1,721,063 research outputs found
Modeling self-organized spatio-temporal patterns of PIP3 and PTEN during spontaneous cell polarization
No full textDuring spontaneous cell polarization of Dictyostelium discoideum cells, phosphatidylinositol (3,4,5)-triphoshpate (PIP3) and PTEN (phosphatase tensin homolog) have been identified as key signaling molecules which govern the process of polarization in a self-organized manner. Recent experiments have quantified the spatio-temporal dynamics of these signaling components. Surprisingly, it was found that membrane-bound PTEN can be either in a high or low state, that PIP3 waves were initiated in areas lacking PTEN through an excitable mechanism, and that PIP3 was degraded even though the PTEN concentration remained low. Here we develop a reaction-diffusion model that aims to explain these experimental findings. Our model contains bistable dynamics for PTEN, excitable dynamics for PIP3, and postulates the existence of two species of PTEN with different dephosphorylation rates. We show that our model is able to produce results that are in good qualitative agreement with the experiments, suggesting that our reaction-diffusion model underlies the self-organized spatio-temporal patterns observed in experiments
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Signaling Pathways in Living Systems
Full text linkSignaling networks are at the heart of most biological processes. In this dissertation, two classes of models with applications are discussed in the following chapters.In most signaling networks with several components, either the connections between the components or the parameters governing the reaction kinetics are not known. Given this uncertainty, Boolean networks, in which each component is either on or off, have emerged as viable alternatives. Open-source platforms of Boolean models for community use are desirable. Here, we present Boolink, a freely available graphical user interface that allows users to easily construct and analyze existing Boolean networks. We demonstrate its application using a previously published network for abscisic acid (ABA)-driven stomatal closure in Arabidopsis thaliana, and by extending the network to include CO2 regulation of stomatal movements. Predictions of the model were experimentally tested, and the model was iteratively modified based on experiments showing that ABA effectively closes Arabidopsis stomata at near-zero CO2 concentrations.When cells of the social amoeba Dictyostelium discoideum are starved of nutrients they synthesize, secrete, and relay the chemical messenger and chemoattractant cyclic Adenosine Mono Phosphate (cAMP), resulting in the establishment of periodic waves. Cells aggregate through chemotaxis towards the center of these waves. In the process, they experience an elevated background concentration of cAMP as well as multiple waves of a fixed period. We investigated in two separate studies the effect of these two changes, using waves of cAMP generated by a microfluidic device. We found that the chemotactic ability of the cells increases for small to moderate levels but collapses to zero for sufficiently high concentrations. Secondly, we found that the chemotactic ability of cells rises with the number of waves encountered by the cells provided the wave period is not large. We developed mathematical models to explain the observed trends, building on earlier work on the Local Excitation Global Inhibition (LEGI) class of models. We showed that a temporal gradient sensing mechanism underlies the wave-period-dependent rise in the chemotactic ability. The observed trends in the chemotactic ability are relevant to Dictyostelium in aiding its aggregation
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Characterizing Excitation Patterns in Cardiac Arrhythmias
Full text linkHeart rhythm disorders represent a significant global health concern, affecting millions of people worldwide and serving as a risk factor for heart failure. Despite the prevalence of these arrhythmias, broadly effective treatments remain elusive. In this work, we present the cardiac conduction system as an excitable medium whose dynamics can be described with simple mathematical models. We show that the excitation of this system can become altered through well-timed stimuli which create persistent rotational activation patterns. We describe the hypothesized relationship between these patterns and the perpetuation of fibrillation, discussing the fragmenting state of multi-wavelet reentry as well as the theory of mother rotors. To distinguish between potential mechanisms, we present results using concepts of phase synchrony to characterize clinical recordings of fibrillation. Such analysis implies a level of spatiotemporal stability inconsistent with a mechanism exclusively comprised of chaotic multi-wavelet reentry. Moreover, we outline a technique to determine continuously updating vector flow fields which align with the direction of local conduction. We show that this methodology can be used to determine the source of rotational and focal excitation patterns in an automated fashion, providing a useful means to interpret otherwise complex spatiotemporal maps. Finally, we describe the spontaneous termination of fibrillation as a stochastic event, and construct a birth-death Master equation for the number of spiral tips n during the turbulent state of spiral-defect chaos. Within this framework, we can infer important statistical quantities related to fibrillation such as the mean number of spiral tips n, the quasi-stationary distribution Pqs, and the mean episode duration t. Our results imply that t scales exponentially with the area A. We derive this scaling law using a WKB approach, which yields an effective Hamiltonian describing tip density q = n=A and fluctuation momentum p. This stochastic approach can accurately predict t, even for systems in which a multitude of episodes cannot be simulated due to prohibitive computational cost
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Models to study the mechanism of single and collective cell migration
Full text linkCell migration plays a vital role in many biological processes and the mechanism for both single cell and collective cell migration remains unclear. It is a complex multi-step procedure, including signal receiving, signal processing, force generation and when considering multicell migration, cell-cell interaction. Thus it inspires many scientists in biology as well as interdisciplinary areas to study this topic. This dissertation includes the study of cell migration from different aspects and on different scales, using quantitative models. I write them with a sequence from the smallest scale to the largest. In Chapter 2, I study the kinetics of the activation of G-protein-coupled receptors used in chemotaxis trying to explain the two activation rates observed in the experiments. In Chapter 3, I study single cell chemotaxis, focusing on the signaling networks to explain the memory effect observed in the experiments. In Chapter 4, I study the collective cell chemotaxis taking cell-cell communication into consideration, trying to find the possible minimal network topologies for the signal processing. Finally, in Chapter 5, I study multi-cell migration on a much larger scale with thousands of cells and use a simplified model which focuses on the different kinds of forces applied on the cells to study the relation between single cell properties and large scale behaviors shown in the multi-cell migration. All these works provide some new knowledge on part of the mechanism controlling cell migration
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Development of Models to Describe the Electrical Properties of Cardiomyocytes
Full text linkThe membrane electric potential of the human heart depends on numerous of ionic channels and concentrations. Spikes in the membrane potential are responsible for the contracting of the heart, but disruptions to the membrane potential are also responsible for disorders. A cardiac arrhythmia is an irregular heartbeat that is caused by an abnormal activation pattern in the membrane potential. This dissertation focuses on the development of computational models that describe the membrane potential, and examines the conditions for when an arrhythmia can occur. We begin by comparing the efficacy of a highly detailed model, and a very simple one, to describe patient specific quantities of the membrane potential. From this we conclude that increased complexity does not improve the model's ability to describe the data. Next, we use a unique approach of reducing a detailed model in order to develop our own simplified model. In this way we remove the variables and equations that are not necessary, while still preserving physical relevance for the parameters that are most essential to describing the voltage. Finally, we examine the spiral wave dynamics typically seen in cardiac systems, and look to understand when these spirals can become chaotic. Surprisingly, a single spiral can become chaotic without breaking up into multiple waves. Together these results offer new insights into the physics of cardiac dynamics, and present new possibilities for future treatment of cardiac arrhythmias
Eukaryotic Cell Migration — from Single Cell to Collective Behavior
Full text linkCell migration is the movement of a single cell or group of cells, usually in response to various environmental cues. It is crucial in many biological processes, including morphogenesis, immune response and metastasis of cancer cells. This dissertation studies eukaryotic cell migration from single cell level to collective behavior using both experiments and theoretical models.
In Chapter 2, we study single cell migration of the social amoeba Dictyostelium discoideum and investigate the effect of background chemoattractant concentration on cellular memory in eukaryotic chemotaxis. Our results suggest that aggregation of Dictyostelium cells can be facilitated by a rising level of chemoattractant during its developmental program.
In Chapter 3, we study the collective behavior of the social amoeba Dictyostelium discoideum during its multicellular mound stage. We find tight correlation between the traction force, signaling activity and cell velocity in the mound, all showing oscillations in their magnitude with the same period. With our mathematical model and perturbation experiments, we show that collective cell motion is crucial in setting up a persistent signaling vortex state within the mound.
In Chapter 4, we study the collective behavior of breast cancer cells (MDA-MB-231) and liver cancer cells (SK-HEP-1) during their proliferation in dense extracellular matrices (ECMs). We find the formation of two distinct morphologies and migration modes, namely rotational spheroids and invasive networks. Our experimental and numerical results show that the localization of matrix-degrading enzymes is a key factor in distinguishing formation of the distinct structures and migration modes
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Applications of Mathematical Physics to Quantitative Biology
Full text linkInterdisciplinary investigation has the potential to advance all fields involved.In this dissertation, three distinct fields of Quantitative Biology are discussed and advanced incrementally using the general tools of Mathematical Physics. Chapter one applies reaction-diffusion equations to explain the dispersal of cells
by localized degradation of a chemoattractant, which could explain the migration of leukocytes from the thymus and be a mechanism for morphogenesis. Chapter two investigates a particle model wherein an attractive force explains the termination of atrial fibrillation. Atrial fibrillation— the most common cardiac arrhythmia in the world with approximately 30 million patients in 2010— is associated with increased morbidity and mortality. Chapter three applies machine learning to explain social recognition in primate hippocampus, showing that cross-modal representations of identity can be achieved by at least two distinct neural mechanisms and that these representations comprise multiple social categories that reflect different relationships. Together, these chapters demonstrate the general capacity of Mathematical Physics to advance Quantitative Biology in addition to the capacity for Quantitative Biology to motivate novel analytic results and analyses within Mathematical Physics
Cell substratum adhesion during early development of Dictyostelium discoideum.
No full textVegetative and developed amoebae of Dictyostelium discoideum gain traction and move rapidly on a wide range of substrata without forming focal adhesions. We used two independent assays to quantify cell-substrate adhesion in mutants and in wild-type cells as a function of development. Using a microfluidic device that generates a range of hydrodynamic shear stress, we found that substratum adhesion decreases at least 10 fold during the first 6 hr of development of wild type cells. This result was confirmed using a single-cell assay in which cells were attached to the cantilever of an atomic force probe and allowed to adhere to untreated glass surfaces before being retracted. Both of these assays showed that the decrease in substratum adhesion was dependent on the cAMP receptor CAR1 which triggers development. Vegetative cells missing talin as the result of a mutation in talA exhibited slightly reduced adhesive properties compared to vegetative wild-type cells. In sharp contrast to wild-type cells, however, these talA mutant cells did not show further reduction of adhesion during development such that after 5 hr of development they were significantly more adhesive than developed wild type cells. In addition, both assays showed that substrate adhesion was reduced in 0 hr cells when the actin cytoskeleton was disrupted by latrunculin. Consistent with previous observations, substrate adhesion was also reduced in 0 hr cells lacking the membrane proteins SadA or SibA as the result of mutations in sadA or sibA. However, there was no difference in the adhesion properties between wild type AX3 cells and these mutant cells after 6 hr of development, suggesting that neither SibA nor SadA play an essential role in substratum adhesion during aggregation. Our results provide a quantitative framework for further studies of cell substratum adhesion in Dictyostelium
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Intermittent trapping of spiral waves in a cardiac model.
Full text linkSpiral waves are found in many excitable systems and are thought to play a role in the incoherent electrical activation that underlies cardiac arrhythmias. It is well-known that spiral waves can be permanently trapped by local heterogeneities. In this paper, we demonstrate that spiral waves can also be intermittently trapped by such heterogeneities. Using simulations of a cardiac model in two dimensions, we show that a tissue heterogeneity of sufficient strength or size can result in a spiral wave that is trapped for a few rotations, after which it dislodges and meanders away from the heterogeneity. We also show that these results can be captured by a particle model in which the particle represents the spiral wave tip. For both models, we construct a phase diagram which quantifies which parameter combinations of heterogeneity size and strength result in permanent, intermittent, or no trapping. Our results are consistent with clinical observations in patients with atrial fibrillation that showed that spiral wave reentry can be intermittent
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