Stability robust analysis of calcium in neuron

For real-time systems, most of the analysis involves efficient or exact schedulability checking. While this is important, analysis is often based on the assumption that the task parameters such as execution requirements and inter-arrival times between jobs are known exactly. In most cases, however, only a worst-case estimate of these quantities is available at the time of analysis. It is therefore imperative that schedulability analysis hold for better parameter values (Sustainable Analysis). On the other hand, if the task or system parameters turn out to be worse off, then the analysis should tolerate some deterioration (Robust Analysis). Robust analysis is especially important, because the implication of task schedulability is often weakened in the presence of optimizations that are performed on its code, or dynamic system parameters.In this work, we define and address sustainability and robustness questions for analysis of embedded real-time software that is modeled by conditional real-time tasks. Specifically, we show that, while the analysis is sustainable for changes in the task such as lower job execution times and increased relative deadlines, it is not the case for code changes such as job splitting and reordering. We discuss the impact of these results in the context of common compiler optimizations, and then develop robust schedulability techniques for operations where the original analysis is not sustainable.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Calcium homeostasis and low-frequency magnetic and electric field exposure: A systematic review and meta-analysis of in vitro studies

For real-time systems, most of the analysis involves efficient or exact schedulability checking. While this is important, analysis is often based on the assumption that the task parameters such as execution requirements and inter-arrival times between jobs are known exactly. In most cases, however, only a worst-case estimate of these quantities is available at the time of analysis. It is therefore imperative that schedulability analysis hold for better parameter values ( Sustainable Analysis ). On the other hand, if the task or system parameters turn out to be worse off, then the analysis should tolerate some deterioration ( Robust Analysis ). Robust analysis is especially important, because the implication of task schedulability is often weakened in the presence of optimizations that are performed on its code, or dynamic system parameters. In this work, we define and address sustainability and robustness questions for analysis of embedded real-time software that is modeled by conditional real-time tasks. Specifically, we show that, while the analysis is sustainable for changes in the task such as lower job execution times and increased relative deadlines, it is not the case for code changes such as job splitting and reordering. We discuss the impact of these results in the context of common compiler optimizations, and then develop robust schedulability techniques for operations where the original analysis is not sustainable.

Huntington's disease is a hereditary neurodegenerative disease that primarily affects striatal neurons. Recent studies demonstrated abnormalities in calcium regulation in striatal neurons in Huntington's disease, which leads to elimination of synaptic connections between cortical and striatal neurons. In the present study, we focused on the neuroprotective properties of σ1-receptor, because one of its main functions is associated with modulation of calcium homeostasis in cells. The application of selective σ1-receptor agonists to the corticostriatal cell culture restores synaptic connections between the cortical and striatal neurons. Based on the obtained data, we assume that σ1-receptor is a promising target for the development of drugs for the therapy of Huntington's disease.

Chronic neuroinflammation is a pivotal pathogenesis in neurodegenerative diseases (NDDs). Transient receptor potential canonical protein 6 (TRPC6) has an essential role in the maintenance of calcium homeostasis in cells. Our previous study indicated that TRPC6 signaling is involved in Aβ deposition and NLRP1 inflammasome activation in type 2 diabetes mellitus-associated cognitive dysfunction. However, whether TRPC6 signaling contributes to chronic lipopolysaccharide (LPS)-induced neuroinflammatory injury and the mechanism remain unclear. In this study, male mice received intraperitoneal injections of LPS (200µg/kg) for 21 days to induce a chronic neuroinflammation model. The open field test, hole-board test, and Morris water maze were conducted to evaluate cognitive function. The H&E and Nissl staining was employed to examine neuronal injury. The immunofluorescence, western blotting, or q-PCR were used to analyze TRPC6, AIM2 inflammasome expression, and Nrf2 activation. The fluorescent probes and calcium imaging were performed to assess ROS accumulation and calcium dysregulation in LPS-induced HT22 neuron cells. Chronic LPS exposure induced behavioral deficits in locomotion, exploratory behavior, and learning and memory, and neuronal damages with less expressions of PSD95 and Synaptophysin in mice. Mechanistically, LPS exposure significantly increased ROS production, TRPC6 expression and calcium overload, and induced AIM2 inflammasome activation in vivo or in vitro. While Trpc6 knockout could significantly improve LPS-induced cognitive dysfunction and neuronal injuries, inhibit TRPC6-mediated calcium overload, and downregulate the expressions of AIM2, caspase-1, IL-1β, IL-6, caspase-3 and Bax in vivo or in vitro. Additionally, Rg1 treatment significantly inhibited calcium overload and AIM2 inflammasome activation in LPS-induced HT22 cells. More importantly, Rg1 significantly activated Nrf2 signaling and reduced ROS production in LPS-induced mice or HT22 cells. Trpc6 knockout can improve chronic LPS-induced neuroinflammation and injury by inhibiting TRPC6-AIM2 inflammasomes. While Rg1 treatment can alleviate LPS-induced neuroinflammation and injury not only by inhibiting TRPC6-AIM2 inflammasomes activation but also activating Nrf2 signaling.

Taurine is a kind of endogenous free amino acid in tissue and a potent antioxidant agent its function may in part be to adjust calcium homeostasis in cells, anti-oxidative stress, anti-inflammatory and cell protector but little is known about the expression or the role of Taurine in the central nervous system. Stroke is the major cause of death and disability worldwide. Here, we investigated the role of Taurine in ischemic stroke as a potential neuroprotective using rat model of transient cerebral ischemia. Transient cerebral ischemia was induced by MCAO were performed on male Sprague-Dawley rats. TTC staining used to measurement of infarct volume in the brain and ELISA kits to assay cytokines. Our data suggested that Taurine reduced cerebral infarct size, decreased pro-inflammatory cytokines expression and produced lower level of ICAM-1. These results suggest that Taurine can be exerting significantly protective effect against brain ischemic injury through inhibiting pro-inflammatory cytokines and ICAM-1.

Robustness is one of the principles of design inherent to biological systems. Cellular robustness can be measured as limits of intracellular parameters such as gene expression levels. We have recently developed an experimental approach coined as genetic Tug-Of-War (gTOW), which we used to perform robustness analysis in yeast. Using gTOW, we were able to measure the upper limit of expression of gene targets. In this review, we first elaborate on how the gTOW method compares to current mathematical simulation models prevalently used in the determination of robustness. We then explain the experimental principles underlying gTOW and its associated tools, and we provide concrete examples of robustness analysis using gTOW, i.e. cell cycle and HOG pathway gene expression analysis. Finally, we list a series of Q&As related to the experimental utilization of gTOW and we describe the potential impact of gTOW and its relevance to the understanding of biological systems.

Hypertension Secondary to Early-Stage Kidney Disease: The Pathogenetic Role of Altered Cytosolic Calcium (Ca2+) Homeostasis of Vascular Smooth Muscle Cells

The direct adaptive regulation of affine in the control nonlinear square (system states equals to control inputs) dynamical systems with modeling error effects, is considered in this paper. The method is based on a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Dynamical Systems (FDS) operating in conjunction with High Order Neural Network Functions, which in the sequel approximate the fuzzy rules. This way the unknown plant is modeled by a fuzzy-recurrent high order neural network (F-RHONN), which is of known structure considering the neglected nonlinearities. The development is combined with a sensitivity analysis of the closed loop in the presence of modeling imperfections and provides a comprehensive and rigorous analysis of the stability properties of the closed loop system. The existence and boundness of the control signal is always assured by introducing a novel method of parameter hopping and incorporating it in weight updating law. Simulations illustrate the potency of the method and its applicability is tested on well known benchmarks where it is shown that our approach is superior to the case of simple RHONN's.

Aim:Anticancer drugs (chemotherapeutics) used in cancer treatment (chemotherapy) lead to drug resistance. This study was conducted to investigate the possible effect of iron on calcium homeostasis in epithelial ovarian cancer cells (MDAH-2774) and cisplatin-resistant cells of the same cell line (MDAH-2774/DDP).Methods:To develop MDAH-2774/DDP cells, MDAH-2774 (MDAH) cells were treated with cisplatin in dose increases of 5 μM between 0 μM and 70 μM. The effect of iron on the viability of MDAH and MDAH/DDP cells was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide test at the end of 24 h incubation.Results:At increasing iron concentrations in MDAH and MDAH/DDP cells, the mRNA gene of fifteen genes [inositol 1,4,5-triphosphate receptor (IP3R)1/2/3, ryanodine receptor (RYR)1/2, sarco/endoplasmic reticulum Ca2+ ATPase (SERCA)1/2/3, Na+/Ca2+ exchange (NCX)1/2/3, and plasma membrane Ca2+ ATPase (PMCA)1/2/3/4] associated with Ca2+ differences in expression were determined by quantitative reverse transcription-polymerase chain reaction. Changes in IP3R2, RYR1, SERCA2, NCX3, PMCA1, and PMCA3 gene expressions were observed in iron treatment of MDAH/DDP cells, while changes were detected in iron treatment of MDAH cells in IP3R1/2/3, RYR1/2, SERCA1/2/3, NCX2/3, and PMCA1 expressions.Conclusions:This changes in the expression of calcium channels, pumps, and exchange proteins in the epithelial ovarian cancer cell line and in cisplatin-resistant epithelial ovarian cancer cells suggest that iron may have an important role in regulating calcium homeostasis. Due to differences in the expression of genes that play of an important role in the regulation of calcium homeostasis in the effect of iron, drug resistance can be prevented by introducing a new perspective on the use of inhibitors and activators of these genes and thus cytostatic treatment strategies.

Skeletal muscle myogenesis is a sophisticated process controlled by genetic and epigenetic regulators. In animals, one of the key enzymes for the DNA demethylation of 5-methylcytosine is TET2. Although TET2 is essential for muscle development, the mechanisms by which TET2 regulates myogenesis, particularly the implication for muscle stem cells, remains unclear. In the present study, we employed the TET2 knockout mouse model to investigate the function of TET2 in muscle development and regeneration. We observed that TET2 deficiency caused impaired muscle stem cell proliferation and differentiation, resulting in the reduction in both myofiber number and muscle tissue size. Specifically, TET2 maintains calcium homeostasis in muscle stem cells by controlling the DNA methylation levels of the calcium pathway genes. Forced expression of the sodium/calcium exchanger protein SLC8A3 could rescue the myogenic defects in TET2 knockout cells. Our data not only illustrated the vital function of TET2 during myogenesis but also identified novel targets that contribute to calcium homeostasis for enhancing muscle function.

Toluene Disrupts Outer Hair Cell Morphometry and Intracellular Calcium Homeostasis in Cochlear Cells of Guinea Pigs

Nature has repeatedly converged on the use of starting flows for mass, momentum, and energy transport. The vortex loops that form during flow initiation have been reproduced in the laboratory and have been shown to make a proportionally larger contribution to fluid transport than an equivalent steady jet. However, physical processes limit growth of the vortex loops, suggesting that these flows may be amenable to optimization. Although it has been speculated that optimal vortex formation might occur naturally in biological systems, previous efforts to quantify the biological mechanisms of vortex formation have been inconclusive. In addition, the unsteady fluid dynamical effects associated with starting flow vortex generators are poorly understood. This thesis describes a combination of new experimental techniques and in vivo animal measurements that determine the effects of fluid-structure interactions on vortex formation by starting flow propulsors. Results indicate that vortex formation across various biological systems is manipulated by these kinematics in order to maximize thrust and/or propulsive efficiency. An emphasis on observed vortex dynamics and transient boundary conditions facilitates quantitative comparisons across fluid transport schemes, irrespective of their individual biological functions and physical scales. The primary contributions of this thesis are the achievement of quantitative measures of unsteady vortex dynamics via fluid entrainment and added-mass effects, and the development of a robust framework to facilitate the discovery of general design principles for effective fluid transport in engineering technologies and biological therapies. The utility of this new research paradigm is demonstrated through a variety of examples.

Models and computational predictions are useful in identifying certain key parameters that play a central role in defining the overall behavior of the system, and thus lead to new and more informative experiments. In this thesis, in-silico models are developed over a range of individual biological scales (macroscopic, mesoscopic and microscopic) for a range of cellular phenomena (cellular interactions, migration and signalling pathways) in order to highlight the importance of combined in-vitro – in-silico investigations. It is widely accepted that Systems Biology aims to provide a simpler and more abstract framework to explain complex biological phenomena. However, integration of these models with experimental data is often underutilised. Incorporation of experimentally derived data sets into the mathematical framework of in-silico modelling results in reliable, well parameterised systems capable of replicating dynamical properties of the biological systems. Work in this thesis includes the development of a continuous macroscopic in-silico model to identify the key mechanisms of interaction between cells present within the gastric tumour microenvironment. This model of discovery is used in a predictive capacity to accept or reject hypotheses. Next, the construction of a discrete cell based model of fibroblast migration is used to determine the degree of bias fibroblast cells experience when migrating over different surface topologies. The key results from this model show that particular surface topographies can have an effect on migratory cell behaviour. Then, the parameterisation of a differential equation model is used to quantify the key mechanisms of Nrf2 regulation in the cytoplasm and nucleus. Validation with experimentally derived datasets results in the quantification of rate ratios important to the dynamics of this signalling pathway. Finally, a stochastic Petri-net model capable of simulating the dynamical behaviour of functional cross-talk between the Nrf2 and NF-κB pathways is developed. This approach allows for the evaluation of a wide array of network responses, without the need for computationally expensive parameterisation. Together, these models exhibit how integration of in-silico models with in-vitro datasets can be used to generate new knowledge, or testable hypotheses.