Details of a Researcher - FUNAHASHI Akira

Papers - FUNAHASHI Akira

Division display 1 - 20 of about 119 ／ All the affair displays >>

Novère Nicolas Le, Hucka Michael, Mi Huaiyu, Moodie Stuart, Schreiber Falk, Sorokin Anatoly, Demir Emek, Wegner Katja, Aladjem Mirit I, Wimalaratne Sarala M, Bergman Frank T, Gauges Ralph, Ghazal Peter, Kawaji Hideya, Li Lu, Matsuoka Yukiko, Villéger Alice, Boyd Sarah E, Calzone Laurence, Courtot Melanie, Dogrusoz Ugur, Freeman Tom C, FUNAHASHI AKIRA, Ghosh Samik, Jouraku Akiya, Kim Sohyoung, Kolpakov Fedor, Luna Augustin, Sahle Sven, Schmidt Esther, Watterson Steven, Wu Guanming, Goryanin Igor, Kell Douglas B, Sander Chris, Sauro Herbert, Snoep Jacky, L Kohn Kurt, Kitano Hiroaki

Nature Biotechnology 27 ( 8 ) 735-741 - 741 2009.08

Research paper (scientific journal), Joint Work, Accepted, ISSN 1087-0156

Access to Document (DOI)

Using process diagrams for the graphical representation of biological networks

Kitano Hiroaki, Funahashi Akira, Matsuoka Yukiko, Oda Kanae

Nature Biotechnology 23 ( 8 ) 961 - 966 2005.08

ISSN 1087-0156

<p>With the increased interest in understanding biological networks, such as protein-protein interaction networks and gene regulatory networks, methods for representing and communicating such networks in both human- and machine-readable form have become increasingly important. Although there has been significant progress in machine-readable representation of networks, as exemplified by the Systems Biology Mark-up Language (SBML) (http://www.sbml.org) issues in human-readable representation have been largely ignored. This article discusses human-readable diagrammatic representations and proposes a set of notations that enhances the formality and richness of the information represented. The process diagram is a fully state transition-based diagram that can be translated into machine-readable forms such as SBML in a straightforward way. It is supported by CellDesigner, a diagrammatic network editing software (http://www.celldesigner. org/), and has been used to represent a variety of networks of various sizes (from only a few components to several hundred components).</p>

Access to Document (DOI)

A comprehensive pathway map of epidermal growth factor receptor signaling.

Oda, K., Matsuoka, Y., Funahashi, A., and Kitano, H.

Nature Molecular Systems Biology 1 ( 1 ) 1-17 2005.05

Research paper (scientific journal), Joint Work, Accepted, ISSN 1744-4292

Access to Document (DOI)

3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis

Tokuoka Y, Yamada T.G, Mashiko D, Ikeda Z, Hiroi N.F, Kobayashi T.J, Yamagata K, Funahashi A

npj Systems Biology and Applications (npj Systems Biology and Applications) 6 ( 1 ) 32 2020.12

Research paper (scientific journal), Joint Work, Accepted

© 2020, The Author(s). During embryogenesis, cells repeatedly divide and dynamically change their positions in three-dimensional (3D) space. A robust and accurate algorithm to acquire the 3D positions of the cells would help to reveal the mechanisms of embryogenesis. To acquire quantitative criteria of embryogenesis from time-series 3D microscopic images, image processing algorithms such as segmentation have been applied. Because the cells in embryos are considerably crowded, an algorithm to segment individual cells in detail and accurately is needed. To quantify the nuclear region of every cell from a time-series 3D fluorescence microscopic image of living cells, we developed QCANet, a convolutional neural network-based segmentation algorithm for 3D fluorescence bioimages. We demonstrated that QCANet outperformed 3D Mask R-CNN, which is currently considered as the best algorithm of instance segmentation. We showed that QCANet can be applied not only to developing mouse embryos but also to developing embryos of two other model species. Using QCANet, we were able to extract several quantitative criteria of embryogenesis from 11 early mouse embryos. We showed that the extracted criteria could be used to evaluate the differences between individual embryos. This study contributes to the development of fundamental approaches for assessing embryogenesis on the basis of extracted quantitative criteria.

Access to Document (DOI)

BioSimulators: a central registry of simulation engines and services for recommending specific tools

B Shaikh, LP Smith, D Vasilescu, G Marupilla, M Wilson, E Agmon, ...

Nucleic acids research 50 (W1), W108-W114 (Nucleic Acids Research) 50 ( W1 ) W108 - W114 2022.07

Joint Work, Accepted, ISSN 03051048

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

Access to Document (DOI)

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

M Ostaszewski, A Niarakis, A Mazein, I Kuperstein, R Phair, ...

Molecular systems biology 17 (10), e10387 (Molecular Systems Biology) 17 ( 10 ) e10387 2021.10

Joint Work, Accepted, ISSN 1744-4292

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.

Access to Document (DOI)

The principles of whole-cell modeling

Karr Jonathan R., Takahashi Koichi, Funahashi Akira

Current Opinion in Microbiology (Elsevier {BV}) 27 18 - 24 2015.10

Research paper (scientific journal), Joint Work, Accepted, ISSN 1369-5274

<p>Whole-cell models which comprehensively predict how phenotypes emerge from genotype promise to enable rational bioengineering and precision medicine. Here, we outline the key principles of whole-cell modeling which have emerged from our work developing bacterial whole-cell models: single-cellularity; functional, genetic, molecular, and temporal completeness; biophysical realism including temporal dynamics and stochastic variation; species-specificity; and model integration and reproducibility. We also outline the whole-cell model construction process, highlighting existing resources. Numerous challenges remain to achieving fully complete models including developing new experimental tools to more completely characterize cells and developing a strong theoretical understanding of hybrid mathematics. Solving these challenges requires collaboration among computational and experimental biologists, biophysicists, biochemists, applied mathematicians, computer scientists, and software engineers.</p>

Access to Document (DOI)

Acceleration of discrete stochastic biochemical simulation using GPGPU

Sumiyoshi, Kei, Hirata, Kazuki, Hiroi, Noriko, Funahashi, Akira

FRONTIERS IN PHYSIOLOGY 6 155 - 169 2015.02

Research paper (scientific journal), Joint Work, Accepted, ISSN 1664-042X

Access to Document (DOI)

SBML Level 3: an extensible format for the exchange and reuse of biological models

SM Keating, D Waltemath, M König, F Zhang, A Dräger, C Chaouiya, ...

Molecular systems biology 16 (8), e9110 16 ( 8 ) e9110 2020.08

Joint Work, Accepted, ISSN 1744-4292

Access to Document (DOI)

High-speed microscopy with an electrically tunable lens to image the dynamics of in vivo molecular complexes

Yuichiro Nakai; Mitsunori Ozeki; Takumi Hiraiwa; Ryuichi Tanimoto; Akira Funahashi; Noriko Hiroi; Atsushi Taniguchi; Shigenori Nonaka; Viviane Boilot; Roshan Shrestha; Joanna Clark; Naoka Tamura; Viji M. Draviam; Hiromasa Oku

Review of Scientific Instruments 86 ( 1 ) 013707 2015.01

Research paper (scientific journal), Joint Work, Accepted, ISSN 0034-6748

Access to Document (DOI)

Symbolic integration by integrating learning models with different strengths and weaknesses

H Kubota, Y Tokuoka, TG Yamada, A Funahashi

IEEE Access 10, 47000-47010 (IEEE Access) 10 47000 - 47010 2022

Joint Work, Accepted, ISSN 2169-3536

Integration is indispensable, not only in mathematics, but also in a wide range of other fields. A deep learning method has recently been developed and shown to be capable of integrating mathematical functions that could not previously be integrated on a computer. However, that method treats integration as equivalent to natural language translation and does not reflect mathematical information. In this study, we adjusted the learning model to take mathematical information into account and developed a wide range of learning models that learn the order of numerical operations more robustly. In this way, we achieved a 98.80% correct answer rate with symbolic integration, a higher rate than that of any existing method. We judged the correctness of the integration based on whether the derivative of the primitive function was consistent with the integrand. By building an integrated model based on this strategy, we achieved a 99.79% rate of correct answers with symbolic integration. In summary, we have developed a more accurate method of selecting the correct model than the existing method by judging the result of symbolic integration based on whether the output of the model equals the input formula when the output is differentiated.

Access to Document (DOI)

Identification of a master transcription factor and a regulatory mechanism for desiccation tolerance in the anhydrobiotic cell line Pv11

TG Yamada, Y Hiki, NF Hiroi, E Shagimardanova, O Gusev, R Cornette, ...

PLoS One 15 (3), e0230218 (PLoS ONE) 15 ( 3 ) e0230218 2020.03

Research paper (scientific journal), Joint Work, Accepted, ISSN 1932-6203

Copyright: © 2020 Yamada et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Water is essential for living organisms. Terrestrial organisms are incessantly exposed to the stress of losing water, desiccation stress. Avoiding the mortality caused by desiccation stress, many organisms acquired molecular mechanisms to tolerate desiccation. Larvae of the African midge, Polypedilum vanderplanki, and its embryonic cell line Pv11 tolerate desiccation stress by entering an ametabolic state, anhydrobiosis, and return to active life after rehydration. The genes related to desiccation tolerance have been comprehensively analyzed, but transcriptional regulatory mechanisms to induce these genes after desiccation or rehydration remain unclear. Here, we comprehensively analyzed the gene regulatory network in Pv11 cells and compared it with that of Drosophila melanogaster, a desiccation sensitive species. We demonstrated that nuclear transcription factor Y subunit gamma-like, which is important for drought stress tolerance in plants, and its transcriptional regulation of downstream positive feedback loops have a pivotal role in regulating various anhydrobiosis-related genes. This study provides an initial insight into the systemic mechanism of desiccation tolerance.

Access to Document (DOI)

Predicting the future direction of cell movement with convolutional neural networks

S Nishimoto, Y Tokuoka, TG Yamada, NF Hiroi, A Funahashi

PloS one 14 (9), e0221245 (PLoS ONE) 14 ( 9 ) e0221245 2019.09

Research paper (scientific journal), Joint Work, Accepted, ISSN 1932-6203

© 2019 Nishimoto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell movement. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future direction of cell movement from current cell shape, and can be used to automatically identify those morphological features that influence future cell movement.

Access to Document (DOI)

Detection of Temperature Difference in Neuronal Cells

Tanimoto, Ryuichi, Hiraiwa, Takumi, Nakai, Yuichiro, Shindo, Yutaka, Oka, Kotaro, Hiroi, Noriko, Funahashi, Akira

SCIENTIFIC REPORTS 6 22071 2016.03

Research paper (scientific journal), Joint Work, Accepted, ISSN 2045-2322

Access to Document (DOI)

Metabolic Tug-of-War between Glycolysis and Translation Revealed by Biochemical Reconstitution

G Sato, S Kinoshita, TG Yamada, S Arai, T Kitaguchi, A Funahashi, N Doi, ...

ACS Synthetic Biology (ACS Synthetic Biology) 13 ( 5 ) 1572 - 1581 2024.05

Joint Work, Accepted, ISSN 2161-5063

Inside cells, various biological systems work cooperatively for homeostasis and self-replication. These systems do not work independently as they compete for shared elements like ATP and NADH. However, it has been believed that such competition is not a problem in codependent biological systems such as the energy-supplying glycolysis and the energy-consuming translation system. In this study, we biochemically reconstituted the coupling system of glycolysis and translation using purified elements and found that the competition for ATP between glycolysis and protein synthesis interferes with their coupling. Both experiments and simulations revealed that this interference is derived from a metabolic tug-of-war between glycolysis and translation based on their reaction rates, which changes the threshold of the initial substrate concentration for the success coupling. By the metabolic tug-of-war, translation energized by strong glycolysis is facilitated by an exogenous ATPase, which normally inhibits translation. These findings provide chemical insights into the mechanism of competition among biological systems in living cells and provide a framework for the construction of synthetic metabolism in vitro.

Access to Document (DOI)

Switching from weak to strong cortical attachment of microtubules accounts for the transition from nuclear centration to spindle elongation in metazoans

S Tada, Y Yamazaki, K Yamamoto, K Fujii, TG Yamada, NF Hiroi, ...

Heliyon (Heliyon) 10 ( 3 ) e25494 2024.02

Joint Work, Accepted, ISSN 24058440

The centrosome is a major microtubule organizing center in animal cells. The position of the centrosomes inside the cell is important for cell functions such as cell cycle, and thus should be tightly regulated. Theoretical models based on the forces generated along the microtubules have been proposed to account for the dynamic movements of the centrosomes during the cell cycle. These models, however, often adopted inconsistent assumptions to explain distinct but successive movements, thus preventing a unified model for centrosome positioning. For the centration of the centrosomes, weak attachment of the astral microtubules to the cell cortex was assumed. In contrast, for the separation of the centrosomes during spindle elongation, strong attachment was assumed. Here, we mathematically analyzed these processes at steady state and found that the different assumptions are proper for each process. We experimentally validated our conclusion using nematode and sea urchin embryos by manipulating their shapes. Our results suggest the existence of a molecular mechanism that converts the cortical attachment from weak to strong during the transition from centrosome centration to spindle elongation.

Access to Document (DOI)

SBML level 3 package: spatial processes, version 1, release 1

JC Schaff, A Lakshminarayana, RF Murphy, FT Bergmann, A Funahashi, ...

Journal of Integrative Bioinformatics 20 (1), 20220054 20 ( 1 ) 2023.03

Joint Work, Accepted

Access to Document (DOI)

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

A Niarakis, M Ostaszewski, A Mazein, I Kuperstein, M Kutmon, ...

Frontiers in Immunology 14, 1282859 (Frontiers in Immunology) 14 1282859 2023

Joint Work, Accepted, ISSN 1664-3224

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.

Access to Document (DOI)

Titanium Culture Vessel Presenting Temperature Gradation for the Thermotolerance Estimation of Cells

C Imashiro, Y Jin, M Hayama, TG Yamada, A Funahashi, K Sakaguchi, ...

Cyborg and Bionic Systems 4, 0049 (Cyborg and Bionic Systems) 4 0049 2023

Joint Work, Accepted, ISSN 20971087

Hyperthermia can be induced to exploit the thermal intolerance of cancer cells, which is worse than that of normal cells, as a potential noninvasive cancer treatment. To develop an effective hyperthermia treatment, thermal cytotoxicity of cells should be comprehensively investigated. However, to conduct such investigations, the culture temperature must be accurately regulated. We previously reported a culture system in which the culture temperature could be accurately regulated by employing metallic culture vessels. However, appropriate temperature conditions for hyperthermia depend on the cell species. Consequently, several experiments need to be conducted, which is a bottleneck of inducing hyperthermia. Hence, we developed a cell culture system with temperature gradation on a metallic culture surface. Michigan Cancer Foundation-7 cells and normal human dermal fibroblasts were used as cancer and normal cell models, respectively. Normal cells showed stronger thermal tolerance; this was because the novel system immediately exhibited a temperature gradation. Thus, the developed culture system can be used to investigate the optimum thermal conditions for effective hyperthermia treatment. Furthermore, as the reactions of cultured cells can be effectively assessed with the present results, further research involving the thermal stimulation of cells is possible.

Access to Document (DOI)

Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning

J Komuro, Y Tokuoka, T Seki, D Kusumoto, H Hashimoto, T Katsuki, ...

Biochemical and Biophysical Research Communications 632, 181-188 (Biochemical and Biophysical Research Communications) 632 181 - 188 2022.12

Joint Work, Accepted, ISSN 0006291X

The number of patients with heart failure and related deaths is rapidly increasing worldwide, making it a major problem. Cardiac hypertrophy is a crucial preliminary step in heart failure, but its treatment has not yet been fully successful. In this study, we established a system to evaluate cardiomyocyte hypertrophy using a deep learning-based high-throughput screening system and identified drugs that inhibit it. First, primary cultured cardiomyocytes from neonatal rats were stimulated by both angiotensin II and endothelin-1, and cellular images were captured using a phase-contrast microscope. Subsequently, we used a deep learning model for instance segmentation and established a system to automatically and unbiasedly evaluate the cardiomyocyte size and perimeter. Using this system, we screened 100 FDA-approved drugs library and identified 12 drugs that inhibited cardiomyocyte hypertrophy. We focused on ezetimibe, a cholesterol absorption inhibitor, that inhibited cardiomyocyte hypertrophy in a dose-dependent manner in vitro. Additionally, ezetimibe improved the cardiac dysfunction induced by pressure overload in mice. These results suggest that the deep learning-based system is useful for the evaluation of cardiomyocyte hypertrophy and drug screening, leading to the development of new treatments for heart failure.

Access to Document (DOI)

　 Previous page - Next page 　