Department of Software Technologies, Faculty of Management Science and Informatics, University of žilina, Cell-in-fluid Research Group, žilina, Slovakia.
Department of Technical Cybernetics, Faculty of Management Science and Informatics, University of žilina, žilina, Slovakia.
BMC Bioinformatics. 2020 Mar 11;21(Suppl 2):90. doi: 10.1186/s12859-020-3357-5.
For optimization of microfluidic devices for the analysis of blood samples, it is useful to simulate blood cells as elastic objects in flow of blood plasma. In such numerical models, we primarily need to take into consideration the movement and behavior of the dominant component of the blood, the red blood cells. This can be done quite precisely in small channels and within a short timeframe. However, larger volumes or timescales require different approaches. Instead of simplifying the simulation, we use a neural network to predict the movement of the red blood cells.
The neural network uses data from the numerical simulation for learning, however, the simulation needs only be run once. Alternatively, the data could come from video processing of a recording of a biological experiment. Afterwards, the network is able to predict the movement of the red blood cells because it is a system of bases that gives an approximate cell velocity at each point of the simulation channel as a linear combination of bases.In a simple box geometry, the neural network gives results comparable to predictions using fluid streamlines, however in a channel with obstacles forming slits, the neural network is about five times more accurate.The network can also be used as a discriminator between different situations. We observe about two-fold increase in mean relative error when a network trained on one geometry is used to predict trajectories in a modified geometry. Even larger increase was observed when it was used to predict trajectories of cells with different elastic properties.
While for uncomplicated box channels there is no advantage in using a system of bases instead of a simple prediction using fluid streamlines, in a more complicated geometry, the neural network is significantly more accurate. Another application of this system of bases is using it as a comparison tool for different modeled situations. This has a significant future potential when applied to processing data from videos of microfluidic flows.
为了优化用于分析血样的微流控设备，在模拟血流中的弹性血细胞时，将其视为血浆中的弹性物体是很有用的。在这种数值模型中，我们主要需要考虑血液的主要成分，即红细胞的运动和行为。在小通道和短时间内可以非常精确地做到这一点。然而，对于较大的体积或时间范围，则需要采用不同的方法。我们不是简化模拟，而是使用神经网络来预测红细胞的运动。
神经网络使用数值模拟的数据进行学习，但是，模拟只需运行一次。或者，数据可以来自生物实验记录的视频处理。之后，该网络能够预测红细胞的运动，因为它是一个基系统，它将模拟通道中每个点的细胞速度作为基的线性组合给出近似值。在简单的盒形几何形状中，神经网络给出的结果可与使用流线的预测相媲美，但是在具有形成狭缝的障碍物的通道中，神经网络的准确性要高出约五倍。该网络还可以用作不同情况之间的鉴别器。当将在一种几何形状上训练的网络用于预测在修改后的几何形状中的轨迹时，我们观察到平均相对误差增加了一倍。当将其用于预测具有不同弹性特性的细胞的轨迹时，观察到的增加更大。
虽然对于不复杂的盒形通道，使用基系统而不是使用流体流线进行简单预测没有优势，但是在更复杂的几何形状中，神经网络的准确性要高得多。该基系统的另一个应用是将其用作不同建模情况的比较工具。当将其应用于处理微流控流视频数据时，它具有重要的未来潜力。
