The Experts below are selected from a list of 243 Experts worldwide ranked by ideXlab platform

Shaoheng Liang - One of the best experts on this subject based on the ideXlab platform.

  • latent Periodic Process inference from single cell rna seq data
    Nature Communications, 2020
    Co-Authors: Shaoheng Liang, Fang Wang, Ken Chen
    Abstract:

    The development of a phenotype in a multicellular organism often involves multiple, simultaneously occurring biological Processes. Advances in single-cell RNA-sequencing make it possible to infer latent developmental Processes from the transcriptomic profiles of cells at various developmental stages. Accurate characterization is challenging however, particularly for Periodic Processes such as cell cycle. To address this, we develop Cyclum, an autoencoder approach identifying circular trajectories in the gene expression space. Cyclum substantially improves the accuracy and robustness of cell-cycle characterization beyond existing approaches. Applying Cyclum to removing cell-cycle effects substantially improves delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. Traditional methods for determining cell type composition lack scalability, while single-cell technologies remain costly and noisy compared to bulk RNA-seq. Here, the authors present a highly efficient tool to measure cellular heterogeneity in bulk expression through robust integration of single-cell information.

  • latent Periodic Process inference from single cell rna seq data
    bioRxiv, 2019
    Co-Authors: Shaoheng Liang, Fang Wang, Ken Chen
    Abstract:

    Convoluted biological Processes underlie the development of multicellular organisms and diseases. Advances in scRNA-seq make it possible to study these Processes from cells at various developmental stages. Achieving accurate characterization is challenging, however, particularly for Periodic Processes, such as cell cycles. To address this, we developed Cyclum, a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Cyclum substantially improves the accuracy and robustness of cell-cycle characterization beyond existing approaches. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. Cyclum is available at https://github.com/KChen-lab/cyclum.

Ken Chen - One of the best experts on this subject based on the ideXlab platform.

  • latent Periodic Process inference from single cell rna seq data
    Nature Communications, 2020
    Co-Authors: Shaoheng Liang, Fang Wang, Ken Chen
    Abstract:

    The development of a phenotype in a multicellular organism often involves multiple, simultaneously occurring biological Processes. Advances in single-cell RNA-sequencing make it possible to infer latent developmental Processes from the transcriptomic profiles of cells at various developmental stages. Accurate characterization is challenging however, particularly for Periodic Processes such as cell cycle. To address this, we develop Cyclum, an autoencoder approach identifying circular trajectories in the gene expression space. Cyclum substantially improves the accuracy and robustness of cell-cycle characterization beyond existing approaches. Applying Cyclum to removing cell-cycle effects substantially improves delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. Traditional methods for determining cell type composition lack scalability, while single-cell technologies remain costly and noisy compared to bulk RNA-seq. Here, the authors present a highly efficient tool to measure cellular heterogeneity in bulk expression through robust integration of single-cell information.

  • latent Periodic Process inference from single cell rna seq data
    bioRxiv, 2019
    Co-Authors: Shaoheng Liang, Fang Wang, Ken Chen
    Abstract:

    Convoluted biological Processes underlie the development of multicellular organisms and diseases. Advances in scRNA-seq make it possible to study these Processes from cells at various developmental stages. Achieving accurate characterization is challenging, however, particularly for Periodic Processes, such as cell cycles. To address this, we developed Cyclum, a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Cyclum substantially improves the accuracy and robustness of cell-cycle characterization beyond existing approaches. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. Cyclum is available at https://github.com/KChen-lab/cyclum.

Fang Wang - One of the best experts on this subject based on the ideXlab platform.

  • latent Periodic Process inference from single cell rna seq data
    Nature Communications, 2020
    Co-Authors: Shaoheng Liang, Fang Wang, Ken Chen
    Abstract:

    The development of a phenotype in a multicellular organism often involves multiple, simultaneously occurring biological Processes. Advances in single-cell RNA-sequencing make it possible to infer latent developmental Processes from the transcriptomic profiles of cells at various developmental stages. Accurate characterization is challenging however, particularly for Periodic Processes such as cell cycle. To address this, we develop Cyclum, an autoencoder approach identifying circular trajectories in the gene expression space. Cyclum substantially improves the accuracy and robustness of cell-cycle characterization beyond existing approaches. Applying Cyclum to removing cell-cycle effects substantially improves delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. Traditional methods for determining cell type composition lack scalability, while single-cell technologies remain costly and noisy compared to bulk RNA-seq. Here, the authors present a highly efficient tool to measure cellular heterogeneity in bulk expression through robust integration of single-cell information.

  • latent Periodic Process inference from single cell rna seq data
    bioRxiv, 2019
    Co-Authors: Shaoheng Liang, Fang Wang, Ken Chen
    Abstract:

    Convoluted biological Processes underlie the development of multicellular organisms and diseases. Advances in scRNA-seq make it possible to study these Processes from cells at various developmental stages. Achieving accurate characterization is challenging, however, particularly for Periodic Processes, such as cell cycles. To address this, we developed Cyclum, a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Cyclum substantially improves the accuracy and robustness of cell-cycle characterization beyond existing approaches. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. Cyclum is available at https://github.com/KChen-lab/cyclum.

Diego Di Bernardo - One of the best experts on this subject based on the ideXlab platform.

  • Towards feedback control of the cell-cycle across a population of yeast cells
    2019 18th European Control Conference (ECC), 2019
    Co-Authors: Giansimone Perrino, Davide Fiore, Sara Napolitano, Mario Di Bernardo, Diego Di Bernardo
    Abstract:

    Cells are defined by their unique ability to self-replicate through cell division. This Periodic Process is known as the cell-cycle and it happens with a defined period in each cell. The budding yeast divides asymmetrically with a mother cell generating multiple daughter cells. Within the cell population each cell divides with the same period but asynchronously. Here, we investigate the problem of synchronising the cell-cycle across a population of yeast cells through a microfluidics-based feedback control platform. We propose a theoretical and experimental approach for cell-cycle control by considering a yeast strain that can be forced to start the cell-cycle by changing growth medium. The duration of the cell-cycle is strictly linked to the cell volume growth, hence a hard constraint in the controller design is to prevent excessive volume growth. We experimentally characterised the yeast strain and derived a simplified phase-oscillator model of the cell-cycle. We then designed and implemented three impulsive control strategies to achieve maximal synchronisation across the population and assessed their control performance by numerical simulations. The first two controllers are based on event-triggered strategies, while the third uses a model predictive control (MPC) algorithm to select the sequence of control impulses while satisfying built-in constraints on volume growth. We compared the three strategies by computing two cost functions: one quantifying the level of synchronisation across the cell population and the other volume growth during the Process. We demonstrated that the proposed control approaches can effectively achieve an acceptable trade-off between two conflicting control objectives: (i) obtaining maximal synchronisation of the cell cycle across the population while (ii) minimizing volume growth. The results can be used to implement effective strategies to unfold the biological mechanisms controlling cell cycle and volume growth in yeast cells.

  • Feedback control promotes synchronisation of the cell-cycle across a population of yeast cells
    2019 IEEE 58th Conference on Decision and Control (CDC), 2019
    Co-Authors: Giansimone Perrino, Davide Fiore, Sara Napolitano, Francesca Galdi, Antonella La Regina, Mario Di Bernardo, Diego Di Bernardo
    Abstract:

    The Periodic Process of cell replication by division, known as cell-cycle, is a natural phenomenon occurring asynchronously in any cell population. Here, we consider the problem of synchronising cell-cycles across a population of yeast cells grown in a microfluidics device. Cells were engineered to reset their cell-cycle in response to low methionine levels. Automated syringes enable changing methionine levels (control input) in the microfluidics device. However, the control input resets only those cells that are in a specific phase of the cell-cycle (G1 phase), while the others continue to cycle unperturbed. We devised a simplified dynamical model of the cell-cycle, inferred its parameters from experimental data and then designed two control strategies: (i) an open-loop controller based on the application of Periodic stimuli; (ii) a closed-loop model predictive controller (MPC) that selects the sequence of control stimuli which maximises a synchronisation index. Both the proposed control strategies were validated in-silico, together with experimental validation of the open-loop strategy.

A G Zhilkin - One of the best experts on this subject based on the ideXlab platform.

  • flow structure in magnetic close binary stars
    Physics-Uspekhi, 2012
    Co-Authors: A G Zhilkin, Dmitrii V Bisikalo, A A Boyarchuk
    Abstract:

    The current understanding of mass exchange Processes between close binary system (CBS) components is reviewed, with particular attention on the mass flow structure and accretion disk physics. Using 3D MHD calculation results, the variation of key accretion disk characteristics with the accretor magnetic field is studied and the magnetic field generation Process is analyzed. In particular, it is shown that the quasi-Periodic Process of toroidal magnetic field generation in disks results in alternating accretion and decretion regimes in the inner regions of the disk. By treating MHD flows in CBSs self-consistently, disk formation conditions are established and a separation criterion between intermediate-polar and polar flows is found. The possibility of using MHD simulation results for explaining observations is discussed.