biophysical/chemical methods. I then developed a sensitive electrochemical-based detection method using this aptamer. The principal mechanism is that structure switching upon binding is associated with the electron transfer rate changes of the MB redox label. I then customised this assay to numerous device platforms under our rapid prototyping strategy including 96 well automated platform, continuous sensing platform and chip-based multiple electrode platform. The best-performing device was found to be the AELECAP (Automated ELEctroChemical Aptamer Platform) – a 96-well plate based automatic micro-wire sensing platform capable of measuring a series of low volume luteinising hormone within a short time. Clinical samples were evaluated using AELECAP. A series of clinical samples were measured including LH pulsatility profile of menopause female (high LH amplitude), normal female/male (normal LH amplitude) and female with hypothalamic amenorrhea (no LH pulsatility). Total patient numbers were 12 of each type, with 50 blood samples collected every 10 mins in 8 hours. Results showed that the system can distinguish LH pulsatile pattern among the cohorts and pulsatility profiles were consistent with the result measured by clinical assays. AELECAP shows high potential as a novel approach for clinical aptamer-based sensing. AELECAP competes with current automated immunometric assays system with lower costs, lower reagent use, and a simpler setup. There is potential for this approach to be further developed as a tool for infertility research and to assist clinicians in personalised treatment with hormonal therapy.Open Acces

    Stochastic modeling and control of neural and small length scale dynamical systems

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    Recent advancements in experimental and computational techniques have created tremendous opportunities in the study of fundamental questions of science and engineering by taking the approach of stochastic modeling and control of dynamical systems. Examples include but are not limited to neural coding and emergence of behaviors in biological networks. Integrating optimal control strategies with stochastic dynamical models has ignited the development of new technologies in many emerging applications. In this direction, particular examples are brain-machine interfaces (BMIs), and systems to manipulate submicroscopic objects. The focus of this dissertation is to advance these technologies by developing optimal control strategies under various feedback scenarios and system uncertainties. Brain-machine interfaces (BMIs) establish direct communications between living brain tissue and external devices such as an artificial arm. By sensing and interpreting neuronal activity to actuate an external device, BMI-based neuroprostheses hold great promise in rehabilitating motor disabled subjects such as amputees. However, lack of the incorporation of sensory feedback, such as proprioception and tactile information, from the artificial arm back to the brain has greatly limited the widespread clinical deployment of these neuroprosthetic systems in rehabilitation. In the first part of the dissertation, we develop a systematic control-theoretic approach for a system-level rigorous analysis of BMIs under various feedback scenarios. The approach involves quantitative and qualitative analysis of single neuron and network models to the design of missing sensory feedback pathways in BMIs using optimal feedback control theory. As a part of our results, we show that the recovery of the natural performance of motor tasks in BMIs can be achieved by designing artificial sensory feedbacks in the proposed optimal control framework. The second part of the dissertation deals with developing stochastic optimal control strategies using limited feedback information for applications in neural and small length scale dynamical systems. The stochastic nature of these systems coupled with the limited feedback information has greatly restricted the direct applicability of existing control strategies in stabilizing these systems. Moreover, it has recently been recognized that the development of advanced control algorithms is essential to facilitate applications in these systems. We propose a novel broadcast stochastic optimal control strategy in a receding horizon framework to overcome existing limitations of traditional control designs. We apply this strategy to stabilize multi-agent systems and Brownian ensembles. As a part of our results, we show the optimal trapping of an ensemble of particles driven by Brownian motion in a minimum trapping region using the proposed framework

    Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

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    Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018
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