Abstract : Systems biology approaches that integrate heterogeneous biological data in quantitative mathematical models are expected to facilitate a comprehensive understanding of complex biological systems. This A3 (China-Japan-Korea) mini-symposium will bring together Asian mathematicians working in the field of mathematical modeling and data analysis to share their cutting-edge research results on dynamic phenomena at all levels from molecular and cellular to population.
Organizer(s) : Jae Kyoung Kim, Sungrim Seirin-Lee, Lei Zhang
Jayson Cortez (University of Philippines at Los Banos)
Amanda Alexander (University of Houston)
Charilaos Giannitsis (Rice University)
Oleg Igoshin (Rice University)
Abstract : Plasmids, extrachromosomal DNA elements, are found in most bacteria and confer
benefits to their hosts. Most models suggest that plasmids are lost in
barring strong selection. The ubiquity of plasmids thus presents a
paradox. We developed a mathematical model of ColE1 plasmid copy number
based on experimental findings. This allows us to relate the probability of plasmid loss to
properties of the population and provide testable predictions about conditions under which
plasmids are lost.
[00813] Morphology of organoids using a multicellular phase-field model
Author(s) :
Sakurako Tanida (The University of Tokyo)
Kana Fuji (The University of Tokyo)
Tetsuya Hiraiwa (The University of Tokyo, National University of Singapore)
Makiko Nonomura (Nihon University)
Masaki Sano (The University of Tokyo, Shanghai Jiaotong University)
Abstract : Organoids are self-organizing cells grown from stem cells in vitro. The organoid morphology is affected not only by genes but also by mechanical constrained due to the geometrical requirements to maintain the cell cluster. In this study, using a multicellular phase-field model, we examined the morphology when changing luminal fluid pressure and the minimum time of the cell cycle. Classifying the patterns by several indices, we discuss the mechanisms which generate the different patterns.
[00720] Mind the gap:The extra-embryonic space is crucial geometric constraints regulating cell arrangement.
Author(s) :
Sungrim Seirin-Lee (Kyoto University)
Kazunori Yamamoto (Kanagawa Institute of Technology)
Akatsuki Kimura (National Institute of Genetics)
Abstract : Imagine sitting at a meeting where the shape of the table and your place at it might impact how you get along with the other members. In multicellular systems, cells also communicate with adjacent cells to decide their positions and fates. Cellular arrangement in space is thus important for development. Orientation of cell division, cell-cell interaction, and geometric constraints are the three major factors that define cell arrangement. In particular, the details of geometric constraints are difficult to be revealed only in experiments and the contribution of local contour has been remained elusive. Here we developed a multicellular morphology model based on the phase-field method so that we can incorporate precise geometric constrains. We applied the model to examine cell arrangement in the 4-cell stage embryo of nematodes, and succeeded in reproducing cell arrangements observed in vivo, including an arrangement which has not been explained before. Our cell morphology model predicted that the amount of extra-embryonic space (ES), the empty space within the eggshell not occupied by embryonic cells, affects cell arrangement in a manner dependent on the local contour and the aspect ratio of the eggshell as well as the strength of cell adhesion. The prediction was validated experimentally as increasing the (ES) did change the cell arrangement in the Cenorhabditis elegans embryo. Overall, our analyses characterized the roles of new geometrical contributors, namely the amount of (ES) and the local contour, to cell arrangements. These factors should be considered in all multicellular systems, including human being.
Reference
S. Seirin-Lee*, K. Yamamoto, A. Kimura, The extra-embryonic space and the local contour are critical geometric constraints regulating cell arrangement (2022) Development. 149, dev200401.
[03207] Test three different models for the Chlamydia developmental cycle with intrinsic noise
Author(s) :
Jinsu Kim (POSTECH)
Abstract : Chlamydia is an intracellular bacterium that reproduces via an unusual developmental cycle such as late RB-EB conversion and heterogeneity of individual Chlamydia size. A key step is a conversion from a replicating form (RB) to an infectious form (EB), which occurs in a delayed and asynchronous manner. The regulatory mechanisms that control this developmental switch are unknown, but could potentially include extrinsic signals from the host cell or from other chlamydiae, or an intrinsic signal such as chlamydial cell size. In this presentation, we introduce three stochastic models, each based on a different regulatory mechanism. To test the models, we use the intrinsic noise of each model that can be estimated with statistical quantities measured experimentally. We found that all three models successfully reproduced the observed timing of RB-to-EB conversion and the growth curves of the developmental forms within an inclusion. However, only one model, based on the regulation of RB-to-EB conversion by RB size, was able to produce the positive correlation between the number of RBs and EBs and the monotonic time evolution of the coefficient of variation in the RB population.
[03206] Network design principle for biological dual functions
Author(s) :
Lei Zhang (Peking University)
Abstract : Biological systems are capable of performing complex functions with a remarkable degree of accuracy, reliability, and robustness. We postulate that behind the celebrated diversity of the biological world lie “universal” principles that emerge at various levels of organization. For example, many signaling systems execute adaptation under noisy circumstances, and transcriptional regulatory networks can robustly achieve accurate oscillation in the presence of biological noise. In this talk, we will explore two dual functions: one is adaptation and noise attenuation, and the other one is oscillation and noise attenuation. By analyzing and computing three-node or four-node networks, we reveal essential network design principles for biological dual functions, which can be utilized in synthetic biology.
[03385] Density Physics-Informed Neural Network infers an arbitrary density distribution for non-Markovian system
Author(s) :
Hyeontae Jo (Institute for Basic Science)
Hyukpyo Hong (KAIST)
Hyung Ju Hwang (Pohang University of Science and Technology)
Won Chang (University of Cincinnati)
Jae Kyoung Kim (KAIST)
Abstract : In this talk, we developed Density-PINN (Physics-Informed Neural Networks), a method capable of estimating the probability density function embedded within a differential equation. While conventional PINNs have focused on determining the solutions or parameters of differential equations that can explain observed data, we introduce a specialized approach for estimating the probability density function contained within the equation. Specifically, when dealing with a limited number of stochastic time series as observed data, and where only the average of the data satisfies the solution of the differential equation, we have constructed a mean-generating model using Variational Autoencoders. By applying our method to single-cell gene expression data from 16 promoters in response to antibiotic stress, we discovered that promoters with slower signaling initiation and transduction exhibit greater cell-to-cell heterogeneity in response intensity.
[02263] Integrating different layers of biological data to enhance prediction
Author(s) :
Suoqin Jin (Wuhan University)
Abstract : The rapid advances of single-cell technologies have been attracting more attention. Recently we made some efforts to enhance biological prediction and discovery from single-cell RNA sequencing. By integrating single-cell RNA-seq with single-cell epigenomic data, bulk data or prior knowledge, we were able to dissect cellular heterogeneity and communication more comprehensively, and prioritize clinically-relevant cell subsets and prognostic signatures, which cannot be fully explained by single-cell genomics only, and highlights the valuable role of data integration.
[03677] Physics of Furrow Ingression in C. elegans Zygote
Author(s) :
Masatoshi Nishikawa (Hosei University)
Abstract : Cleavage furrow ingression is asymmetric in the first cleavage of Caenorhabditis elegans zygote. The asymmetric ingression gives rise to the symmetry breaking in terms of dorsal-ventral axis establishment, but its underlying mechanisms are largely unexplored. We will demonstrate that the distribution of cortical tension generator in the contractile ring becomes asymmetric as the curvature change at the ingression site and cortical flow toward the ring, suggesting the feedback between cell shape, contractility and flow.
[01465] Screening cell-cell communication in spatial transcriptomics via collective optimal transport
Author(s) :
Yanxiang Zhao (George Washington University)
Abstract : Spatial transcriptomic technologies and spatially annotated single cell RNA-sequencing (scRNA-seq) datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). How to incorporate the spatial constraints and other physical processes when inferring CCC computationally remains a major challenge. Here we present COMMOT to infer CCC in spatial transcriptomics accounting for the competition among different ligand and receptor species and cells or spots, and enforcing spatial constraints. A novel collective optimal transport method is developed to handle these complex interactions and constraints. Further downstream analysis tools on spatial signaling directions and signaling-regulated genes are then developed using machine learning models. We validate the method with simulation data and one spatially annotated scRNA-seq dataset. We show that COMMOT effectively infers spatial CCC using datasets by three popular spatial transcriptomic technologies. Finally, COMMOT reveals connections between CCC and skin development in a case study of human epidermal development. The method will have broad application in uncovering ligand-receptor mediated CCC using spatial genomics datasets.
[04188] A Novel Tool for Enhanced Single-cell RNA Sequencing Data Preprocessing and Dimensionality Reduction
Author(s) :
Hyun Kim (Institute for basic science)
JaeKyoung Kim (Korea Advanced Institute of Science and Technology, Institute for basic science)
Jong-Eun Park (Korea Advanced Institute of Science and Technology)
Minseok Seo (Korea University)
Abstract : Single-cell RNA sequencing (scRNA-seq) has revolutionized various cellular research applications, including cellular phenotyping and gene regulatory network reconstruction. However, data analysis remains challenging due to sparsity, high dimensionality, bias, skewed data distribution, and technological noise. In addition, conventional preprocessing methods, such as log-normalization and user-driven dimensionality reduction techniques, often introduce subjectivity and signal distortion, leading to decreased data dimension accuracy. To address these limitations, we developed a novel tool that effectively filters out data noise and corrects signal distortion during preprocessing. This approach significantly improves the accuracy of dimensionality reduction and overcomes the drawbacks associated with current methodologies. Our solution was tested on 53 real and simulated datasets and demonstrated superior performance compared to ten widely-used tools, including Seurat, Scanpy, and Monocle3. The enhanced performance of our tool offers promise for advancing scRNA-seq data analysis and facilitating more accurate downstream analyses.
[00602] Adaptive immune discrimination of antigen risks by predictive coding
Author(s) :
Kana Yoshido (Graduate School of Biostudies, Kyoto University)
Honda Naoki (Graduate School of Integrated Sciences for Life, Hiroshima University)
Abstract : Immune system induces appropriate responses depending on the risk of antigens: Strong responses to harmful antigens and weak or no responses to harmless antigens. To reveal the mechanism, we modeled T cell population dynamics with memory formation based on predictive coding. By the simulation, we found antigen concentration- and input rapidness- dependent discrimination between harmful and harmless antigens. Furthermore, we reproduced temporal change of discrimination as seen in the onset and therapy of allergy.
