magnetophoretic separation model. For mathematical convenience, we used radii of the magnetic beads ( r b = d b 2 −1 ) instead of diameter in modeling equations. To explain the binding effi ciency of beads to bacteria f ( r b ), we employed a colloidal model of collisions (perikinetic) due to Brownian motion. [ 3,7 ] We can safely neglect a colloidal model (orthokinetic) of collisions driven by gravity because of the extremely small size of the beads, ranging from nanometer to micrometer scales. [ 3,8 ] To simplify the binding reaction between the bead and the bacteria, we assumed that this binding reaction is a pseudo-fi rst-order reaction, where most of magnetic beads are free in solution and their concentration does not change as bacteria are bound with the beads. [ 8,15 ] Because cell surface proteins bind to a ligand rapidly (within milliseconds), [ 16,17 ] we assumed that the binding reaction is determined by the rate of collision between bacteria and magnetic beads, not by the kinetics of lectin binding to carbohydrate ligands on cell surface. In the model, we assumed that the surface density of the FcMBL protein on the beads is constant, and the binding of the beads to bacteria is not reversible. [ 3,7,8 ] For modeling the magnetic separation effi ciency g ( r b ), we employed a model based on the geometry of the system and the magnetic forces applied on the bead–bacteria complex. We reasoned that the total magnetic separation effi ciency h ( r b ) would theoretically be proportional to the product of these two modeled processes: h ( r b ) ∝ f ( r b ) g ( r b ). Below, we described our models for the functions f ( r b ) and g ( r b ), and also included remarks about the experiment protocols that relate to the modeling. Among the various parameters affecting the magnetic separation effi ciency, we parameterized the effi ciency in a function of magnetic bead sizes, and then used this model to theoretically determine the optimal size of magnetic beads that should yield the most effi cient magnetic separation in our experimental condition.
磁泳分离模型。为了数学上的方便,我们在建模方程中使用了磁珠的半径(rb = db 2 -1 )而不是直径。为了解释珠子与细菌 f ( rb ) 的结合效率,我们采用了由于布朗运动引起的碰撞胶体模型 (periki?netic)。[ 3,7 ] 我们可以安全地忽略重力驱动碰撞的胶体模型(正交运动),因为珠子的尺寸非常小,范围从纳米到微米尺度。[ 3,8 ] 为了简化磁珠和细菌之间的结合反应,我们假设这种结合反应是一个伪一级反应,其中大部分磁珠在溶液中是游离的,并且它们的浓度不会改变因为细菌与珠子结合。[ 8, 15 ] 因为细胞表面蛋白与配体快速结合(在几毫秒内),[ 16,17 ] 我们假设结合反应是由细菌和磁珠之间的碰撞速率决定的,而不是由凝集素的动力学决定的与细胞表面的碳水化合物配体结合。在模型中,我们假设珠子上 FcMBL 蛋白的表面密度是恒定的,并且珠子与细菌的结合是不可逆的。[ 3,7,8 ] 为了对磁分离效率 g ( rb ) 进行建模,我们采用了一个基于系统几何形状和施加在珠-细菌复合体上的磁力的模型。我们推断总磁分离效率 h ( rb ) 理论上与这两个建模过程的乘积成正比: h ( rb ) ∝ f ( rb ) g ( rb )。以下,我们描述了函数 f ( rb ) 和 g ( rb ) 的模型,还包括对与建模相关的实验协议的评论。在影响磁分离效率的各种参数中,我们将效率参数化为磁珠尺寸的函数,然后使用该模型从理论上确定磁珠的最佳尺寸,该尺寸应在我们的实验条件。
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磁泳分离模型。为了便于数学计算,我们使用了磁珠的半径(Rb=Db2)−1)代替建模方程中的直径。为了解释珠子与细菌f(RB)的结合效率,我们采用了布朗运动引起的胶体碰撞模型(periki netic)[3,7]我们可以安全地忽略重力驱动碰撞的胶体模型(正动力学),因为珠子的尺寸非常小,从纳米到微米不等。[3,8]为了简化磁珠与细菌之间的结合反应,我们假设该结合反应为伪一级反应,其中大多数磁珠在溶液中是游离的,且其浓度不会随着细菌与磁珠结合而改变。[8,15]因为细胞表面蛋白质与配体rap(在毫秒内)结合[16,17],我们假设结合反应是由bac teria和磁珠之间的碰撞速率决定的,而不是由凝集素与细胞表面碳水化合物配体结合的动力学决定的。在该模型中,我们假设微珠上FcMBL蛋白质的表面密度是恒定的,并且微珠与bac teria的结合是不可逆的。[3,7,8]为了对磁选效率g(rb)进行建模,我们采用了一个基于系统几何结构和施加在珠-细菌复合体上的磁力的模型。我们推断,总磁选效率h(rb)理论上与这两个模拟过程的乘积成正比:h(rb)∝ f(rb)g(rb)。下面,我们描述了函数f(rb)和g(rb)的模型,还包括与建模相关的实验协议的备注。在影响磁选效率的各种参数中,我们将磁选效率参数化为磁珠尺寸的函数,然后使用该模型从理论上确定在我们的实验条件下应产生最有效磁选的磁珠的最佳尺寸。<br>
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