OTHE Posters

eSMB2020 eSMB2020 Follow 2:30 - 3:30pm, Monday - Wednesday
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  1. Alexander P Hoover (OTHE)

    University of Akron
    "Exploring Neuromechanical Resonance in Jellyfish Locomotion"
    In order for an organism to have a robust mode of locomotion, their neuromuscular organization must be adaptable in a changing environment. In jellyfish, the activation and release of muscular tension is governed by the interaction of pacemakers with the underlying motor nerve net that communicates with the musculature. This set of equally-spaced pacemakers located at bell rim alter their firing frequency in response to environmental cues, forming a distributed mechanism to control the bell's muscular contraction. The relative simplicity of the jellyfish nervous system presents mathematicians with the opportunity to examine an intriguing multi-scale, multi-physics system with many potential applications to soft-body robotics and tissue-engineered pumps. In this talk, we explore the control of medusan neuromuscular activation in with a model jellyfish bell immersed in a viscous fluid and use numerical simulations to describe the interplay between active muscle contraction, passive body elasticity, and fluid forces. The fully-coupled fluid structure interaction problem is resolved using an adaptive and parallelized version of the immersed boundary method (IBAMR). This model is then used to explore the interplay between the speed of neuromechanical activation, fluid dynamics, and the material properties of the bell.

  2. Ananta Samrajya Shri Kishore Hari (OTHE)

    "Identifying inhibitors of epithelial–mesenchymal plasticity using a network topology-based approach"
    Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges, such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance. Various biochemical networks have been identified to contribute to phenotypic plasticity, but how plasticity emerges from the dynamics of these networks remains elusive. Here, we investigated the dynamics of various regulatory networks implicated in Epithelial–mesenchymal plasticity (EMP)—an important arm of phenotypic plasticity—through two different mathematical modelling frameworks: a discrete, parameter-independent framework (Boolean) and a continuous, parameter-agnostic modelling framework (RACIPE). Results from either framework in terms of phenotypic distributions obtained from a given EMP network are qualitatively similar and suggest that these networks are multi-stable and can give rise to phenotypic plasticity. Neither method requires specific kinetic parameters, thus our results emphasize that EMP can emerge through these networks over a wide range of parameter sets, elucidating the importance of network topology in enabling phenotypic plasticity. Furthermore, we show that the ability to exhibit phenotypic plasticity correlates positively with the number of positive feedback loops in a given network. These results pave a way toward an unorthodox network topology-based approach to identify crucial links in a given EMP network that can reduce phenotypic plasticity and possibly inhibit metastasis—by reducing the number of positive feedback loops.

  3. Anca Radulescu (OTHE)

    SUNY New Paltz
    "Effects of local mutations in quadratic iterations"
    The study of copying mechanisms is of great importance to genetics. We study in a theoretical system how a mutation (replication error) affects the temporal evolution of the system, on both a local and global scale (from tumor formation to overall systemic unsustainability). We introduce a new mathematical framework for studying replication mechanisms, in the form of discrete iterations of complex quadratic maps. This approach builds upon a century of existing knowledge of iterated maps towards obtaining results with potential impact on applications. More specifically, our modeling framework considers a “correct” function acting on the complex plane (representing the space of genes to be copied) and a “mutation,” acting at a specific focal point, of a given size r, and moves radially toward an outer radius R. We use the Julia set of the system to quantify simultaneously the long-term behavior of the entire space under such transformations. We analyze how the position, timing and size of the mutation can alter the topology of the Julia set, hence the system’s long-term evolution, its progression into disease, but also its ability to recover or heal. In the context of genetics, such results may help shed some light on aspects such as the importance of location, size and type of mutation when evaluating a system’s prognosis, and of customizing timing of treatment to address each specific situation. Our current work is a proof of principle. Once these aspects are understood theoretically, they can be further applied to empirically driven genetic models, validated with data and used for predictions.

  4. Angela L Moreno (OTHE)

    Federal University of Alfenas
    "ART Neural Networks in the Classification of Spinal Pathologies"
    Spinal diseases are among the significant public health problems and have a negative impact on patients' quality of life. Of these diseases, herniated disc and spondylolisthesis are examples of spinal pathologies that cause severe pain. Currently, in various medical problems related to the diagnosis of diseases, Machine Learning techniques have been used, especially Artificial Neural Networks. From the attributes of the spine such as pelvic incidence angle, pelvic tilt, sacral angulation, pelvic radius, lumbar lordosis angle, and degree of sliding, pattern recognition techniques can be employed to classify herniated disc pathologies and spondylolisthesis. Thus, this paper presents the results obtained by using neural networks of the Adaptive Resonance Theory (ART) family for the classification of spinal pathologies, comparing the results obtained by the different ART networks with those obtained in the literature. Using a neural network in a classification problem has the advantage of robust, stable, fast models that are capable of classification even with little data about the problem. In particular, ART Fast networks are characterized by the ability, even from a few data, to classify the data and, according to the network, will expand from the insertion of new data, improving the chance right. It is also noteworthy that ART extit {Fast} networks perform the classification process faster than traditional ART networks, maintaining the number of hits. The methodology adopted is based on the implementation of ART networks using the Vertebral Column Data Set database, available in the UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/. The results obtained by the network meant satisfactory, obtaining an accuracy of 91.26% for the binary classification problem and 90.97% for the ternary.

  5. Atanaska Dobreva (OTHE)

    "Mathematical modeling of photoreceptor metabolism"
    Photoreceptors are the sensory cells of the eye and have the most important role in vision. They convert light to electrical signals, which are sent to the brain via the optic nerve. Vision deterioration or blindness occur if the vitality of photoreceptors is compromised. To understand how to mitigate such pathological cases, it is essential to study the metabolism of photoreceptors, as this is the factor of greatest importance for photoreceptor vitality. We develop a mathematical model of nonlinear ordinary differential equations to describe metabolic dynamics in a single photoreceptor, focusing on key metabolites, such as glucose, pyruvate and lactate. Using bifurcation techniques, we find that the model has a bistable regime, biologically corresponding to a healthy versus a pathological state. We also conduct sensitivity analysis to determine which processes have the largest impact on the photoreceptor metabolic system. The results indicate that of greatest importance are the pathways linking photoreceptor metabolism with the metabolism of the retinal pigment epithelium, a cellular layer in the retina with which photoreceptors have a reciprocal resource relation.

  6. Baeckkyoung Sung (OTHE)

    KIST Europe/ UST
    "Mathematical modeling of a temperature-sensitive and tissue-mimicking gel matrix"
    Miniaturized biopolymer gel systems have been attracting interests for the application to regenerative medicine, due to their physiological compatibility/sensitivity and rapid kinetics with response to external stimuli. For explaining such responsivity in terms of gel thermodynamics and mechanics, classical mean-field Flory-Huggins-Rehner theory has long been developed with various analytical and numerical modifications. In this work, we present a novel mathematical model on the volume phase transitions of biological hybrid gels as a function of temperature. In order to mimic living soft tissues, the biological microgels are designed to comprise 3D network of extracellular matrix (ECM) protein chains such as collagen and gelatin, which are covalently cross-linked and remain swollen in aqueous media. Within the network, thermoresponsive synthetic polymer chains are doped by physical entrapment and chemical conjugations. Based on the Flory’s framework, our analytical model phenomenologically predicts well-defined volume phase behaviors of the 3D tissue mimics with response to the change in ambient thermodynamic parameters.

  7. Benedikt Sabass (OTHE)

    Forschungszentrum Jülich, LMU Munich
    "Molecular mechanics of type-IV pili driven migration of P. aeruginosa"
    Bacteria can generate mechanical forces that are important for the colonization of surfaces, formation of biofilms, and infection of host cells. In Gram-negative bacterial pathogens, such as Pseudomonas aeruginosa, forces result from ATP-hydrolysis-driven extension-retraction cycles of extracellular filaments called type-IV pili. How bacteria adapt their pilus-based behavior to the mechanical environment is not known. Here, we show that the early stage of surface colonization by P. aeruginosa is modulated by substrate-dependent pilus activity. Our experimental data reveals a complex response of the bacterial migration machinery to substrate properties, including adaptation of the dynamics of pili, their spatial arrangement, and their number. The combination of experimental data with mathematical modeling reveals a comprehensive picture of the interplay of active and passive molecular mechanisms during migration of P. aeruginosa on solid substrates.

  8. Calmelet Jeanne Colette (OTHE)

    California State University Chico
    "Surfactant Driven Flows in Respiratory and Ocular Regions"
    Jensen equations describe the dynamics of an exogenous surfactant moving on the top of an endogenous fluid layer in biological systems. The fluid motion is caused by the decrease in surface tension at the free surface of the thin film at the region of contact with the surfactant. The variations in space and time of the surfactant concentration and film height are analyzed under different geometries. We focus our attention on two biological applications; first, on the respiratory system, where an exogenous surfactant is inserted on the lining of a respiratory airway wall. Second, on the ocular region, where a surfactant is instilled on the eye lining. We conclude that the presence of the surfactant increases the fluid flow of the lining and therefore, can be used as therapeutic agent to prevent potential film rupture.

  9. DaeWook Kim (OTHE)

    "Systems pharmacology model reveals the sources of the inter- and intraspecies variability in drug efficacy"
    The majority of previous studies investigate the drug efficacy only in nocturnal species (e.g. mice) although humans are diurnal. Here, using diurnal monkeys, we examine the effect of a daily (circadian) clock-modulator drug and find the high variability in its effect between diurnal monkeys and nocturnal mice. To identify the source of the interspecies variability, we used the systems pharmacology model, which accurately simulates the intracellular action of the drug and thus its effect in the circadian clock. This revealed that the interspecies variability in the drug effect is due to the different sensitivity of nocturnal and diurnal animals to environment light, the natural clock-modulator. Furthermore, via a combination of the model simulation and experiment, we found the molecular biomarker to predict the drug effect, which explains the high interindividual variability in the drug response. Based on these findings, we developed a model-based precision medicine strategy to treat circadian disruption. Our works show how the mathematical model can be used to reveal an unrecognized biological variable in drug efficacy translation between nocturnal and diurnal animals and enable precision medicine.

  10. Daniel Koch (OTHE)

    King's College London
    "In signal transduction, all you need are oligomers! Dynamic signal encoding, homeostasis, bistability and more"
    Homo-oligomerisation of proteins is a ubiquitous phenomenon whose exact role remains unclear in many cases. This talk will explore general dynamical mathematical models of homo-oligomerisation. I show that homo-oligomerisation on its own allows for a remarkable variety of complex dynamic and steady state regulatory behaviour such as transient overshoots or homeostatic control of monomer concentration. Post-translational modifications could make homo-oligomerisation even more versatile: by enabling pseudo-multisite modification and kinetic pseudo-cooperativity via multi-enzyme regulation, homo-oligomerisation can lead to bistability, thereby constituting a novel motif for bistable modification reactions. If modification and demodification follow different kinetic mechanisms the modification status of homo-oligomers can furthermore exhibit sustained oscillations. Due to these potential signal processing capabilities, homo-oligomerisation could play far more versatile roles in biochemical signal transduction than previously appreciated.

  11. Diego Samuel Rodrigues (OTHE)

    Universidade Estadual de Campinas
    "Mathematical modeling of pharmacokinetic profiles of magnetic nanoparticles acquired by multichannel alternating current biosusceptrometry"
    This study is about a novel ordinary differential equation model aimed at describing in vivo biodistribution of tracer magnetic nanoparticles (MNP). The proposed pharmacokinetic model is built based on in vivo experimental data gathered from images of alternating current biosusceptometry. Three compartments are considered: the one in which MPN is found free the blood, another for a reversible bound state of MNP with the liver sinusoid, and a third compartment for the MNP permanently absorbed by the Kupffer cells. As a result, the proposed model allowed us to suitably describe the experimental pharmacokinetic profiles, providing a promising theoretical-quantitative basis for the results of the experiments [1]. Besides, it also suggests that the multichannel biosusceptrometry system is a valuable imaging device to assess in vivo and real-time pharmacokinetics. As the latter was the main purpose of the first publication in the subject [1], further developments are needed to improve the model's parameter estimation using a Bayesian framework. The author intends to implement this last idea soon. References: [1] Soares, G.; Próspero, A.; Calabresi, M.; Rodrigues, D. S.; Simões, L.; Quini, C.; Matos, R.; Pinto, L.; Sousa, A.; Bakuzis, A.; Mancera, P.; Miranda, J. R. Multichannel AC biosusceptometry system to map biodistribution and assess the pharmacokinetic profile of magnetic nanoparticles by imaging. IEEE Transactions on NanoBioscience, v. 18, 456–462, 2019. doi.org/10.1109/TNB.2019.2912073. Impact Factor by JCR (2018): 1.927.

  12. Dominic Olver (OTHE)

    University of Saskatchewan
    "Time Dependent Osmotic Damage in Sea Urchin Oocytes"
    Most cryobiological protocols require loading and unloading of cryoprotective agents (CPAs) to mitigate ice damage during the freezing and thawing process. However, CPAs change the osmolality of the solution creating an osmotic gradient across the cell membrane, causing large volumetric changes. Classically, mechanical damages due to swelling or shrinking have been thought to have a constant osmotic tolerance limit (E.g. 20% reduction in survival of the population at a given hyper and hyposmolality), which are crucial in determining optimized cryoprotocols. Here we show that osmotic damage is not dependent solely on volume deviance for sea urchin (Paracentrotus lividus) oocytes, but instead osmotic damage is time-dependent. We exposed urchin oocytes (n >= 100 per treatment with 3 replicates) to two hypertonic treatments at differing osmolalities (1500, 2000, and 2500 mOsm/kg). The hypertonic solutions either had NaCl or Sucrose added to holding medium (Sea water 1000 mOsm/kg) to obtain the desired osmolality. The exposure duration periods were for 2, 6, 15, 30, 50, 75, and 90 minutes. We tested hypotonic damage by diluting the holding medium with DI water exposing them to osmolalities of 800, 700, 600, and 500 mOsm/kg. After exposure, oocytes were returned to isosmotic holding media, in vitro fertilization was performed, and development to the 4-arm-Pluteus stage was assessed at 48 h. We fit these data to a mathematical model of population cell death that is proportional to the integration of the absolute value of the isosmotic volume minus the cell volume throughout time. This model works well (Adjusted R2 values of 0.97, 0.96, and 0.76 for DI water, NaCl, and Sucrose respectively) to describe osmotic related damage across multiple concentrations and solution types. The next step is to include this novel model as a refined metric of mechanical or osmotic stress instead of standard osmotic tolerance limit models when determining optimal CPA equilibration protocols using cytotoxicity cost functions. This may result in more accurate models of cell damage, and better optimized protocols for loading and unloading of CPAs.

  13. Euan Smithers (OTHE)

    University of Birmingham
    "How do plant leaf pavement cells form puzzle piece like shapes? Using a multi-model approach to simulate chemical and visco-elastic mechanical processes and experimental methods to discover their secrets."
    Pavement cells in the plant leaf epidermis form interesting and intriguing interlocking puzzle like shapes with undulations of lobes and indents. However, no one has been able to fully explain how these shapes form. There are two possible pathways of pavement cell development, one involving a combination of plant Rho like GTPases signalling proteins and cytoskeleton components, specifically microtubules which could provide a feedback loop and the second being possible mechanical effects from the tissue. As a result, we have developed three models, one to model microtubule behaviour, the second to model the protein signalling dynamics and the third to model the mechanics of the cells, using a stochastic network, reaction diffusion equations solved via the finite element method and a visco-elastic vertex element model. I shall also outline some of the experimental procedures we have carried out to test how pavement cells develop. We can demonstrate that the signalling pathways provide a feedback loop to sustain pavement cell shape, but don’t initiate the shape, while the mechanical effects from the tissue can initiate pavement cell lobes.

  14. Haryana Thomas (OTHE)

    Oklahoma State University
    "Modeling Cellular Signaling and Mesangial Fibrosis during Diabetic Kidney"
    In the U.S. alone over 250,000 people use dialysis or have received a kidney transplant due to diabetic kidney failure. Although we have come a long way in the treatment of diabetes, kidney failure due to diabetic kidney damage is still prevalent, and the need for increasing our understanding of kidney damage to enable the development of better treatment methods is ever present. Thus the goal of this research is to develop computational models to better understand the kidney damage that occurs due to diabetic kidney disease. In the kidney glomerulus lies a network of capillaries that are surrounded by interstitial tissue called the mesangium. In health, the mesangium acts as a support for the capillaries; however, during diabetic kidney disease, the mesangium expands due to excess accumulation of collagen and causes damage to the cellular environment around it. This mesangial expansion is not only a hallmark of kidneys damaged by diabetes but also many other chronic kidney diseases that lead to kidney failure. As such there has been a lot of research effort in trying to figure out the cause of the mesangial expansion. Researchers have found high glucose-induced dysfunction in the mesangial cell, a cell native to the mesangium, to be one of the main reasons for mesangial expansion. The mesangial cell dysfunction is mediated by the overstimulation of key signaling and cellular communication molecules such as TGF-B, and Ang II which play a key role in perturbing the function of downstream collagen metabolism molecules such as MMP, and TIMP leading to the accumulation of excess collagen. The complexity of the interactions necessitates the development of computational models to understand the whole, yet there are few computational models of mesangial expansion and even fewer that study the effect of the mesangial expansion on cellular communication and signaling in the glomerulus. In this work, we present a computational model of mesangial expansion to study its effect on cellular signaling. Previously, researchers have developed computational models of mesangial expansion to understand its impact on the accumulation of certain macromolecules whose accumulation has been shown to lead to glomerular damage. Our computational model builds on such a model to elucidate the impact of mesangial cell mediated mesangial expansion on cellular signaling through multiscale modeling of ECM remodeling and macromolecular transport. We are extending the previous model by incorporating a cellular environment using the Cellular Potts model, modeling mesangial expansion using fundamental biological principles of collagen fiber growth and accumulation, solving macromolecular transport equations using a solver and linking them all using python and CompuCell3D, a multiscale tissue simulation software. The model captures mesangial expansion and provides insight into cellular communication.

  15. Ielyaas Cloete (OTHE)

    University of Auckland
    "Not all hormone receptors are created equal"
    Calcium in hepatocytes modulates diverse functions, including bile secretion, glucose and energy metabolism and vesicular trafficking. A major question in the study of calcium signalling in hepatocytes is how these distinct cellular processes are controlled and organised via coordinated spatial and temporal calcium signals. Downstream cellular responses are controlled via intracellular calcium oscillations, but the underlying mechanisms which shape these oscillations have yet to be elucidated. In particular, we are interested in the effect of protein kinase C (PKC) on the purinergic family of receptors. Recent data has shown that the activation of different receptors within the family of purinergic receptors generate qualitatively different calcium responses. It is believed that PKC differentially regulates each receptor resulting in distinct calcium response patterns. Furthermore, multiple pools of PKC, with unique activation pathways, are understood to exist in the cell with discrete downstream targets. We discuss recent progress in construction and analysis of a model of calcium oscillations that incorporates the new experimental results about likely feedback mechanisms in hepatocytes. Our model suggests that multiple, uniquely activated, PKC feedback loops acting on unique cellular substrates, present in the cell coordinate to determine the qualitative behaviour of calcium oscillations in hepatocytes.

  16. Jackelyn M Kembro (OTHE)

    National Scientific and Technical Research Council (CONICET)
    "Accelerometers as a tool to characterize reproductive behavior within social groups in long term experiments: the case of the Japanese Quill"
    Accelerometers are devices that convert movement into three signals belonging to each component of the acceleration vector at a high acquisition rate, up to 25 data per second. When they are fixed to an animal, each action performed by the individual leads to a particular shape in these signals that, when depicted in a computer, can be isolated and classified. Hence, accelerometer recordings can be combined with machine learning techniques in order to automatically classify signals into behavioral categories. This is particularly useful in the context of long-term social behavior studies in large or natural environments were recording from visual observation is difficult and time consuming. Herein, we placed accelerometers on the back of adult male quails (Coturnix japonica) to register their activity when they are released into a home box containing two female quails during a 1-hour period. At the same time, the experiment was video-recorded to obtain a time series of the different behaviors performed by the male and their corresponding duration by direct inspection. The accelerometric signals and behavioral time series obtained were used to train a neuronal network. Our neuronal network was able to classify reproductive behavior of males at high temporal resolution. In particular, we showed, first, that the duration of some reproductive events can be much shorter than those reported previously and transitions between different behaviors are very fast (of the order of ~100ms). Second, reproductive behavior appears to begins earlier and finish later than it is possible to observe visually using video recordings. Our results show that combining accelerometer recordings with neural network processing is a powerful method to automatically register reproductive behaviors within social groups with high accuracy. This is of particular importance given that it has the potential to replace registering from visual observation of social behavior. Moreover, the long, high resolution reproductive time series obtained by this approach can be useful for studding long-term reproductive behavioral rhythms in poultry.

  17. Janina Hesse (OTHE)

    Charité University Hospital Berlin
    "Diurnal variation in gene expression and sports performance: a matter of timing?"
    Virtually all living organisms show oscillations in physiology that track the daily rhythm of light and darkness. In humans, not only the sleep-wake cycle has a period of about 24 hours, also basic physiological and cellular processes such as core-body temperature, cortisol levels or cell cycle and metabolism oscillate accordingly. While circadian oscillations are well studied at many levels from genes to behaviour, the impact of the molecular profile on the behavioural output has so far been hardly studied in humans. With the aim to establish a relation between genes and behaviour, we analysed time series data sets of gene expression and sports performance from the same human subjects. Gene expression of two specific core-clock genes, measured via RT-PCR, shows circadian variation in samples of bodily fluids, which constitutes a practical source for human genetic material. Sports performance was evaluated by a set of three standardized tests probing strength and endurance. While we find overall best sports performance in the afternoon, the individual best performance times, which show larger variations, can be predicted by a machine learning approach. Besides best performance time, the variance in sports performance over the day is of interest. Here we discuss our latest findings in the field and their putative benefit for professional athletes, as well as their general implications for our well-being.

  18. Jonas Knoch (OTHE)

    Friedrich-Alexander-Universität Erlangen-Nürnberg
    "A mathematical model of HIF-1 regulated cellular energy metabolism"
    We formulate a mathematical model of hypoxia-inducible factor 1 (HIF-1) mediated regulation of cellular energy metabolism, describing the reprogramming of cell metabolic processes from oxidative phosphorylation to glycolysis under reduced oxygen levels as it can be observed in many diseases such as sepsis or cancer. The model considers the dynamics of fifteen biochemical species and the proton concentration at the inner mitochondrial membrane with the underlying reaction processes localized in three intracellular compartments, namely cytoplasm, mitochondria and nucleus. More than sixty parameters of the model were calibrated using both the published data and a system steady-state based identification procedure. The model is validated by generating predictions which can be compared to empirical observations.

  19. Jonggul Lee (OTHE)

    National Institute for Mathematical Sciences
    "Machine Learning for Risk Prediction of Highly Pathogenic Avian In uenza in the Republic of Korea"
    There have been 7 outbreaks of highly pathogenic avian influenza (HPAI) in the Republic of Korea since 2003 resulting a serious economic burden on the poultry industry. Due to uncertainty of transmission from migratory birds, which is known as the main source of infection, and transmission between poultry farms linked by livestock-related vehicles, it is very difficult to predict and respond to the epidemic. In this work we aim at forecasting spatio-temporal pattern of HPAI occurrence and identifying risk factors with a machine learning technique based on Random Forest regression. Historical data on HPAI outbreaks in 250 regions from 2014 to 2017 are used as a target. Three types of features are used to train the model: epidemiological features related to information on farms infected in the past, demographic features including the number (density) of farms regarding breeding species (chicken and duck) in an area, and geographical features including the habitats of migratory birds and slaughterhouses. The model provides a highly accurate prediction of both temporal and spatial patterns of HPAI outbreaks. Furthermore, we investigate feature importance to explain which features contribute most to the local outbreak of HPAI. Results show that epidemiological features mainly contribute to prediction of the temporal pattern, while the demographic and environmental features mainly contribute to prediction of the spatial distribution.

  20. Juan Calvo (OTHE)

    Universidad de Granada
    "The initial-boundary value problem for the Lifshitz-Slyozov system with inflow boundary conditions: Analysis and applications"
    The Lifshitz-Slyozov system describes the temporal evolution of a mixture of particles ('atoms') and aggregates, where individual atoms can attach to or detach from already existing clusters. The aggregate distribution follows a transport equation with respect to a size variable, whose transport rates are coupled to the dynamic of atoms through a mass conservation relation. Being a system traditionally designed to model phase transitions, the attachment and detachment rates proposed by Lifshitz and Slyozov are such that no boundary condition at zero size is needed. However, the scope of this model is becoming wider (e.g. descriptions of protein polymerization or tentative applications to oceanography). These situations impose attachment and detachment rates that requiere a boundary condition at zero size, which is intrepreted as the synthesis of new clusters from atoms by a nucleation process. Up to date, the mathematical results on this new setting are scarce. In this contribution we study existence and uniquenes of local-in-time solutions when nonlinear boundary conditions are used, together with continuation criteria and results on long-time behavior. We are able to deal with attachment and detachment rates that may eventually lack Lipschitz regularity, like power-law rates. This requires a careful analysis of the characteristic curves associated to the transport process.

  21. Keertana Yalamanchili (OTHE)

    "Assessing the Efficacy of Various NOX Enzyme Inhibitors as Potential Treatments for Ischemic Stroke in Silico"
    Ischemic stroke occurs when blood flow to the brain is interrupted, causing brain damage. There is evidence that ROS (reactive oxygen species) are produced by the enzyme family NADPH oxidase (NOX) following ischemic stroke, which leads to further brain injury. The ADMET profiles of each inhibitor was taken, in which four classifications, namely applicability domain, human intestinal absorption, blood brain barrier, and human oral bioavailability, were observed. Then, AutoDock Vina was used to model the docking of the inhibitors: VAS2870, GSK2795039, Apocynin, and AEBSF to NOX2, an isoform of the NOX family. The binding affinities of each of the inhibitors to NOX2 were recorded, and the value was used to calculate the Ki value of each inhibitor. It was found that VAS2870 and Apocynin were the most potent NOX2 inhibitors (p < 0.001). All the inhibitors did inhibit the NOX2 enzymes, and they all had favorable ADMET profiles. This study helps corroborate previous in vivo and in vitro studies in an in silico format, and can be used towards evidence for developing drugs to treat ischemic stroke.

  22. Kento Nakamura (OTHE)

    University of Tokyo
    "Optimality of the sensory system of Escherichia coli"
    Escherichia coli chemotaxis is one of the model systems from which we can obtain insights for understanding the biological sensory system. To realize chemotaxis, an E. coli cell has to detect temporal changes of ligand concentration caused by its motion based on noisy sensing of the ligand in the environment. How efficiently is the sensory system of E. coli designed to infer the dynamically changing environment behind the noise? While several works have analyzed the effect of noise on the signaling pathway and predicted the necessary feature for the sensory system to operate against noise, these analyses relied on the linear response approximation, which may limit the predictive capacity of the model. In this work, we utilize the nonlinear filtering theory to explore the requisite for information acquisition in chemotaxis. First, we derive the optimal dynamics for extracting the necessary information for chemotaxis, i.e., temporal concentration change. Then, we show how the derived dynamics can be linked to a biochemical model of the sensory system of E. coli. Furthermore, we demonstrate that the optimal dynamics obtained can reproduce a nonlinear response relation observed experimentally. These results indicate that the bacterial sensory system may be developed so as to obtain environmental information from a noisy and dynamic signal.

  23. Khem Raj Ghusinga (OTHE)

    "Molecular switch architecture determines response properties of signaling pathways"
    Many intracellular signaling pathways are composed of molecular switches, proteins that transition between two states—on and off. Typically, signaling is initiated when an external stimulus activates its cognate receptor that in turn causes downstream switches to transition from off to on using one of the following mechanisms: activation, in which the transition rate from the off state to the on state increases; derepression, in which the transition rate from the on state to the off state decreases; and concerted, in which activation and derepression operate simultaneously. We use mathematical modeling to compare these signaling mechanisms in terms of their dose-response curves, response times, and abilities to process upstream fluctuations. Our analysis elucidates several general principles. First, activation increases the sensitivity of the pathway, whereas derepression decreases sensitivity. Second, activation generates response times that decrease with signal strength, whereas derepression causes response times to increase with signal strength. These opposing features allow the concerted mechanism to not only show dose-response alignment, but also to decouple the response time from stimulus strength. However, these potentially beneficial properties come at the expense of increased susceptibility to upstream fluctuations. In addition to above response metrics, we also examine the effect of receptor removal on switches governed by activation and derepression. We find that if inactive (active) receptors are preferentially removed then activation (derepression) exhibits a sustained response whereas derepression (activation) adapts. In total, we show how the architecture of molecular switches govern their response properties. We also discuss the biological implications of our findings.

  24. Marta Helena Oliveira (OTHE)

    The skin wound healing is a complex process divided into three overlapping and interdependent phases (inflammatory, proliferative and remodelling). The inflammatory response must occur rapidly to avoid chronic inflammation and it depends on biochemical, molecular and cellular events. The effective crosstalk between leukocytes and cytokines (proinflammatory and anti-inflammatory) lead to correct healing of the lesions. We considered a system of ordinary differential equations to model the inflammatory phase of skin wound healing process under treatments with oleoresin and hydroalcoholic cream extract from Copaifera langsdorffii. The model can exhibit two stable steady states corresponding to healthy or unhealthy skin, nevertheless this study has been concentrated in a parameter search to healthy state in order to verify the treatment efficiency comparing the results of the oleoresin against hydroalcoholic extract. Thus, we have analysed the roles among the main leukocytes (neutrophils and macrophages), present in the inflammatory phase, and the inflammatory cytokines: interleukin 6 (IL-6) and interleukin 10 (IL-10). The model solution reproduced the dynamics of the neutrophils and macrophages during inflammatory phase, however there was a lack between numeric and biological results suggesting the necessity to improve the model. One possible strategy to enhance this model is to consider the interaction between the pro-inflammatory cytokine and macrophages in the mathematical model.

  25. Marvin Fritz (OTHE)

    Technical University of Munich
    "On the modelling and analysis of tumor growth with phase-field equations of Cahn-Hilliard type"
    In this talk, we present a system of partial differential equations modelling the growth of tumor cells. We consider the effect of phase separation into proliferative, hypoxic and necrotic phases. Further, we model the invasion due to ECM degradation and the influence of chemotaxis and haptotaxis. We establish the existence of weak solutions in appropriate function spaces via the Faedo-Galerkin method and illustrate the effects of the model on the tumor growth through numerical simulations with finite element approximations.

  26. Milad Ghomlaghi (OTHE)

    Monash University
    "Akt regulates PIP3 production by PI3K to form a potent negative feedback loop"
    The phosphoinositide 3-kinase (PI3K)-Akt pathway is a central component of signalling networks and is dysregulated in numerous pathologies. As such, its activity is under the tight control of several feedback signals, which work to control signal flow and ensure signal fidelity. A rapid overshoot in the insulin-stimulated recruitment of Akt to the plasma membrane has previously been reported, which is indicative of negative feedback operating on acute timescales. Here, using computational modelling and cell biology we show that described mTORC1/S6K-dependent feedback mechanisms do not account for this behaviour. However, our system-based analysis suggests that another negative feedback must exist within the network to explain the overshoot in the recruitment of Akt to the plasma membrane. To identify this negative feedback, six different mathematical models are constructed that represent different possible negative feedback scenarios. Interrogating these models based on their quality of fitness to the experimental data allows us to reject unlikely candidate feedback mechanisms and guide experiment towards the most likely feedback model. Integrating model simulation and biological validation using live cell imaging and biochemical assays methods, we demonstrate existence of a negative feedback from Akt to PIP3, which limits plasma membrane associated PI3K and phosphatidylinositol (3,4,5)-trisphosphate (PIP3) synthesis. This feedback is both rapid and powerful - suppression of the feedback using Akt inhibitors increased PIP3 abundance by ~5-fold within 10 min of insulin stimulation. This had profound effects on the localisation of PIP3-binding proteins such as PDK1 and GAB2, as well as the activation of MAPK signalling. As a feature of multiple cell types and growth factors, this novel Akt-dependent feedback loop plays a vital role in regulating PIP3 abundance and thus has important implications for therapies targeting Akt.

  27. Mubasher Rashid Rather (OTHE)

    Central University of Rajasthan
    "Organization of biogeochemical nitrogen pathways with switch-like adjustment in fluctuating soil redox conditions"
    Nitrogen is cycled throughout ecosystems by a suite of biogeochemical processes. The high complexity of the nitrogen cycle resides in an intricate interplay between reversible biochemical pathways alternatively and specifically activated in response to diverse environmental cues. Despite aggressive research, how the fundamental nitrogen biochemical processes are assembled and maintained in fluctuating soil redox conditions remains elusive. Here, we address this question using a kinetic modelling approach coupled with dynamical systems theory and microbial genomics. We show that alternative biochemical pathways play a key role in keeping nitrogen conversion and conservation properties invariant in fluctuating environments. Our results indicate that the biochemical network holds inherent adaptive capacity to stabilize ammonium and nitrate availability, and that the bistability in the formation of ammonium is linked to the transient upregulation of the amo-hao mediated nitrification pathway. The bistability is maintained by a pair of complementary subsystems acting as either source or sink type systems in response to soil redox fluctuations. It is further shown how elevated anthropogenic pressure has the potential to break down the stability of the system, altering substantially ammonium and nitrate availability in the soil, with dramatic effects on biodiversity.

  28. Patrick Gelbach (OTHE)

    University of Southern California Dept of Biomedical Engineering
    "Metabolomics and Mechanistic Kinetic Modeling Reveal Mechanisms Driving Intracellular Metabolism and Insulin Secretion in Pancreatic Beta Cells"
    Pancreatic beta cells maintain blood glucose levels within a healthy range by producing insulin. Insulin production is heavily dependent on the intracellular metabolic reactions carried out by the cell, and diseases, such as Type 2 Diabetes, occur when that metabolism functions poorly. In order to better treat diabetes, we must understand glucose-stimulated insulin secretion in beta cells. Systems-focused computational modeling of metabolic processes can provide quantitative insight into the mechanisms driving insulin production in varied extracellular conditions, informing future research into novel treatments for diabetes. We developed a kinetic, ordinary differential equation model of PBC intracellular metabolism. The model includes glycolysis, glutaminolysis, the TCA cycle, the pentose phosphate pathway, and the aldose-reductase pathway. We linked metabolism to insulin production using partial least-squares regression. We performed a global sensitivity analysis to determine the kinetic parameters that significantly influence predicted metabolite levels. We trained the model by fitting its reaction velocities (Vmax parameters) to mass spectrometry-based metabolomics measurements of metabolite levels following 5- and 30-minute stimulation of the INS-1E cell line with varied concentrations of glucose. We applied the kinetic model to simulate clinically-relevant metabolic perturbations. The sensitivity analyses identified influential metabolic reactions. At both time points, Vmax values for the glucose transporter (GLUT2) reaction and the Glucokinase reaction were impactful on predicted metabolite levels. The results make sense, given both the primary role of beta cells (to import glucose into the cell to produce insulin) and published results showing GK to be a key regulator in overall PBC activity. At only the 30-minute time point, the Lactate Dehydrogenase, Monocarboxylate Cotransporter (MCT), and Aldose Reductase reaction velocities were found to be significantly impactful. The results suggest possible mechanisms by which extended treatment with glucose causes cells to adjust their metabolism to avoid glucotoxicity. At the 5-minute time point, the Triose-phosphate isomerase reaction velocity was influential, which may support the experimentally-seen rapid equilibration of glyceraldehyde-3-phosphate and dihydroxyacetone phosphate. Using the fitted model, we simulated 95% knockdown and upregulation of the GLUT2, MCT, pyruvate-hydrogen shuttle, and glucose-6-phosphate dehydrogenase reactions, to understand the effect of adjusted glucose import, lactate excretion, TCA Cycle flux, and PPP flux, respectively. Though the network as a whole is robust to many changes, the model predicts that controlling flux into the TCA Cycle had substantial effects at the 5-minute time point, suggesting that targeting TCA Cycle reactions may improve insulin production.

  29. Paul A Roberts (OTHE)

    University of Sussex
    "Using mathematics to investigate the mechanisms behind vision loss"
    (poster is withdrawn -- please see the NEUR Thursday 9:30am session for Paul Roberts' talk.)

  30. Priyom Adhyapok (OTHE)

    Indiana University
    "Modeling liver injury progression and repair"
    Drug induced liver injury (DILI) can result in a build-up of oxidative stress in hepatocytes,causing them to become stressed and die. Experiments using APAP as a model of DILI show an initial pattern of centrilobular damage which gets amplified by stressed cells communicating through gap junctions and the activation of the immune system in response to this injury. While hepatocyte proliferation takes place to combat liver mass loss, higher doses could still be lethal to the tissue. This talk will try to address the question of what tips the balance, that the same set of cell behaviors that are needed for tissue survival can also lead to widespread tissue death in some other situations? To answer this, I will discuss results of a computational model based on the competing biological processes of hepatocyte proliferation, necrosis and injury propagation. This model sheds light on the evolution of tissue damage or recovery and predicts the potential for divergent fates given different rates of the parameters related to these three processes.

  31. Rahma Jerbi (OTHE)

    "modélisation mathématique"
    Je suis Rahma JERBI, doctorante en mathématiquées appliquées à l'université de Sfax (Tunisie) pour la préparation du doctorat sous le thème ' Etudes théoriques et numériques de quelques problèmes inverses', ma thèse deonc est basée sur la modélisation mathématique de quelques problèmes inverses. Pour céla je suis intéressée par votre conférence pour améliorer mes compétences en mathématiques appliquées.

  32. Rahul Bishnoi (OTHE)

    IIT Guwahati
    "Eliminating the Need for User-Definition of Gap Penalties using Linear Programming"
    In the widely renowned `Needleman-Wunsch’ algorithm for sequence alignment, an affine gap penalty system is used for penalizing the algorithm for the usage of gaps. There are two types of gaps used: opening gap penalty and extending gap penalty, where the former refers to the introduction of a gap and the latter to the extension of pre-existing gaps. The choice of determining the values of these gap penalties has to be predefined by the user. As a result, there may arise an inefficient sequence alignment solely due to the poor choice of opening and extending gap penalties. In this poster, we aim to address this concern by constructing a mathematical black-box model which is governed by a set of linear equations in the form of a modified scoring system for judging the quality of the alignment. We use three dynamic programming states (Identity, Similarity, and DP score) to determine the optimal alignment. While iterating through various values of the scoring parameters, the highest cumulative score determines an optimal alignment and the respective gap penalty. For distantly-related and average-related sequences, our algorithm shows a significant increase in terms of identity and similarity for all the cases as compared to EMBOSS-Needle. We eliminate the need for the user to choose the gap penalties based on mere intuition and judgement and provide an automated method of choosing the optimal values specifically for each set of sequences.

  33. Reginaldo José da Silva (OTHE)

    Federal University of Alfenas, Alfenas, Brazil
    "Acoustic measures for voices in the classification of Parkinson's Disease"
    The ART Family Networks are neural networks based on the Adaptive Resonance Theory that has as a characteristic the resonance between the input data and the cluster center as the data was classified. In particular, self-expanding ART Neural Networks characterized by the ability, even with little data, to perform the data classification and to expand according to the inclusion of the data. This work uses the Fuzzy ART Self-expanding neural network for the diagnosis of Parkinson's Disease. For this, it uses the Parkinson Speech Dataset with Multiple Types of Sound Recordings database, available in the UCI Machine Learning Repository repository. This database is composed of data from tested individuals, based on the characteristics extracted from 26 different voice samples per individual. The problem is to classify patients as healthy or with Parkinson's disease. The correlation coefficient used to select which voice resources were most relevant. After implementation using the resources that showed the strongest positive correlation, an accuracy of 98.56% obtained with a Mattews Correlation Coefficient of 0.9716 using the 10-fold cross-validation method.

  34. Renee Dale (OTHE)

    "Using a trait-based dynamic mathematical framework to investigate the relationship between phenotypic dynamical parameters and the plant genome"
    Phenotypic data is used to measure a variety of traits that can be traced to genes. In this study I use a mathematical approach to generate new traits that describe dynamic processes. By abstracting the process of the above ground Setaria plant tissue growth, a top-down trait based dynamical model was constructed. This model describes events that occur within the growth and development of above-ground tissue. The mathematical framework considers above-ground plant tissue as either ‘resource generating’ or ‘non-resource generating’/’structural’. Care was taken in the mathematical representation to model the underlying growth and developmental processes, as well as the actual measurables described in the data. The data consist of biomass and height estimates by the image processing software PlantCV, as well as total water usage over 250 Setaria lines across 1100 plants in drought and well-watered conditions. To estimate the parameters of the dynamic processes, the model was fitted to the data. The model parameters were constrained when possible based on plant physiological understanding of Setaria growth and development as well as plant biomass measurements. The heritability of the parameters were calculated for both wet and dry conditions. We found that heritability of these parameters differs between wet and dry conditions, as well as certain processes that describe events critical to the dynamics of Setaria growth as described by the model. The parameters in our model are describing growth and developmental decisions of the plant. This method provides a novel way to identify plant phenotypic trait for identifying new genes that control dynamic processes. This novel framework will be used in the future to understand if phenotypic variability may be emergent from the interaction between environmental space searching strategies, biomass allocation strategies, and genotype.

  35. Robyn Shuttleworth (OTHE)

    University of Saskatchewan
    "Application of a cell-dense triphasic model to cryoprotectant equilibration"
    The loading of cryoprotectants (CPAs) into tissue remains challenging due to the risk of both mechanical strain on the tissue and the risk of toxicity damage from the cryoprotectants flooding into the tissue. Many models have been developed to describe the loading of CPAs into either individual cells, or continuously into a thin slab of tissue, however there is little evidence of the two models being combined. To that end, we propose a model that builds upon the triphasic model for articular cartilage introduced in Abazari et. al. 2009, using a system of partial differential equations to describe the mass transport of each component, namely, water, CPA, salt, and the solid matrix. Within this system we incorporate the well-known two-parameter model (Kleinhans, 1998) to describe the cell membrane transport of both water and CPA within individual cells. Combining these two systems allows us to investigate the stress placed on the tissue by considering the interactions at both an extracellular and intracellular fluid level. In addition, this general model allows us to specify properties of a tissue, ranging from their structure and composition, i.e., their percentage of tissue solids and cells, to their hydraulic conductivity and CPA permeability rates. Using all of this information, and by defining a bath solution containing a CPA concentration, our model is able to predict the amount of stress that will be placed on the tissue during CPA loading. We will use our results to create optimised loading protocols to reduce the overall strain on the tissue during CPA loading.

  36. Shelby M Scott (OTHE)

    "COVID and Crime: Analysis of Crime Dynamics Amidst Social Distancing Protocols"
    In response to the spread of the global pandemic in early 2020, many cities implemented states of emergency and stay at home orders to reduce virus spread. Changes in social dynamics due to restrictions has had an impact on cities across the United States. One change of interest is how crime dynamics shifted in response to quarantine. In this paper, we compare the crimes that occurred before the implementation of stay at home orders and the two weeks after these orders were put in place across three cities. Using t-tests, we find that in Chicago, Illinois, Baltimore, Maryland, and Baton Rouge, Louisiana, total crimes showed significant declines in the two weeks following stay at home orders. Chicago showed the most stark differences between these two time periods, but in all three cities the crime types contributing to these declines were related to property crime rather than interpersonal interactions.

  37. Sungyoung Shin (OTHE)

    Monash University
    "A mathematical model of GβL (de)ubiquitination switch uncovers biphasic response within the PI3K/mTOR signalling network"
    The PI3K/AKT/mTOR signalling pathway is a critical pathway in mammalian cells that regulates a broad array of cellular processes, including proliferation, survival and metabolism. G-protein beta-subunit-like (GβL or mLST8) has been long known as one of the shared subunits of both the mTORC1 and mTORC2 complexes. Recently, it was reported that the dynamic (de)ubiquitination of GβL generates a molecular switch mechanism that governs the binding of GβL towards Raptor and Sin1, core subunits of mTORC1 and 2, respectively; thereby actively coordinating the formation and abundances of these complexes. This new switch mechanism adds an extra layer of complexity to an already complex signalling network featuring abundant interlinked feedback regulation. However, how the GβL-mediated switch interplays with other regulatory mechanisms to control the dynamics and steady state behaviors of the PI3K/AKT/mTOR network is poorly understood. Here we integrate computational network modelling and biological experiments in a systems-based framework to characterize the network-level properties of PI3K/mTOR signalling conferred by the GβL-regulated switch, and interrogate the impact on network behavior when this switch is disrupted. To this end, we construct a novel mechanistic mathematical model of the PI3K/mTOR network that explicitly considers the GβL switch. The model is quantitatively calibrated and kinetic parameters are estimated using time-course data obtained from Mouse Embryonic Fibroblasts (MEF) cells. In contrast to previous studies indicating GβL is required for mTORC1 formation but not activity, our integrative in-silico/experimental analyses demonstrate that GβL is essential for formation as well as activation of both mTOR complexes. Importantly, mode simulations predict a previously unknown biphasic dependence of mTORC1 activity on Sin1, an integral component of mTORC2, revealing an intriguing non-linear functional linkage between the complexes. An increase of Sin1 from a low level initially promotes mTORC1 activity (first phase), but further increase of mSIN1 beyond a critical threshold (second phase) instead downregulates mTORC1 activity. We subsequently validate this prediction experimentally in MEF cells. In summary, this study presents a novel mathematical model of the PI3K/mTOR pathway that enables quantitative analysis of the role of GβL in regulating network behaviours. Modelling and experimental validation confirms a biphasic dependency between mTORC1 and Sin1, which may help explain context-specific biological observations in cells with low and high levels of Sin1.

  38. Vojtech Kumpost (OTHE)

    Karlsruhe Institute of Technology
    "Elucidating Mechanisms Underlying Experimentally Induced Changes in the Zebrafish Circadian Clock"
    Zebrafish embryonic cell lines are a unique experimental model of the circadian regulation in vertebrates. They can be directly paced by external light stimuli and the oscillatory dynamics of gene expression can be monitored by luciferase reporter assays. These assays are designed to convert transcriptional activation to bioluminescence with an excellent time resolution and obvious changes in the measured waveforms can be observed as a result of light stimuli, gene knockouts or drug treatment. Here, we aim to utilize mathematical modeling to clarify how those changes in the recorded data correspond to the specific cellular mechanisms that generate them. We start with a simplified ODE Kim-Forger model proposed for the mammalian clock. This model consists of three state variables connected in a single negative feedback loop, which represents the core mechanism of the circadian clock mechanism. The model is further adjusted to correspond to the specifics of the zebrafish circadian regulatory system. Next, we validate the proposed model on a previously published data set with a variety of light-pacing regimes. In our further work, we will use stochastic modeling to account for molecular noise on the single-cell level that affects the observed dynamics of the recorded cell population. Finally, we plan to quantify the effect of a variety of drug treatments by fitting the model parameters to the drug-treated cell cultures and exploring the parameter space that can explain such variations. Our work represents a novel approach to studying bioluminescence recordings of zebrafish embryonic cell lines and aims to better quantify the observed differences due to drug treatments.

Hosted by eSMB2020 Follow
Virtual conference of the Society for Mathematical Biology, 2020.