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Predictive Power of “A Minima” Models in Biology

机译:生物学中“最小”模型的预测能力

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摘要

Many apparently complex mechanisms in biology, especially in embryology and molecular biology, can be explained easily by reasoning at the level of the “efficient cause” of the observed phenomenology: the mechanism can then be explained by a simple geometrical argument or a variational principle, leading to the solution of an optimization problem, for example, via the co-existence of a minimization and a maximization problem (a min–max principle). Passing from a microscopic (or cellular) level (optimal min–max solution of the simple mechanistic system) to the macroscopic level often involves an averaging effect (linked to the repetition of a large number of such microscopic systems with possible random choice of the parameters of each of them) that gives birth to a global functional feature (e.g. at the tissue level). We will illustrate these general principles by building in four different domains of application “a minima” models and showing the main properties of their solutions: (1) extraction of a minimal RNA structure functioning as the first “peptidic machine,” a kind of ancestral ribosome; (2) study of a genetic regulatory network of Drosophila centred on Engrailed gene and expressing successively two genes inside a limit cycle; (3) study of a genetic network regulating neural activity and proliferation in mammals; and (4) study of a simple geometric model of epiboly in zebrafish.
机译:通过在观察现象学的“有效原因”层面上进行推理,可以轻松地解释生物学中许多明显复杂的机制,尤其是胚胎学和分子生物学中的机制:然后可以通过简单的几何论证或变分原理来解释该机制,例如,通过最小化和最大化问题(最小-最大原理)的并存,导致解决优化问题。从微观(或细胞)水平(简单机械系统的最佳最小-最大解)传递到宏观水平,通常涉及平均效应(与大量此类微观系统的重复以及可能的参数随机选择有关)其中的每一个)都具有全局功能(例如在组织级别)。我们将通过在四个不同的应用领域中构建“最小”模型并显示其解决方案的主要特性来说明这些一般原理:(1)提取起第一个“肽机器”作用的最小RNA结构,这是一种祖先核糖体(2)研究果蝇的遗传调控网络,该网络以Engrailed基因为中心,并在极限循环内连续表达两个基因。 (3)研究调节哺乳动物神经活动和增殖的遗传网络; (4)研究斑马鱼表皮的简单几何模型。

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