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生物形态动力学中随机和动态图文法规则算子结构交换关系的显式计算

Explicit Calculation of Structural Commutation Relations for Stochastic and Dynamical Graph Grammar Rule Operators in Biological Morphodynamics.

作者信息

Mjolsness Eric

机构信息

Departments of Computer Science and Mathematics, University of California, Irvine, CA, United States.

出版信息

Front Syst Biol. 2022 Sep;2. doi: 10.3389/fsysb.2022.898858. Epub 2022 Sep 9.

Abstract

Many emergent, non-fundamental models of complex systems can be described naturally by the temporal evolution of spatial structures with some nontrivial discretized topology, such as a graph with suitable parameter vectors labeling its vertices. For example, the cytoskeleton of a single cell, such as the cortical microtubule network in a plant cell or the actin filaments in a synapse, comprises many interconnected polymers whose topology is naturally graph-like and dynamic. The same can be said for cells connected dynamically in a developing tissue. There is a mathematical framework suitable for expressing such emergent dynamics, "stochastic parameterized graph grammars," composed of a collection of the graph- and parameter-altering rules, each of which has a time-evolution operator that suitably moves probability. These rule-level operators form an operator algebra, much like particle creation/annihilation operators or Lie group generators. Here, we present an explicit and constructive calculation, in terms of elementary basis operators and standard component notation, of what turns out to be a general combinatorial expression for the operator algebra that reduces products and, therefore, commutators of graph grammar rule operators to equivalent integer-weighted sums of such operators. We show how these results extend to "dynamical graph grammars," which include rules that bear local differential equation dynamics for some continuous-valued parameters. Commutators of such time-evolution operators have analytic uses, including deriving efficient simulation algorithms and approximations and estimating their errors. The resulting formalism is complementary to spatial models in the form of partial differential equations or stochastic reaction-diffusion processes. We discuss the potential application of this framework to the remodeling dynamics of the microtubule cytoskeleton in cortical microtubule networks relevant to plant development and of the actin cytoskeleton in, for example, a growing or shrinking synaptic spine head. Both cytoskeletal systems underlie biological morphodynamics.

摘要

许多复杂系统的涌现性、非基本模型可以通过具有某些非平凡离散拓扑结构的空间结构的时间演化自然地描述,例如带有合适参数向量标记其顶点的图。例如,单个细胞的细胞骨架,如植物细胞中的皮质微管网络或突触中的肌动蛋白丝,由许多相互连接的聚合物组成,其拓扑结构自然呈类图状且动态变化。对于发育组织中动态连接的细胞也是如此。存在一个适合表达这种涌现动力学的数学框架,即“随机参数化图文法”,它由一组改变图和参数的规则组成,每个规则都有一个时间演化算子,该算子适当地移动概率。这些规则级算子形成一个算子代数,很像粒子产生/湮灭算子或李群生成元。在这里,我们根据基本基算子和标准分量表示法,给出了一个明确的构造性计算,结果表明这是一个算子代数的一般组合表达式,它将图文法规则算子的乘积以及因此的对易子简化为这些算子的等效整数加权和。我们展示了这些结果如何扩展到“动态图文法”,其中包括对一些连续值参数带有局部微分方程动力学的规则。这种时间演化算子的对易子有解析用途,包括推导高效的模拟算法和近似方法以及估计它们的误差。所得形式体系与偏微分方程或随机反应扩散过程形式的空间模型互补。我们讨论了这个框架在与植物发育相关的皮质微管网络中微管细胞骨架的重塑动力学以及例如生长或收缩的突触棘头中的肌动蛋白细胞骨架的潜在应用。这两种细胞骨架系统都是生物形态动力学的基础。

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