Rethinking Causation for Data-intensive Biology: Constraints, Cancellations, and Quantized Organisms: Causality in complex organisms is sculpted by constraints rather than instigators, with outcomes perhaps better described by quantized patterns than rectilinear pathways

重新思考数据密集型生物学中的因果关系:约束、抵消和量化生物体:复杂生物体的因果关系是由约束而非引发因素塑造的,其结果或许用量化模式而非直线路径来描述更为恰当。

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Abstract

Complex organisms thwart the simple rectilinear causality paradigm of "necessary and sufficient," with its experimental strategy of "knock down and overexpress." This Essay organizes the eccentricities of biology into four categories that call for new mathematical approaches; recaps for the biologist the philosopher's recent refinements to the causation concept and the mathematician's computational tools that handle some but not all of the biological eccentricities; and describes overlooked insights that make causal properties of physical hierarchies such as emergence and downward causation straightforward. Reviewing and extrapolating from similar situations in physics, it is suggested that new mathematical tools for causation analysis incorporating feedback, signal cancellation, nonlinear dependencies, physical hierarchies, and fixed constraints rather than instigative changes will reveal unconventional biological behaviors. These include "eigenisms," organisms that are limited to quantized states; trajectories that steer a system such as an evolving species toward optimal states; and medical control via distributed "sheets" rather than single control points.

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