Andrei Khrennikov , Satoshi Iryama , Irina Basieva , Keiko Sato
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引用次数: 0
摘要
教科书中关于表型产生的概念,即 "基因型(G)+环境(E)+基因型与环境相互作用(GE)↦表型(Ph)",是用开放量子系统理论(OQST)或更广义的自适应动力学理论(ADT)来建模的。该模型是类量子模型,即与生物系统中的量子物理过程无关。一般来说,这种建模是关于量子形式主义和方法论在物理学之外的应用。宏观生物系统,在我们的例子中是基因型和表型,被视为信息处理器,其运作符合量子信息论的规律。表型是量子主方程戈里尼-科萨科夫斯基-苏达山-林德布拉德方程(GKSL)所描述的电子适应过程的输出。其静止状态与表型相对应。我们强调了一类以(冯-诺依曼)熵的骆驼状图为特征的 GKSL 动力学:在 E 适应过程中,表型的状态熵(无序)先是增加,然后下降--一个稳定而有序的表型就产生了。生物体的表型特征--性状,是量子测量理论中的模型,一般是由正算子有值量度(POVMs)给出的非锐利观测值。本文也是对量子信息生物学方法和数学装置的综述。
Quantum-like environment adaptive model for creation of phenotype
The textbook conceptualization of phenotype creation, “genotype (G) + environment (E) + genotype & environment interactions (GE) phenotype (Ph)”, is modeled with open quantum systems theory (OQST) or more generally with adaptive dynamics theory (ADT). The model is quantum-like, i.e., it is not about quantum physical processes in biosystems. Generally such modeling is about applications of the quantum formalism and methodology outside of physics. Macroscopic biosystems, in our case genotypes and phenotypes, are treated as information processors which functioning matches the laws of quantum information theory. Phenotypes are the outputs of the -adaptation processes described by the quantum master equation, Gorini–Kossakowski–Sudarshan–Lindblad equation (GKSL). Its stationary states correspond to phenotypes. We highlight the class of GKSL dynamics characterized by the camel-like graphs of (von Neumann) entropy: in the process of -adaptation phenotype’s state entropy (disorder) first increases and then falls down — a stable and well-ordered phenotype is created. Traits, an organism’s phenotypic characteristics, are modeled within the quantum measurement theory, as generally unsharp observables given by positive operator valued measures (POVMs. This paper is also a review on the methods and mathematical apparatus of quantum information biology.
期刊介绍:
BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.