Exploring Instructive Physiological Signaling with the Bioelectric Tissue Simulation Engine.
Exploring Instructive Physiological Signaling with the Bioelectric Tissue Simulation Engine.
复制标题
使用生物电组织仿真引擎探索指导性的生理信号传导。
DOI：
10.3389/fbioe.2016.00055
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发表时间：
2016
影响因子：
5.7
通讯作者：
Levin M
中科院分区：
文献类型：
--
作者：
Pietak A;Levin M
生物电细胞特性已被揭示为调节干细胞功能、再生反应、发育模式和肿瘤重编程的强大靶标。内源静息电位、离子流和电场的时空分布不仅受到基因组和外部信号的影响，还受到其自身内在动力学的影响。离子通道和电突触（间隙连接）都决定细胞静息电位，并且它们本身受细胞静息电位的控制。因此，多细胞组织中生物电模式的起源和进展是复杂的，这阻碍了生物医学干预中电压分布的合理控制。为了加深对这些动力学的理解并促进生物电模式控制策略的开发，我们开发了生物电组织模拟引擎（BETSE），这是一种有限体积方法多物理场模拟器，它通过对离子通道和间隙连接活动进行建模并跟踪离子浓度基本属性的变化来预测生物电模式及其时空动力学。我们通过将实验获得的膜渗透性、离子浓度和静息电位数据与模拟值进行匹配，并通过展示一系列众所周知的案例的预期结果来验证模拟器的性能，例如预测单细胞膜状态和环境离子浓度扰动的正确跨膜电压变化，以及真实的跨上皮电位和生物电损伤信号的发展。计算机实验揭示了影响跨膜电位的因素在具有紧密连接的间隙连接网络细胞簇中显着不同，并确定了能够产生强的、紧急的、簇范围的静息电位梯度的非线性反馈机制。 BETSE 平台将能够深入了解组织中的局部和远程生物电动力学，并协助开发特定的干预措施，以在形态发生和重塑过程中实现对模式的更好控制。
Bioelectric cell properties have been revealed as powerful targets for modulating stem cell function, regenerative response, developmental patterning, and tumor reprograming. Spatio-temporal distributions of endogenous resting potential, ion flows, and electric fields are influenced not only by the genome and external signals but also by their own intrinsic dynamics. Ion channels and electrical synapses (gap junctions) both determine, and are themselves gated by, cellular resting potential. Thus, the origin and progression of bioelectric patterns in multicellular tissues is complex, which hampers the rational control of voltage distributions for biomedical interventions. To improve understanding of these dynamics and facilitate the development of bioelectric pattern control strategies, we developed the BioElectric Tissue Simulation Engine (BETSE), a finite volume method multiphysics simulator, which predicts bioelectric patterns and their spatio-temporal dynamics by modeling ion channel and gap junction activity and tracking changes to the fundamental property of ion concentration. We validate performance of the simulator by matching experimentally obtained data on membrane permeability, ion concentration and resting potential to simulated values, and by demonstrating the expected outcomes for a range of well-known cases, such as predicting the correct transmembrane voltage changes for perturbation of single cell membrane states and environmental ion concentrations, in addition to the development of realistic transepithelial potentials and bioelectric wounding signals. In silico experiments reveal factors influencing transmembrane potential are significantly different in gap junction-networked cell clusters with tight junctions, and identify non-linear feedback mechanisms capable of generating strong, emergent, cluster-wide resting potential gradients. The BETSE platform will enable a deep understanding of local and long-range bioelectrical dynamics in tissues, and assist the development of specific interventions to achieve greater control of pattern during morphogenesis and remodeling.