Mei Yongguo, Abedi Vida, Carbo Adria, Zhang Xiaoying, Lu Pinyi, Philipson Casandra, Hontecillas Raquel, Hoops Stefan, Liles Nathan, Bassaganya-Riera Josep
BMC Bioinformatics. 2015;16 Suppl 12(Suppl 12):S2. doi: 10.1186/1471-2105-16-S12-S2. Epub 2015 Aug 25.
Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation.
Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed.
We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation.Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.
计算技术正变得越来越强大，对生物系统建模工具的需求也越来越大。生物系统本质上是多尺度的，从分子到组织，从纳秒到数年或数十年的寿命。ENISI MSM整合了多种建模技术，以理解从细胞内信号通路到组织水平病变形成的免疫过程。本文研究并总结了ENISI从最初版本到最新前沿实现的技术细节。
采用面向对象编程方法，基于ENISI开发了一套工具。整合了多种建模技术，以可视化组织、细胞和蛋白质；此外，还解决了不同尺度之间的性能匹配问题。
我们使用ENISI MSM开发了肠道炎症期间黏膜免疫系统的预测性多尺度模型。我们的建模预测剖析了效应性CD4+ T细胞反应在免疫失调后导致肠道黏膜组织损伤的机制。计算建模技术在推动对生物过程的系统层面机制理解方面发挥着越来越重要的作用。计算机模拟指导并支持实验和临床工作。本研究介绍了肠道免疫模拟器（ENISI），这是一种用于模拟黏膜免疫反应的多尺度建模工具。ENISI的建模环境可以模拟从分子信号通路到组织水平事件（如组织病变形成）的计算机模拟实验。ENISI的架构整合了多种建模技术，包括基于主体的建模（ABM）、常微分方程（ODE）、随机建模方程（SDE）和偏微分方程（PDE）。本文重点关注ENISI的实现和发展挑战。开发了一个结肠炎症期间黏膜免疫反应的多尺度模型，包括CD4+ T细胞分化和组织水平的细胞间相互作用，以说明ENISI MSM的能力、威力和范围。
