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Computational modeling of neuronal morphology is a powerful tool for understanding developmental processes and structure-function relationships. We present a multifaceted approach based on stochastic sampling of morphological measures from digital reconstructions of real cells. We examined how dendritic elongation, branching, and taper are controlled by three morphometric determinants: Branch Order, Radius, and Path Distance from the soma. Virtual dendrites were simulated starting from 3,715 neuronal trees reconstructed in 16 different laboratories, including morphological classes as diverse as spinal motoneurons and dentate granule cells. Several emergent morphometrics were used to compare real and virtual trees. Relating model parameters to Branch Order best constrained the number of terminations for most morphological classes, except pyramidal cell apical trees, which were better described by a dependence on Path Distance. In contrast, bifurcation asymmetry was best constrained by Radius for apical, but Path Distance for basal trees. All determinants showed similar performance in capturing total surface area, while surface area asymmetry was best determined by Path Distance. Grouping by other characteristics, such as size, asymmetry, arborizations, or animal species, showed smaller differences than observed between apical and basal, pointing to the biological importance of this separation. Hybrid models using combinations of the determinants confirmed these trends and allowed a detailed characterization of morphological relations. The differential findings between morphological groups suggest different underlying developmental mechanisms. By comparing the effects of several morphometric determinants on the simulation of different neuronal classes, this approach sheds light on possible growth mechanism variations responsible for the observed neuronal diversity.
Statistical mechanics and molecular dynamics simulations have been carried out to study the distribution and dynamics of internal water molecules in bovine heart cytochrome c oxidase (CcO). CcO is found to be capable of holding plenty of water, which in subunit I alone amounts to about 165 molecules. The dynamic characterization of these water molecules is carried out. The nascent water molecules produced in the redox reaction at the heme a(3)-CuB binuclear site form an intriguing chain structure. The chain begins at the position of Glu242 at the end of the D channel, and has a fork structure, one branch of which leads to the binuclear center, and the other to the propionate d of heme a(3). The branch that leads to the binuclear center has dynamic access both to the site where the formation of water occurs, and to delta-nitrogen of His291. From the binuclear center, the chain continues to run into the K channel. The stability of this hydrogen bond network is examined dynamically. The catalytic site is located at the hydrophobic region, and the nascent water molecules are produced at the top of the energy hill. The energy gradient is utilized as the mechanism of water removal from the protein. The water exit channels are explored using high-temperature dynamics simulations. Two putative channels for water exit from the catalytic site have been identified. One is leading directly toward Mg(2+) site. However, this channel is only open when His291 is dissociated from CuB. If His291 is bound to CuB, the only channel for water exit is the one that originates at E242 and leads toward the middle of the membrane. This is the same channel that is presumably used for oxygen supply.
We present a series of simulation studies that explore the relative performance of several phylogenetic network approaches (statistical parsimony, split decomposition, union of maximum parsimony trees, neighbor-net, simulated history recombination upper bound, median-joining, reduced median joining and minimum spanning network) compared to standard tree approaches, (neighbor-joining and maximum parsimony) in the presence and absence of recombination.
Blockchain has been applied to quality control in manufacturing, but the problems of false defect detections and lack of data transparency remain. This paper proposes a framework, Blockchain Quality Controller (BCQC), to overcome these limitations while fortifying data security. BCQC utilizes blockchain and Internet-of-Things to form a peer-to-peer supervision network. This paper also proposes a consensus algorithm, Quality Defect Tolerance (QDT), to adopt blockchain for during-production quality control. Simulation results show that BCQC enhances data security and improves defect detections. Although the time taken for the quality control process increases with the number of nodes in blockchain, the application of QDT allows multiple inspections on a workpiece to be consolidated at a faster pace, effectively speeding up the entire quality control process. The BCQC and QDT can improve the quality of parts produced for mass personalization manufacturing.
Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (Aβ) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against Aβ and then demonstrate the effects of this intervention on soluble Aβ, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble Aβ, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against Aβ. Since, soluble Aβ, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against Aβ may not be effective unless it is performed very early in the disease process or combined with other therapies.
The structure of very thin polymer films formed by strongly adsorbed macromolecules was studied by computer simulation. A coarse-grained model of strictly two-dimensional polymer systems was built, and its properties determined by an efficient Monte Carlo simulation algorithm. Properties of the model system were determined by means of Monte Carlo simulations with a sampling algorithm that combines Verdier-Stockmayer, pivot and reputation moves. The effects of temperature, chain length and polymer concentration on the macromolecular structure were investigated. It was shown that at low temperatures, the chain size increases with the concentration, that is, inversely with high temperatures. This behavior should be explained by the influence of inter-chain interactions.
We present a computer simulation and associated experimental validation of assembly of glial-like support cells into the interweaving hexagonal lattice that spans the Drosophila pupal eye. This process of cell movements organizes the ommatidial array into a functional pattern. Unlike earlier simulations that focused on the arrangements of cells within individual ommatidia, here we examine the local movements that lead to large-scale organization of the emerging eye field. Simulations based on our experimental observations of cell adhesion, cell death, and cell movement successfully patterned a tracing of an emerging wild-type pupal eye. Surprisingly, altering cell adhesion had only a mild effect on patterning, contradicting our previous hypothesis that the patterning was primarily the result of preferential adhesion between IRM-class surface proteins. Instead, our simulations highlighted the importance of programmed cell death (PCD) as well as a previously unappreciated variable: the expansion of cells' apical surface areas, which promoted rearrangement of neighboring cells. We tested this prediction experimentally by preventing expansion in the apical area of individual cells: patterning was disrupted in a manner predicted by our simulations. Our work demonstrates the value of combining computer simulation with in vivo experiments to uncover novel mechanisms that are perpetuated throughout the eye field. It also demonstrates the utility of the Glazier-Graner-Hogeweg model (GGH) for modeling the links between local cellular interactions and emergent properties of developing epithelia as well as predicting unanticipated results in vivo.
The year 2011 marked the half-centenary of the publication of what came to be known as the Anfinsen postulate, that the tertiary structure of a folded protein is prescribed fully by the sequence of its constituent amino acid residues. This postulate has become established as a credo, and, indeed, no contradictions seem to have been found to date. However, the experiments that led to this postulate were conducted on only a single protein, bovine ribonuclease A (RNAse). We conduct molecular dynamics (MD) simulations on this protein with the aim of mimicking this experiment as well as making the methodology available for use with basically any protein. There have been many attempts to model denaturation and refolding processes of globular proteins in silico using MD, but only a few examples where disulphide-bond containing proteins were studied. We took the view that if the reductive deactivation and oxidative reactivation processes of RNAse could be modelled in silico, this would provide valuable insights into the workings of the classical Anfinsen experiment.
Assembly of normally soluble proteins into ordered aggregates, known as amyloid fibrils, is a cause or associated symptom of numerous human disorders, including Alzheimer's and the prion diseases. Here, we test the ability of discontinuous molecular dynamics (DMD) simulations based on PRIME20, a new intermediate-resolution protein force field, to predict which designed hexapeptide sequences will form fibrils, which will not, and how this depends on temperature and concentration. Simulations were performed on 48-peptide systems containing STVIIE, STVIFE, STVIVE, STAIIE, STVIAE, STVIGE, and STVIEE starting from random-coil configurations. By the end of the simulations, STVIIE and STVIFE (which form fibrils in vitro) form fibrils over a range of temperatures, STVIEE (which does not form fibrils in vitro) does not form fibrils, and STVIVE, STAIIE, STVIAE, and STVIGE (which do not form fibrils in vitro) form fibrils at lower temperatures but stop forming fibrils at higher temperatures. At the highest temperatures simulated, the results on the fibrillization propensity of the seven short de novo designed peptides all agree with the experiments of López de la Paz and Serrano. Our results suggest that the fibrillization temperature (temperature above which fibrils cease to form) is a measure of fibril stability and that by rank ordering the fibrillization temperatures of various sequences, PRIME20/DMD simulations could be used to ascertain their relative fibrillization propensities. A phase diagram showing regions in the temperature-concentration plane where fibrils are formed in our simulations is presented.
Research on the medical applications of artificial intelligence has increased the knowledge of logical and methodological principles of clinical reasoning. Thus, computer-based diagnostic systems are developed on the basis of progress in this field, because thorough knowledge is necessary to obtain efficient simulation. This work was aimed at analyzing the structure of medical and radiological reasoning and at discussing the modalities to simulate it with computer-based diagnostic systems. The diagnostic process includes two steps: data collection and data interpretation; radiological reasoning involves the following 5 steps: procedural, executive, observative, interpretative and communicative. Each of them needs a different approach to simulation, considering, in its development, the different characteristics of each kind of reasoning. The expert system shells on the market are necessary tools to develop expert systems, but they cannot cover the whole of processes taking place during radiological work. Therefore, a particular, radiology-aimed shell should be developed to help the radiologist.
AAA ATPases form a functionally diverse superfamily of proteins. Most members form homo-hexameric ring complexes, are catalytically active only in the fully assembled state, and show co-operativity among the six subunits. The mutual dependence among the subunits is clearly evidenced by the fact that incorporation of mutated, inactive subunits can decrease the activity of the remaining wild type subunits. For the first time, we develop here models to describe this form of allostery, evaluate them in a simulation study, and test them on experimental data. We show that it is important to consider the assembly reactions in the kinetic model, and to define a formal inhibition scheme. We simulate three inhibition scenarios explicitly, and demonstrate that they result in differing outcomes. Finally, we deduce fitting formulas, and test them on real and simulated data. A non-competitive inhibition formula fitted experimental and simulated data best. To our knowledge, our study is the first one that derives and tests formal allosteric schemes to explain the inhibitory effects of mutant subunits on oligomeric enzymes.
Diabetes is a major public health problem and prediabetes (intermediate hyperglycaemia) is associated with a high risk of developing diabetes. With evidence supporting the use of preventive interventions for prediabetes populations and the discovery of novel biomarkers stratifying the risk of progression, there is a need to evaluate their cost-effectiveness across jurisdictions. In diabetes and prediabetes, it is relevant to inform cost-effectiveness analysis using decision models due to their ability to forecast long-term health outcomes and costs beyond the time frame of clinical trials. To support good implementation and reimbursement decisions of interventions in these populations, models should be clinically credible, based on best available evidence, reproducible and validated against clinical data. Our aim is to identify recent studies on computer simulation models and model-based economic evaluations of populations of individuals with prediabetes, qualify them and discuss the knowledge gaps, challenges and opportunities that need to be addressed for future evaluations.
In the past decade, factors such as population growth, increased environmental incidents, and substance abuse have caused patient-overcrowding in emergency departments (EDs). Our main objective was to assess the effects of a discharge lounge on decreasing the patient waiting time and ED overcrowding by computer simulation.
The ionized states of molecular analytes located on solid surfaces require profound investigation and better understanding for applications in the basic sciences in general, and in the design of nanobiosensors, in particular. Such ionized states are induced by the interactions of molecules between them in the analyzed substance and with the target surface. Here, computer simulations using COMSOL Multiphysics software show the effect of surface charge density and distribution on the output generation in a dynamic PIN diode with gate control. This device, having built-in potential barriers, has a unique internal integration of output signal generation. The identified interactions showed the possibility of a new design for implementing a nanobiosensor based on a dynamic PIN diode in a mode with surface charge control.
In this study we present a kinematic approach for modeling needle insertion into soft tissues. The kinematic approach allows the presentation of the problem as Dirichlet-type (i.e. driven by enforced motion of boundaries) and therefore weakly sensitive to unknown properties of the tissues and needle-tissue interaction. The parameters used in the kinematic approach are straightforward to determine from images. Our method uses Meshless Total Lagrangian Explicit Dynamics (MTLED) method to compute soft tissue deformations. The proposed scheme was validated against experiments of needle insertion into silicone gel samples. We also present a simulation of needle insertion into the brain demonstrating the method's insensitivity to assumed mechanical properties of tissue.
Angiogenesis plays an essential role in various normal physiological processes, such as embryogenesis, tissue repair, and skin regeneration. Visfatin is a 52 kDa adipokine secreted by various tissues including adipocytes. It stimulates the expression of vascular endothelial growth factor (VEGF) and promotes angiogenesis. However, there are several issues in developing full-length visfatin as a therapeutic drug due to its high molecular weight. Therefore, the purpose of this study was to develop peptides, based on the active site of visfatin, with similar or superior angiogenic activity using computer simulation techniques.Initially, the active site domain (residues 181∼390) of visfatin was first truncated into small peptides using the overlapping technique. Subsequently, the 114 truncated small peptides were then subjected to molecular docking analysis using two docking programs (HADDOCK and GalaxyPepDock) to generate small peptides with the highest affinity for visfatin. Furthermore, molecular dynamics simulations (MD) were conducted to investigate the stability of the protein-ligand complexes by computing root mean square deviation (RSMD) and root mean square fluctuation(RMSF) plots for the visfatin-peptide complexes. Finally, peptides with the highest affinity were examined for angiogenic activities, such as cell migration, invasion, and tubule formation in human umbilical vein endothelial cells (HUVECs). Through the docking analysis of the 114 truncated peptides, we screened nine peptides with a high affinity for visfatin. Of these, we discovered two peptides (peptide-1: LEYKLHDFGY and peptide-2: EYKLHDFGYRGV) with the highest affinity for visfatin. In an in vitrostudy, these two peptides showed superior angiogenic activity compared to visfatin itself and stimulated mRNA expressions of visfatin and VEGF-A. These results show that the peptides generated by the protein-peptide docking simulation have a more efficient angiogenic activity than the original visfatin.
Computer simulation of the human brain at an individual neuron resolution is an ultimate goal of computational neuroscience. The Japanese flagship supercomputer, K, provides unprecedented computational capability toward this goal. The cerebellum contains 80% of the neurons in the whole brain. Therefore, computer simulation of the human-scale cerebellum will be a challenge for modern supercomputers. In this study, we built a human-scale spiking network model of the cerebellum, composed of 68 billion spiking neurons, on the K computer. As a benchmark, we performed a computer simulation of a cerebellum-dependent eye movement task known as the optokinetic response. We succeeded in reproducing plausible neuronal activity patterns that are observed experimentally in animals. The model was built on dedicated neural network simulation software called MONET (Millefeuille-like Organization NEural neTwork), which calculates layered sheet types of neural networks with parallelization by tile partitioning. To examine the scalability of the MONET simulator, we repeatedly performed simulations while changing the number of compute nodes from 1,024 to 82,944 and measured the computational time. We observed a good weak-scaling property for our cerebellar network model. Using all 82,944 nodes, we succeeded in simulating a human-scale cerebellum for the first time, although the simulation was 578 times slower than the wall clock time. These results suggest that the K computer is already capable of creating a simulation of a human-scale cerebellar model with the aid of the MONET simulator.
To prevent further lung damage in patients with acute respiratory distress syndrome (ARDS), it is important to avoid overdistension and cyclic opening and closing of atelectatic alveoli. Previous studies have demonstrated protective effects of using low tidal volume (VT), moderate positive end-expiratory pressure and low airway pressure. Aspiration of dead space (ASPIDS) allows a reduction in VT by eliminating dead space in the tracheal tube and tubing. We hypothesized that, by applying goal-orientated ventilation based on iterative computer simulation, VT can be reduced at high respiratory rate and much further reduced during ASPIDS without compromising gas exchange or causing high airway pressure.
When subjected to stress, terminally differentiated neurons are susceptible to reactivate the cell cycle and become hyperploid. This process is well documented in Alzheimer's disease (AD), where it may participate in the etiology of the disease. However, despite its potential importance, the effects of neuronal hyperploidy (NH) on brain function and its relationship with AD remains obscure. An important step forward in our understanding of the pathological effect of NH has been the development of transgenic mice with neuronal expression of oncogenes as model systems of AD. The analysis of these mice has demonstrated that forced cell cycle reentry in neurons results in most hallmarks of AD, including neurofibrillary tangles, Aβ peptide deposits, gliosis, cognitive loss, and neuronal death. Nevertheless, in contrast to the pathological situation, where a relatively small proportion of neurons become hyperploid, neuronal cell cycle reentry in these mice is generalized. We have recently developed an in vitro system in which cell cycle is induced in a reduced proportion of differentiated neurons, mimicking the in vivo situation. This manipulation reveals that NH correlates with synaptic dysfunction and morphological changes in the affected neurons, and that membrane depolarization facilitates the survival of hyperploid neurons. This suggests that the integration of synaptically silent, hyperploid neurons in electrically active neural networks allows their survival while perturbing the normal functioning of the network itself, a hypothesis that we have tested in silico. In this perspective, we will discuss on these aspects trying to convince the reader that NH represents a relevant process in AD.
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