ESTIMATES FOR APPROXIMATE SOLUTIONS TO A FUNCTIONAL DIFFERENTIAL EQUATION MODEL OF CELL DIVISION

Stephen Taylor, Xueshan Yang
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Abstract

Abstract The functional partial differential equation (FPDE) for cell division, $$ \begin{align*} &\frac{\partial}{\partial t}n(x,t) +\frac{\partial}{\partial x}(g(x,t)n(x,t))\\ &\quad = -(b(x,t)+\mu(x,t))n(x,t)+b(\alpha x,t)\alpha n(\alpha x,t)+b(\beta x,t)\beta n(\beta x,t), \end{align*} $$is not amenable to analytical solution techniques, despite being closely related to the first-order partial differential equation (PDE) $$ \begin{align*} \frac{\partial}{\partial t}n(x,t) +\frac{\partial}{\partial x}(g(x,t)n(x,t)) = -(b(x,t)+\mu(x,t))n(x,t)+F(x,t), \end{align*} $$which, with known $F(x,t)$, can be solved by the method of characteristics. The difficulty is due to the advanced functional terms $n(\alpha x,t)$ and $n(\beta x,t)$, where $\beta \ge 2 \ge \alpha \ge 1$, which arise because cells of size x are created when cells of size $\alpha x$ and $\beta x$ divide. The nonnegative function, $n(x,t)$, denotes the density of cells at time t with respect to cell size x. The functions $g(x,t)$, $b(x,t)$ and $\mu (x,t)$ are, respectively, the growth rate, splitting rate and death rate of cells of size x. The total number of cells, $\int _{0}^{\infty }n(x,t)\,dx$, coincides with the $L^1$ norm of n. The goal of this paper is to find estimates in $L^1$ (and, with some restrictions, $L^p$ for $p>1$) for a sequence of approximate solutions to the FPDE that are generated by solving the first-order PDE. Our goal is to provide a framework for the analysis and computation of such FPDEs, and we give examples of such computations at the end of the paper.
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细胞分裂的泛函微分方程模型的近似解的估计
细胞分裂的泛函偏微分方程$$ \begin{align*} &\frac{\partial}{\partial t}n(x,t) +\frac{\partial}{\partial x}(g(x,t)n(x,t))\\ &\quad = -(b(x,t)+\mu(x,t))n(x,t)+b(\alpha x,t)\alpha n(\alpha x,t)+b(\beta x,t)\beta n(\beta x,t), \end{align*} $$与一阶偏微分方程$$ \begin{align*} \frac{\partial}{\partial t}n(x,t) +\frac{\partial}{\partial x}(g(x,t)n(x,t)) = -(b(x,t)+\mu(x,t))n(x,t)+F(x,t), \end{align*} $$密切相关,而一阶偏微分方程在已知$F(x,t)$的情况下可以用特征法求解,但不适合用解析解技术求解。困难在于高级函数项$n(\alpha x,t)$和$n(\beta x,t)$,其中$\beta \ge 2 \ge \alpha \ge 1$的出现是因为大小为x的单元格是在大小为$\alpha x$和$\beta x$的单元格分裂时产生的。非负函数$n(x,t)$表示t时刻细胞密度相对于细胞大小x。函数$g(x,t)$、$b(x,t)$和$\mu (x,t)$分别是细胞大小x的生长率、分裂率和死亡率。细胞总数$\int _{0}^{\infty }n(x,t)\,dx$与n的$L^1$范数一致。本文的目标是在$L^1$(并且,在一些限制下,$L^p$为$p>1$),用于求解一阶PDE生成的FPDE的近似解序列。我们的目标是为这种FPDEs的分析和计算提供一个框架,并在论文的最后给出了这种计算的例子。
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