Allen Discovery Center at Tufts University , Medford, MA , USA.
Front Bioeng Biotechnol. 2016 Jul 6;4:55. doi: 10.3389/fbioe.2016.00055. eCollection 2016.
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.
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有力靶点。内源性静息电位、离子流和电场的时空分布不仅受基因组和外部信号的影响，还受其自身固有动力学的影响。离子通道和电突触（缝隙连接）都决定了细胞的静息电位，并且自身也由其决定。因此，多细胞组织中生物电模式的起源和发展是复杂的，这阻碍了对生物医学干预中电压分布的合理控制。为了提高对这些动力学的理解并促进生物电模式控制策略的发展，我们开发了生物电组织模拟引擎（BETSE），这是一种有限体积法多物理场模拟器，通过模拟离子通道和缝隙连接活动以及跟踪离子浓度基本性质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通透性、离子浓度和静息电位数据与模拟值匹配，验证了模拟器的性能，并通过演示一系列众所周知的案例的预期结果，例如预测单个细胞膜状态和环境离子浓度扰动时的正确跨膜电压变化，以及开发现实的跨上皮电位和生物电损伤信号，证明了该模拟器的性能。计算机模拟实验表明，在具有紧密连接的缝隙连接网络细胞簇中，影响跨膜电位的因素有显著差异，并确定了能够产生强、突发、全簇静息电位梯度的非线性反馈机制。BETSE 平台将使人们深入了解组织中的局部和远程生物电动力学，并协助开发特定干预措施，以在形态发生和重塑过程中实现对模式的更大控制。
