Tackling biological context, one network at a time: Examples from network biology and network pharmacology
神元 健児 博士
大阪大学微生物病研究所
https://www.biken.osaka-u.ac.jp/laboratories/detail/72
【概要】
Recent technological advances in single-cell sequencing enable the acquisition of multi-
dimensional data in a high-throughput manner. These technologies reveal the existence of heterogeneity
and the diversity of cell states and identities. To dissect the regulatory mechanisms underlying such
phenomena, many computational methods to infer Gene regulatory Networks (GRNs) have been
proposed. However, understanding biological events from a GRN perspective remains difficult. Even if a
computational algorithm can infer GRN models, the biological network is so complex that it is challenging
to understand how it systematically dictates cell identities. There is significant demand for new methodologies that bridge the gap between cellular phenotypes and the underlying GRN model. Thus, we have developed a new computational method, CellOracle, for the inference and analysis of GRNs.
CellOracle first infers sample-specific GRN configurations from single-cell RNA-seq and ATAC-seq data by
utilizing machine learning algorithms and genetic information. Our GRN models are designed to be used
for the simulation of cell identity changes in response to gene perturbation. This simulation enables
network configurations to be interrogated in relation to cell-fate regulation, facilitating their
interpretation. We validate the efficacy of CellOracle to recapitulate known outcomes of well-characterized gene perturbations in developmental processes, including mouse and human hematopoiesis. We also apply CellOracle to zebrafish embryogenesis, systematically perturbing transcription factors and experimentally validating key candidates, identifying a novel mechanism that
regulates cell identity in axial mesoderm development. Our validation results demonstrate the efficacy of
our new approach to infer and interpret the dynamics of GRN configurations, promoting new mechanistic
insights into the regulation of cell identity.
References:
- Kamimoto, K et al., Dissecting cell identity via network inference and in silico gene perturbation.
Nature, 2023 - Kamimoto, K et al., Gene regulatory network reconfiguration in direct lineage reprogramming. Stem
Cell Reports, 2023
日時： 2025年3月4日（水） 15:00～17:30
場所： Zoom
連絡先： 理学系研究科 生物科学専攻 生物情報科学科
黒田 真也（skuroda AT bs.s.u-tokyo.ac.jp）
参加希望の方は
info.kuroda-lab [at] bs.s.u-tokyo.ac.jp
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