In-silico Experiments Research Articles

Stimulation of GMP formation in hGBP1 is mediated by W79 and its effect on the antiviral activity.

Interferon-inducible large GTPases are critical for innate immunity. The distinctive feature of a large GTPase, human guanylate binding protein-1 (hGBP1), is the sequential hydrolysis of GTP into GMP via GDP. Despite several structural and biochemical studies, the underlying mechanism of assembly-stimulated GMP formation by hGBP1 and its role in immunity are not fully clarified. Using a series of biochemical, biophysical, and insilico experiments, we studied four tryptophan residues, located near switch I-II (in and around the active site) to understand the conformational changes near these regions and also to investigate their effect on enhanced GMP formation. The W79A mutation showed significantly reduced GMP formation, whereas the W81A and W180A substitutions exhibited only a marginal defect. The W114A mutation showed a long-range effect of further enhanced GMP formation, which was mediated through W79. We also observed that after first phosphate cleavage, the W79-containing region undergoes a conformational change, which is essential for stimulated GMP formation. We suggest that this conformational change helps to reposition the active site for the next cleavage step, which occurs through a stable contact between the indole moiety of W79 and the main chain carbonyl of K76. We also showed that stimulated GMP formation is crucial for antiviral activity against hepatitis C. Thus, the present study not only provides new insight for the stimulation of GMP formation in hGBP1, but also highlights the importance of the enhanced second phosphate cleavage product in the antiviral activity.

Identification of zebrafish fumarate hydratase active site by molecular docking and simulation studies

Fumarate hydratase (FH), one of the members of TCA cycle, acts as a catalyte for the synthesis of malate from fumarate. FH has been proposed to play as a tumour suppressor leading to the pathogenicity of leiomyomas, renal cell carcinoma and paraganglioma. Mutations in the active site of FH lead to alteration in the protein structure. Similarly, binding of several chemical inhibitors to the active site also leads to the disruption of protein structural integrity thereby leading to protein dysfunction. Therefore, in order to address this mechanism leading to cancer, the binding efficiency of potential human FH inhibitor citrate to zebrafish fh has been extensively analysed in this study by molecular docking and simulation experiments followed by quantification of fumarate hydratase enzyme activity to validate and confirm the findings. Molecular docking revealed stronger interaction of zebrafish fh protein with inhibitor citrate when compared to natural substrate fumarate. Study on the dynamics of docked structures further confirmed that citrate was found to possess more binding affinity than fumarate. In vitro biochemical analysis also revealed concentration dependent potential inhibitory effect of citrate on zebrafish fh, thus confirming the findings of the in-silico experiments. Communicated by Ramaswamy H. Sarma

Abstract 3207: GenePattern Notebook: An integrative analytical environment for cancer research

Abstract As the availability of genetic and genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The electronic analysis notebook has recently emerged as an effective and versatile tool for this purpose, allowing scientists to combine the scientific exposition – text, images, and multimedia – with the actual code that runs the analysis, creating a single “research narrative” document. The Jupyter Notebook system has become the de facto standard notebook environment in data science and genomic analysis. However, the Jupyter environment requires familiarity with a programming language to run analyses, and even text must be formatted using a programming-style language. To extend notebook capabilities to the needs of researchers at all levels of programming expertise, we developed the GenePattern Notebook environment, which integrates Jupyter's capabilities with the hundreds of genomic tools available through the GenePattern platform. This tool allows scientists to develop, share, collaborate on, and publish their notebooks, requiring only a web browser. In this environment, investigators can design their in-silico experiments, perform and refine analyses, launch compute-intensive analyses on cloud-based and high-performance compute resources, and publish their results as electronic notebooks that other scientists can adopt to reproduce the original analyses and modify for their own work. GenePattern Notebook provides: (1) Access to a wide range of genomic analyses within a notebook. Hundreds of analyses are available, from machine learning techniques such as clustering, classification, and dimension reduction, to omic-specific methods for gene expression analysis, proteomics, flow cytometry, sequence variation analysis, pathway analysis, and others. (2) A library of featured genomic analysis notebooks is provided. These include templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. (3) Notebook enhancements. A rich text editor allows scientists to enter and format text as they would in a word processor. A user interface-building tool allows notebook developers to wrap their code so it is displayed as a web form, with only the necessary inputs exposed. Users of the notebook are presented with a simplified display that allows them to run the analyses without needing to interact with the code behind them. (4) Publication and collaborative editing. To make a notebook public, an author selects the “publish” feature and adds descriptive information. The notebook is then made available on the community section of the notebook workspace. An author can include a web link to a public notebook in a publication, and users who follow the link will see a read-only version of the notebook, with the option to log in to the workspace, where they can run, copy, and edit their own version. The GenePattern Notebook environment is freely available at http://genepattern-notebook.org. Citation Format: Michael M. Reich, Thorin Tabor, Ted Liefeld, Edwin Juarez, Barbara Hill, Helga Thorvaldsdottir, Pablo Tamayo, Jill P. Mesirov. GenePattern Notebook: An integrative analytical environment for cancer research [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3207.

Nephroprotective effect of Combretum micranthum G. Don in nicotinamide-streptozotocin induced diabetic nephropathy in rats: In-vivo and in-silico experiments

Ethnopharmacological relevanceCombretum micranthum G. Don (CM) is extensively used in traditional medicine throughout West Africa and commonly known as “long-life herbal tea” or “plant to heal”. Further, traditional healers frequently use the title plant to mitigate of renal disorders. Aim of the studyTo explore the nephroprotective property of standardised hydroalcoholic extract of Combretum micranthum in nicotinamide-streptozotocin induced diabetic nephropathy in rats. In addition, in-silico computational experiments were performed with bioactive compounds of the title plant against PPARα and PPARγ. Material and methodsMale rats were made diabetic by a single intraperitoneal (ip) injection of STZ (50 mg/kg), 15 min after ip administration of NA (100 mg/kg) dissolved in normal saline. The diabetic rats received CM extract (200 and 400 mg/kg p.o.) daily, for eight weeks. Body weights and blood glucose (non-fasting and fasting) of rats were measured weekly. Daily food and water consumption were also measured. After 8 weeks of treatment, urine biochemical parameters such as N-Acetyl-β-D-Glucosaminidase (NAG), urea (UR), uric acid (UA), creatinine (CRE), and serum markers of diabetes, kidney damage and liver damage such as insulin, lipid parameters), alanine aminotransferase (ALT), aspartate aminotransferase (AST) and gamma-glutamyl transferase (γGT), albumin (Alb), magnesium (Mg2+), calcium (Ca2+), phosphorus (P), were estimated. Blood glycosylated hemoglobin (HbA1C) were also estimated. kidney and liver were used for biochemical estimation of oxidative stress markers such as lipid peroxidation, superoxide dismutase (SOD) activity and glutathione peroxidase (GPx) activity. The kidney and pancreas were used for histopathological study. Further, HPLC chemoprofiling of CM extract and in-silico molecular simulation experiments were performed. ResultsAt the end of eight weeks, renal damage induced by the consequence of prolong diabetic condition was confirmed by altered levels of serum and urine kidney and liver function markers, oxidative stress markers and histopathological variations in kidney. Treatment with CM extract ameliorated the diabetes mellitus-induced renal biochemical parameters and histopathological changes. Further, HPLC-UV & MS experiments revealed that CM extract contains several bioactive compounds including hyperozide (62.35 μg/mg of extract) and quercitrin (19.07 μg/mg of extract). In-silico experiment exhibited cianidanol (−17.133), epicatechin (−15.107) exhibited higher docking score against PPARα and luteoforol (−11.038), epigallocatechin (−10.736) against PPARγ. Based on docking and drug likeness score, four bioactive compounds were selected for molecular dynamic experiments. Cianidanol and epigallocatechin out of the 30 compounds are concluded as a potential candidate for the treatment of DN through activating PPARα and PPARγ target protein. ConclusionsTaken together, the present study provided the scientific footage for the traditional use of Combretum micranthum.

The anti-HIV drug nelfinavir mesylate (Viracept) is a potent inhibitor of cell fusion caused by the SARSCoV-2 spike (S) glycoprotein warranting further evaluation as an antiviral against COVID-19 infections.

Severe acute respiratory syndrome coronavirus‐2 (SARS CoV‐2) is the causative agent of the coronavirus disease‐2019 (COVID‐19) pandemic. Coronaviruses enter cells via fusion of the viral envelope with the plasma membrane and/or via fusion of the viral envelope with endosomal membranes after virion endocytosis. The spike (S) glycoprotein is a major determinant of virus infectivity. Herein, we show that the transient expression of the SARS CoV‐2 S glycoprotein in Vero cells caused extensive cell fusion (formation of syncytia) in comparison to limited cell fusion caused by the SARS S glycoprotein. Both S glycoproteins were detected intracellularly and on transfected Vero cell surfaces. These results are in agreement with published pathology observations of extensive syncytia formation in lung tissues of patients with COVID‐19. These results suggest that SARS CoV‐2 is able to spread from cell‐to‐cell much more efficiently than SARS effectively avoiding extracellular neutralizing antibodies. A systematic screening of several drugs including cardiac glycosides and kinase inhibitors and inhibitors of human immunodeficiency virus (HIV) entry revealed that only the FDA‐approved HIV protease inhibitor, nelfinavir mesylate (Viracept) drastically inhibited S‐n‐ and S‐o‐mediated cell fusion with complete inhibition at a 10‐μM concentration. In‐silico docking experiments suggested the possibility that nelfinavir may bind inside the S trimer structure, proximal to the S2 amino terminus directly inhibiting S‐n‐ and S‐o‐mediated membrane fusion. Also, it is possible that nelfinavir may act to inhibit S proteolytic processing within cells. These results warrant further investigations of the potential of nelfinavir mesylate to inhibit virus spread at early times after SARS CoV‐2 symptoms appear.

A multi-objective optimization model for dairy feeding management

The allocation of feedstuff to intensively managed dairy cows to achieve different objectives is challenging due to the inherent complexity of the system and the combinatorial problem that has to be solved. Pareto-based multi-objective optimization approaches using evolutionary algorithms can help to address these challenges and show the trade-offs and synergies among various objectives. Here we present a framework for multi-objective optimization with the Differential Evolution (DE) algorithm applied to dairy feeding systems with grazing and concentrate supply to generate an approximation of the Pareto front. The available feed resources are located in different feeding areas, and the number of animals and groups of animals with similar feeding requirements are distributed across these areas for feeding purposes. To evaluate the DE algorithm, we performed two in-silico experiments to: (1) compare the solutions quality of single-objective DE with exact Linear Programming (LP) solutions, and (2) assess the influence of different stocking rates (number of cows/ha) on milk production, feed allocation and economic performance indicators. The DE solutions that minimize the feeding costs for different stocking rates (1.1–2.6 cows/ha) closely approached the solutions derived with LP, confirming the quality of the heuristic algorithm. The multi-objective model scenarios demonstrated that increasing stocking density would enhance milk production and gross margin per unit of area at largely unchanged productivity per animal by shifting the feed ration from roughage to a large proportion of supplementary concentrate feed. At low stocking rates solutions with high productivity and gross margin and a large proportion of roughage in the ration and limited supplementary feeding were identified. We conclude that the multi-objective optimization with a Pareto-based DE algorithm is highly effective to explore the interrelations among conflicting objectives and to find suitable solutions.

Mechanistic model of pH and excipient concentration during ultrafiltration and diafiltration processes of therapeutic antibodies.

Ultrafiltration and diafiltration (UF/DF) unit operations are widely used for the manufacture of therapeutic antibodies to control drug substance protein concentration, pH, and excipient properties. During UF/DF, molecular interactions and volume exclusion effects often lead to substantial differences in pH and excipient concentrations between the diafiltration buffer and final UF/DF pool. These differences complicate the design process beyond simply specifying a buffer with the desired drug substance pH and excipient conditions. This article describes a UF/DF process model which dynamically and accurately simulates UF/DF retentate pool pH and excipient conditions throughout the UF/DF process. This multiscale model accounts for microscopic descriptions of ion-protein charge interactions using the Poisson-Boltzmann equation as well as macroscopic descriptions of volume exclusion and mass transfer. Model predictions of the final UF/DF pool properties were experimentally verified through comparisons to design of experiment (DoE) data from four monoclonal antibody (mAb) processes, each with differing formulations and UF/DF operating conditions. Additionally, model simulations of the retentate pool properties throughout the UF/DF process were verified for two mAb processes through comparisons to experimental data collected at intermediate process points. Model results were qualified, using statistical equivalence tests, against the outputs from large-scale GMP runs which confirmed that the model accurately captures large-scale process performance. Finally, the model was applied toward the simulation of process scenarios beyond those examined experimentally. These in-silico experiments demonstrate the model's capability as a tool for augmented process design and it is potential to reduce the extent of UF/DF laboratory experiments.

Reactive, Proactive, and Inductive Agents: An Evolutionary Path for Biological and Artificial Spiking Networks.

Complex environments provide structured yet variable sensory inputs. To best exploit information from these environments, organisms must evolve the ability to anticipate consequences of new stimuli, and act on these predictions. We propose an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior and from simple proactive behavior to induction-based behavior. Based on earlier in-vitro and in-silico experiments, we define the conditions necessary in a network with spike-timing dependent plasticity for the organism to go from reactive to proactive behavior. Our results support the existence of specific evolutionary steps and four conditions necessary for embodied neural networks to evolve predictive and inductive abilities from an initial reactive strategy.

Perturbed spiral real-time phase-contrast MR with compressive sensing reconstruction for assessment of flow in children.

we implemented a golden-angle spiral phase contrast sequence. A commonly used uniform density spiral and a new 'perturbed' spiral that produces more incoherent aliases were assessed. The aim was to ascertain whether greater incoherence enabled more accurate Compressive Sensing reconstruction and superior measurement of flow and velocity. A range of 'perturbed' spiral trajectories based on a uniform spiral trajectory were formulated. The trajectory that produced the most noise-like aliases was selected for further testing. For in-silico and in-vivo experiments, data was reconstructed using total Variation L1 regularisation in the spatial and temporal domains. In-silico, the reconstruction accuracy of the 'perturbed' golden spiral was compared to uniform density golden-angle spiral. For the in-vivo experiment, stroke volume and peak mean velocity were measured in 20 children using 'perturbed' and uniform density golden-angle spiral sequences. These were compared to a reference standard gated Cartesian sequence. In-silico, the perturbed spiral acquisition produced more accurate reconstructions with less temporal blurring (NRMSE ranging from 0.03 to 0.05) than the uniform density acquisition (NRMSE ranging from 0.06 to 0.12). This translated in more accurate results in-vivo with no significant bias in the peak mean velocity (bias: -0.1, limits: -4.4 to 4.1 cm/s; P = 0.98) or stroke volume (bias: -1.8, limits: -9.4 to 5.8 ml, P = 0.19). We showed that a 'perturbed' golden-angle spiral approach is better suited to Compressive Sensing reconstruction due to more incoherent aliases. This enabled accurate real-time measurement of flow and peak velocity in children.

Data-driven modeling of zebrafish behavioral response to acute caffeine administration

Over the last thirty years, we have witnessed a dramatic rise in the use of zebrafish in preclinical research. Every year, more than 5000 technical papers are published about zebrafish, many of them seeking to explain the underpinnings of anxiety through animal testing. In-silico experiments could significantly contribute to zebrafish research and welfare, by offering new means to support the 3Rs principles of replacement, reduction, and refinement. Here, we propose a data-driven modeling framework to predict the anxiety-related behavioral response of zebrafish to acute caffeine administration. The modeling framework unfolds along a two-time-scale dichotomy to capture freezing behavior along a slow temporal scale and burst-and-coast locomotion at a fast time-scale. Anchored in the theory of Markov chains and stochastic differential equations, we demonstrate a parsimonious, yet robust, modeling framework to accurately simulate experimental observations of zebrafish treated at different caffeine concentrations. Our results complement recent modeling efforts, laying the foundations for conducting in-silico experiments in zebrafish behavioral pharmacology.

Partitioning of the pore space based on a non-hierarchical decomposition model

Bio-CAD and in-silico experimentation currently have a growing interest in biomedical applications where scientific data coming from real samples are used to evaluate physical properties. In this sense, analyzing the pore-size distribution is a demanding task to help interpret the characteristics of porous materials by partitioning it into its constituent pores. Pores are defined intuitively as local openings that can be interconnected by narrow apertures called throats that control a non-wetting phase invasion in a physical method. There are several approaches to characterize the pore space in terms of its constituent pores, several of them requiring prior computation of a skeleton. This paper presents a new approach to characterize the pore space, in terms of a pore-size distribution, which does not require the skeleton computation. Throats are identified using a new decomposition model that performs a spatial partition of the object in a non-hierarchical sweep-based way consisting of a set of disjoint boxes. This approach enables the characterization of the pore space in terms of a pore-size distribution.
 computation. Throats are identified using a new decomposition model that performs a spatial partition of the object in a non-hierarchical sweep-based way consisting of a set of disjoint boxes. This approach enables the characterization of the pore space in terms of a pore-size distribution.

Optimizing treatment combination for lymphoma using an optimization heuristic

Background. The standard treatment for high-grade non-Hodgkin lymphoma involves the combination of chemotherapy and immunotherapy. We characterize in-silico the optimal combination protocol that maximizes the overall survival probability. We rely on a pharmacokinetics/pharmacodynamics (PK/PD) model that describes the joint evolution of tumor and effector cells, as well as the effects of both chemotherapy and immunotherapy. The toxicity is taken into account through ad-hoc constraints. We develop an optimization algorithm that belongs to the class of Monte-Carlo tree search algorithms. Our simulations rely on an in-silico population of heterogeneous patients differing with respect to their PK/PD parameters. The optimization objective consists in characterizing the combination protocol that maximizes the overall survival probability of the patient population under consideration.Results. We compare using in-silico experiments our results to standard protocols and observe a gain in overall survival probabilities that vary from 4 to 9 percentage points. The gains increase with the complexity of the potential protocol. Gains are larger in presence of a higher number of injections or of an actual combination with immunotherapy.Conclusions. In in-silico experiments, optimal protocols achieve significant gains over standard protocols when considering overall survival probabilities. Our optimization algorithm enables us to efficiently tackle this numerical problem with a large dimensionality. The in-vivo implications of our in-silico results remain to be explored.

Abstract 4233: Improving the diagnostic yield of a 124 gene pediatric solid tumor panel through somatic copy number variation analysis

Abstract In cancer genomes, both large-scale CNVs (>100 kb) spanning chromosome arms, and smaller CNVs limited to a few genes are prevalent. While large CNVs are readily detected with existing methodologies (e.g. SNP array), gene-level CNVs require a higher resolution not routinely available in current tools, including next generation sequencing (NGS)-based clinical cancer mutation panels. We sought to leverage NGS data generated by one of these panels: the TCH Pediatric Solid Tumor panel (124 cancer genes), and built a clinical-grade analytic pipeline for detection of somatic CNVs. After reviewing literature, CNVkit was selected for its ability to perform unmatched (requiring no matched normal specimen) CNV analysis, customize segmentation algorithms, and provide superior visualization. CNVkit performs circular binary segmentation on the log2 difference of binned read depths (on- and off-target) from tumor and pooled-normal blood samples to identify CNVs. Although the panel captures only 124 genes (~1Mbp) at > 300X coverage, the low background coverage of 0.05X (off-target) due to imperfect hybridization capture allows us to detect chromosome-arm level changes. As a pilot study, we optimized CNVkit to detect gene-level and chromosome arm-level CNVs in a reference aneuploid colon cancer cell line (HT-29) characterized by aCGH and observed high correlation (r = 0.89) between the aCGH and CNVkit fold change. Through in-silico and bench experiments we performed limit of detection analysis, by diluting different proportions of CNV positive tumor samples with normal sample, to set thresholds for calling amplification and losses. We show the ability of the pipeline to detect shallow and deep deletions if they are present in at least 40% and 80% of the sample sequenced, respectively. During validation, we used 24 pediatric cancer samples with diverse histology to confirm and detect numerous CNVs of clinical significance, including deletions of SMARCB1, PTEN, BRCA2, RB1, and ARID1B, and amplifications of MYCN, MYC, CCND1 and KRAS. As the panel densely tiles tumor suppressors, we are also able to infer apparent intragenic deletions in BRCA1, BRCA2, ATRX, RB1 and PTEN, highlighting the theoretical resolution of this tool for detecting intragenic events over other methods. We have also developed a Python based interactive CNV Viewer for assessing the copy number analysis results from NGS data. The browser like interface allows the user to zoom, link out to UCSC Genome Browser, hover for information, take high-quality screenshots, etc. Hence, the CNV pipeline we have developed will allow more comprehensive evaluation of the existing integrated DNA and RNA analysis pipeline, increasing the diagnostic yield of mutation panel testing for childhood cancer patients. Citation Format: Raghu Chandramohan, Jacquelyn Reuther, Ilavarasi Gandhi, Horatiu Voicu, Karla R. Alvarez, Kevin E. Fisher, Dolores H. Lopez-Terrada, Donald W. Parsons, Angshumoy Roy. Improving the diagnostic yield of a 124 gene pediatric solid tumor panel through somatic copy number variation analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4233.

Combining Mathematical Models With Experimentation to Drive Novel Mechanistic Insights Into Macrophage Function.

This perspective outlines an approach to improve mechanistic understanding of macrophages in inflammation and tissue homeostasis, with a focus on human inflammatory bowel disease (IBD). The approach integrates wet-lab and in-silico experimentation, driven by mechanistic mathematical models of relevant biological processes. Although wet-lab experimentation with genetically modified mouse models and primary human cells and tissues have provided important insights, the role of macrophages in human IBD remains poorly understood. Key open questions include: (1) To what degree hyperinflammatory processes (e.g., gain of cytokine production) and immunodeficiency (e.g., loss of bacterial killing) intersect to drive IBD pathophysiology? and (2) What are the roles of macrophage heterogeneity in IBD onset and progression? Mathematical modeling offers a synergistic approach that can be used to address such questions. Mechanistic models are useful for informing wet-lab experimental designs and provide a knowledge constrained framework for quantitative analysis and interpretation of resulting experimental data. The majority of published mathematical models of macrophage function are based either on animal models, or immortalized human cell lines. These experimental models do not recapitulate important features of human gastrointestinal pathophysiology, and, therefore are limited in the extent to which they can fully inform understanding of human IBD. Thus, we envision a future where mechanistic mathematical models are based on features relevant to human disease and parametrized by richer human datasets, including biopsy tissues taken from IBD patients, human organ-on-a-chip systems and other high-throughput clinical data derived from experimental medicine studies and/or clinical trials on IBD patients.

Nephroprotective activity of Combretum micranthum G. Don in cisplatin induced nephrotoxicity in rats: In-vitro, in-vivo and in-silico experiments

Nephrotoxicity is known to be a major complication during cisplatin chemotherapy in cancer patients. In the present study, the protective effect of a hydroalcoholic extract of Combretum micranthum (CM) against cisplatin (CP)-induced renal damage was evaluated using in-vitro human embryonic kidney (HEK)-293 cells and in-vivo experiments. Further, in-silico molecular docking and dynamic experiments were carried out with bioactive compounds of the title plant against nuclear factor kappa B (NF-κB) and soluble epoxide hydrolase (sEH). Incubation of HEK-293 cells with cisplatin resulted in a significant increase in cell death with changes in normal cellular morphology. Co-treatment of HEK-293 cells with CP and CM extract at varying concentrations resulted in significant enhancement of cell growth compared to CP treatment indicating the cytoprotective activity of CM with an EC50 8.136 μg/mL. In vivo nephroprotective activity was evaluated by administering CM (200 and 400 mg/kg, p.o) to rats for 10 days followed by single intraperitonial injection of CP (7.5 mg/kg) on the 5th day of the experiment. Nephrotoxicity induced by CP was apparent by elevated levels of serum and urine kidney function markers, transaminases, oxidative stress markers and histopathological alterations in kidney. Pre-treatment with CM normalized the renal function at both the doses by ameliorating the CP-induced renal damage markers, oxidative stress and histopathological variations. In-silico studies showed that, out of the thirty bioactive compounds, isovitexin and gallic acid exhibited a higher docking score of −22.467, −21.167 kcal/mol against NF-κB. Cianidanol and epicatechin exhibited a higher docking score of −14.234, −14.209 kcal/mol against sEH. The protective effect of CM extract in CP-induced nephrotoxicity might be attributed to its antioxidant, anti-inflammatory activity by inhibiting NF-κB and sEH upregulation.

LibreGrowth: A tumor growth code based on reaction–diffusion equations using shared memory

In recent years, in-silico experimentation within the field of oncological medicine has been intensively investigated with the aim of better understanding tumor dynamics and dose–response relationships in cancer treatments. In a series of previous works, Luján et al. (2018, 2017, 2016)we described the micro-environmental influence on micro-tumor infiltration patterns through in-silico/in-vitro experimentation. Here we present the latest version of the software utilized for, but not limited to, those studies: LibreGrowth, a libre tumor growth code able to simulate the core growth and peripheral tumor cell infiltration, considering a benign and a malignant stages. We implemented a reaction–diffusion based model, with spatially variable diffusion coefficient, into a three-dimensional domain, using C++ and OpenMP over a GNU/Linux system. LibreGrowth aims to provide a flexible implementation for depicting heterogeneous tissues and infiltration processes, and to shed light in current therapy optimization strategies. Program summaryProgram Title: LibreGrowthProgram Files doi:http://dx.doi.org/10.17632/zp2my52xpv.1Licensing provisions: GPLv3Programming language: C++, OpenMPSupplementary material:Nature of problem: In the field of computational oncology, mathematical models based on reaction–diffusion equations describing tumor proliferation and invasion into peripheral host tissue have proved to be of clinical relevance. In-silico/in-vitro experimentation could help in the design of new strategies able to predict as much as possible the invasive behavior of a tumor, based on its particular properties and the bio-physicochemical characteristics of its microenvironment.Solution method: We introduced LibreGrowth, the latest version of the codes used in our previous studies to model the growth and infiltration of a tumor. It implements mentioned reaction–diffusion model through the standard finite difference method, using C++ and OpenMP parallelization technology.

Influence of particle size and blender size on blending performance of bi-component granular mixing: A DEM and experimental study

The effect of particle size enlargement and blender geometry down-scaling on the blend uniformity (BU) was evaluated by Discrete Element Method (DEM) to predict the blending performance of a binary granular mixture. Three 10 kg blending experiments differentiated by the physical properties specifically particle size were performed as reference for DEM simulations. The segregation behavior observed during the diffusion blending was common for all blends, while the sample BU, i.e., standard deviation of active ingredient content % was different among the three blends reflecting segregation due to the particle size differences between the components. Quantitative prediction of the sample BU probability density distribution in reality based on the DEM simulation results was successfully demonstrated. The average root mean square error normalized by the mean of the mean sample BU in the blends was 0.228. Beside the ratio of blender container to particle size, total number of particles in the blender and the number of particles in a sample were confirmed critical for the blending performance. These in-silico experiments through DEM simulations would help in setting a design space with respect to the particle size and in a broader sense with respect to the physical properties in general.

A concept for single-shot volumetric fluorescence imaging via orthogonally polarized excitation lattices

The deconvolution of widefield fluorescence images provides only guesses of spatial frequency information along the optical axis due to the so called missing cone in the optical transfer function. Retaining the single-shot imaging speed of deconvolution microscopy while gaining access to missing cone information is thus highly desirable for microscopy of volumetric samples. Here, we present a concept that superimposes two orthogonally polarized excitation lattices with a phase-shift of p between them. In conjunction with a non-iterative image reconstruction algorithm this permits the restoration of missing cone information. We show how fluorescence anisotropy could be used as a method to encode and decode the patterns simultaneously and develop a rigorous theoretical framework for the method. Through in-silico experiments and imaging of fixed biological cells on a structured illumination microscope that emulates the proposed setup we validate the feasibility of the method.

Characterizing the clinical implementation of a novel activation-repolarization metric to identify targets for catheter ablation of ventricular tachycardias using computational models

Identification of targets for catheter ablation of ventricular tachycardias (VTs) remains a significant challenge. VTs are often driven by re-entrant circuits resulting from a complex interaction between the front (activation) and tail (repolarization) of the electrical wavefront. Most mapping techniques do not take into account the tissue repolarization which may hinder the detection of ablation targets. The re-entry vulnerability index (RVI), a recently proposed mapping procedure, incorporates both activation and repolarization times to uncover VT circuits. The method showed potential in a series of experiments, but it still requires further development to enable its incorporation into a clinical protocol. Here, in-silico experiments were conducted to thoroughly assess RVI maps constructed under clinically-relevant mapping conditions. Within idealized as well as anatomically realistic infarct models, we show that parameters of the algorithm such as the search radius can significantly alter the specificity and sensitivity of the RVI maps. When constructed on sparse grids obtained following various placements of clinical recording catheters, RVI maps can identify vulnerable regions as long as two electrodes were placed on both sides of the line of block. Moreover, maps computed during pacing without inducing VT can reveal areas of abnormal repolarization and slow conduction but not directly vulnerability. In conclusion, the RVI algorithm can detect re-entrant circuits during VT from low resolution mapping grids resembling the clinical setting. Furthermore, RVI maps may provide information about the underlying tissue electrophysiology to guide catheter ablation without the need of inducing potentially harmful VT during the clinical procedure. Finally, the ability of the RVI maps to identify vulnerable regions with specificity in high resolution computer models could potentially improve the prediction of optimal ablation targets of simulation-based strategies.

Toward Brain-Inspired Learning With the Neuromorphic Snake-Like Robot and the Neurorobotic Platform

Neurorobotic mimics the structural and functional principles of living creature systems. Modeling a single system by robotic hardware and software has existed for decades. However, an integrated toolset studying the interaction of all systems has not been demonstrated yet. We present a hybrid neuromorphic computing paradigm to bridge this gap by combining the neurorobotics platform (NRP) with the neuromorphic snake-like robot (NeuroSnake). This paradigm encompasses the virtual models, neuromorphic sensing and computing capabilities, and physical bio-inspired bodies, with which an experimenter can design and execute both in-silico and in-vivo robotic experimentation easily. The NRP is a public Web-based platform for easily testing brain models with virtual bodies and environments. The NeuroSnake is a bio-inspired robot equipped with a silico-retina sensor and neuromorphic computer for power-efficiency applications. We illustrate the efficiencies of our paradigm with an easy designing of a visual pursuit experiment in the NRP. We study two automatic behavior learning tasks which are further integrated into a complex task of semi-autonomous pole climbing. The result shows that robots could build new learning rules in a less explicit manner inspired by living creatures. Our method gives an alternative way to efficiently develop complex behavior control of the ro As spiking neural network is a bio-inspired neural network and the NeuroSnake robot is equipped with a spike-based silicon retina camera, the control system can be easily implemented via spiking neurons simulated on neuromorphic hardware, such as SpiNNaker.bot.