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Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D. and Kavukcuoglu, K. (2016) Asynchronous Methods for Deep Reinforcement Learning. International Conference on Machine Learning, 1928-1937.

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• TITLE: An Observation Data Driven Simulation and Analysis Framework for Early Stage C. elegans Embryogenesis

  AUTHORS: Dali Wang, Zi Wang, Xiaopeng Zhao, Yichi Xu, Zhirong Bao

  KEYWORDS: C. elegans Embryogenesis, Agent-Based Modeling, Deep Reinforcement Learning, Observation-Driven Modeling Framework, 3D Live Images

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.11 No.8, August 28, 2018

  ABSTRACT: Recent developments in cutting-edge live microscopy and image analysis provide a unique opportunity to systematically investigate individual cell’s dynamics as well as simulation-based hypothesis testing. After a summary of data generation and analysis in the observation and modeling efforts related to C. elegans embryogenesis, we develop a systematic approach to model the basic behaviors of individual cells. Next, we present our ideas to model cell fate, division, and movement using 3D time-lapse images within an agent-based modeling framework. Then, we summarize preliminary result and discuss efforts in cell fate, division, and movement modeling. Finally, we discuss the ongoing efforts and future directions for C. elegans embryo modeling, including an inferred developmental landscape for cell fate, a quasi-equilibrium model for cell division, and multi-agent, deep reinforcement learning for cell movement.