Puzzles in modern biology. V. Why are genomes overwired?
Puzzles in modern biology. V. Why are genomes overwired?
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现代生物学中的难题。
作者：
S. Frank
影响真核基因表达的因素很多。转录因子、组蛋白编码、DNA折叠和非编码RNA调节表达。这些因素在广泛联系的大型监管控制网络中相互作用。遵循经典控制论原理的工程师将设计出一个更简单的监管网络。为什么基因组会过度连接？中立性或增强的稳健性可能会导致额外因素的积累，从而使网络架构复杂化。动力学的发展就像棘轮一样。新的因素被添加进来。基因组适应了额外的复杂性。如果没有显著的适应性损失，新增加的因素就不能再被移除。或者，高度连接的基因组可能更具可塑性。在大型网络中，大多数基因组变异对基因表达和特征值的影响相对较小。许多小的影响导致了一个平滑的梯度，在这个梯度中，特征可能会随着潜在的监管变化而稳定地变化。平滑的梯度可以提供从起始点到性能最高峰值的连续路径。提高绩效的潜在途径可以促进适应性和学习。基因组通过自然选择的归纳过程获得收益，这是一种反复尝试的学习算法，它发现了适应环境挑战的一般解决方案。同样，深度和密集连接的计算网络通过各种归纳试错学习过程获得收益，在这些过程中，网络学习以减少顺序试验中的错误。过接线通过平滑沿感应路径的梯度来改变感应的几何形状，从而提高性能。归纳的这些优势既适用于自然生物网络，也适用于人工深度学习网络。
Many factors affect eukaryotic gene expression. Transcription factors, histone codes, DNA folding, and noncoding RNA modulate expression. Those factors interact in large, broadly connected regulatory control networks. An engineer following classical principles of control theory would design a simpler regulatory network. Why are genomes overwired? Neutrality or enhanced robustness may lead to the accumulation of additional factors that complicate network architecture. Dynamics progresses like a ratchet. New factors get added. Genomes adapt to the additional complexity. The newly added factors can no longer be removed without significant loss of fitness. Alternatively, highly wired genomes may be more malleable. In large networks, most genomic variants tend to have a relatively small effect on gene expression and trait values. Many small effects lead to a smooth gradient, in which traits may change steadily with respect to underlying regulatory changes. A smooth gradient may provide a continuous path from a starting point up to the highest peak of performance. A potential path of increasing performance promotes adaptability and learning. Genomes gain by the inductive process of natural selection, a trial and error learning algorithm that discovers general solutions for adapting to environmental challenge. Similarly, deeply and densely connected computational networks gain by various inductive trial and error learning procedures, in which the networks learn to reduce the errors in sequential trials. Overwiring alters the geometry of induction by smoothing the gradient along the inductive pathways of improving performance. Those overwiring benefits for induction apply to both natural biological networks and artificial deep learning networks.