Physiological Environment Induces Quick Response – Slow Exhaustion Reactions

In vivo environments are highly crowded and inhomogeneous, which may affect reaction processes in cells. In this study we examined the effects of intracellular crowding and an inhomogeneity on the behavior of in vivo reactions by calculating the spectral dimension (ds), which can be translated into the reaction rate function. We compared estimates of anomaly parameters obtained from fluorescence correlation spectroscopy (FCS) data with fractal dimensions derived from transmission electron microscopy (TEM) image analysis. FCS analysis indicated that the anomalous property was linked to physiological structure. Subsequent TEM analysis provided an in vivo illustration; soluble molecules likely percolate between intracellular clusters, which are constructed in a self-organizing manner. We estimated a cytoplasmic spectral dimension ds to be 1.39 ± 0.084. This result suggests that in vivo reactions initially run faster than the same reactions in a homogeneous space; this conclusion is consistent with the anomalous character indicated by FCS analysis. We further showed that these results were compatible with our Monte-Carlo simulation in which the anomalous behavior of mobile molecules correlates with the intracellular environment, leading to description as a percolation cluster, as demonstrated using TEM analysis. We confirmed by the simulation that the above-mentioned in vivo like properties are different from those of homogeneously concentrated environments. Additionally, simulation results indicated that crowding level of an environment might affect diffusion rate of reactant. Such knowledge of the spatial information enables us to construct realistic models for in vivo diffusion and reaction systems

Establishment of a New Cell Line(MTT-95) Showing Basophilic Differentiation from the Bone Marrow of a Patient with Acute Myelogenous Leukemia (M7)

A new myeloid cell line, MTT-95, was established from the bone marrow of a patient with acute myelogenous leukemia (AML, M7). MTT-95 cells differentiate into mature basophilic cells in culture medium with no chemical component or cytokine. Surface phenotypes were as follows: CD11b 79.3%, CD13 92.4%, CD33 99.8%, CD34 87.9%, CD41a 77.6% and HLA-DR 0.3%. MTT-95 cells were strongly positive for glycoprotein IIb/IIIa by immunohistochemical staining and revealed metachromatic granules. MTT-95 cells seem to possess characteristics of both megakaryocytes and basophils. These findings suggest that MTT-95 cells are basophil progenitors. MTT-95 cells might be useful in the study not only of the biological aspects of basophils, but also of the diversities of AML (M7).</p

From microscopy data to in silico environments for in vivo-oriented simulations

In our previous study, we introduced a combination methodology of Fluorescence Correlation Spectroscopy (FCS) and Transmission Electron Microscopy (TEM), which is powerful to investigate the effect of intracellular environment to biochemical reaction processes. Now, we developed a reconstruction method of realistic simulation spaces based on our TEM images. Interactive raytracing visualization of this space allows the perception of the overall 3D structure, which is not directly accessible from 2D TEM images. Simulation results show that the diffusion in such generated structures strongly depends on image post-processing. Frayed structures corresponding to noisy images hinder the diffusion much stronger than smooth surfaces from denoised images. This means that the correct identification of noise or structure is significant to reconstruct appropriate reaction environment in silico in order to estimate realistic behaviors of reactants in vivo. Static structures lead to anomalous diffusion due to the partial confinement. In contrast, mobile crowding agents do not lead to anomalous diffusion at moderate crowding levels. By varying the mobility of these non-reactive obstacles (NRO), we estimated the relationship between NRO diffusion coefficient (Dnro) and the anomaly in the tracer diffusion (α). For Dnro=21.96 to 44.49 μ m2/s, the simulation results match the anomaly obtained from FCS measurements. This range of the diffusion coefficient from simulations is compatible with the range of the diffusion coefficient of structural proteins in the cytoplasm. In addition, we investigated the relationship between the radius of NRO and anomalous diffusion coefficient of tracers by the comparison between different simulations. The radius of NRO has to be 58 nm when the polymer moves with the same diffusion speed as a reactant, which is close to the radius of functional protein complexes in a cell.ISSN:1687-4145ISSN:1687-415

An FPGA-Based, Multi-model Simulation Method for Biochemical Systems

Modeling and simulation of a cellular system on computers are now becoming an essential process in biological researches. However, modern PCs can\u27t provide enough performance to simulate large-scale biochemical networks. ReCSiP is the alternative FPGA-based solution for biochemical simulations. In this paper, the novel method of biochemical simulation with multiple reaction models on an FPGA is proposed. The method generates optimal circuit and its optimal schedule for each simulation models written in SBML, the standard markup language in systems biology. ReCSiP has a Xilinx\u27s XC2VP70 and achieved over 20-fold speedup compared to Intel’s PentiumIII 1.13GHz.19th IEEE International Parallel and Distributed Processing Symposium (IPDPS\u2705), April 4-8, 2005, Denver, Colorad

Pipeline scheduling with input port constraints for an FPGA-based biochemical simulator

This paper discusses design methodology of high-throughput arithmetic pipeline modules for an FPGA-based biochemical simulator. Since limitation of data-input bandwidth caused by port constraints often has a negative impact on pipeline scheduling results, we propose a priority assignment method of input data which enables efficient arithmetic pipeline scheduling under given input port constraints. Evaluation results with frequently used rate-law functions in biochemical models revealed that the proposed method achieved shorter latency compared to ASAP and ALAP scheduling with random input orders, reducing hardware costs by 17.57% and by 27.43% on average, respectively.The original publication is available at www.springerlink.co

Giant gastrointestinal stromal tumor, associated with esophageal hiatus hernia

An 85-year-old woman was admitted to our hospital because of vomiting. An upper gastrointestinal series what showed a large esophageal hiatus hernia, suggesting an association with extrinsic pressure in the middle portion of the stomach. An upper gastrointestinal endoscopic examination showed severe esophagitis and a prominent narrowing in the middle portion of the stomach, however, it showed normal gastric mucosa findings. CT and MRI revealed a large tumor extending from the region of the lower chest to the upper abdomen. From these findings, the tumor was diagnosed as gastrointestinal stromal tumor(GIST), which arose from the gastric wall and complicated with an esophageal hiatus hernia. We performed a laparotomy, however, the tumor showed severe invasion to the circumferential organs. Therefore, we abandoned the excision of the tumor. Histologically, the tumor was composed of spindle shaped cells with marked nuclear atypia and prominent mitosis. The tumor cells were strongly positive for CD34 and c-kit by immunohistochemical examination. From these findings, the tumor was definitely diagnosed as a malignant GIST. As palliative treatment, we implanted a self-expandable metallic stent in the narrow segment of the stomach. The patient could eat solid food and was discharged. In the treatment of esophageal hiatus hernia, the rare association of GIST should be considered

The systems biology simulation core algorithm

Background: With the increasing availability of high dimensional time course data for metabolites, genes, and fluxes, the mathematical description of dynamical systems has become an essential aspect of research in systems biology. Models are often encoded in formats such as SBML, whose structure is very complex and difficult to evaluate due to many special cases. Results: This article describes an efficient algorithm to solve SBML models that are interpreted in terms of ordinary differential equations. We begin our consideration with a formal representation of the mathematical form of the models and explain all parts of the algorithm in detail, including several preprocessing steps. We provide a flexible reference implementation as part of the Systems Biology Simulation Core Library, a community-driven project providing a large collection of numerical solvers and a sophisticated interface hierarchy for the definition of custom differential equation systems. To demonstrate the capabilities of the new algorithm, it has been tested with the entire SBML Test Suite and all models of BioModels Database. Conclusions: The formal description of the mathematics behind the SBML format facilitates the implementation of the algorithm within specifically tailored programs. The reference implementation can be used as a simulation backend for Java™-based programs. Source code, binaries, and documentation can be freely obtained under the terms of the LGPL version 3 from http://simulation-core.sourceforge.net. Feature requests, bug reports, contributions, or any further discussion can be directed to the mailing list [email protected]

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.Peer Reviewe

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective