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Design principles and mechanistic explanation.

Wei Fang1

  • 1Research Center for Philosophy of Science and Technology, Shanxi University, Taiyuan, Shanxi, China. wesleyfang@outlook.com.

History and Philosophy of the Life Sciences
|November 3, 2022
PubMed
Summary
This summary is machine-generated.

Design principles in systems biology and neuroscience offer abstract mechanisms, aiding explanation. These principles are general and often causal, supporting mechanistic understanding.

Keywords:
Design explanationDesign principlesMechanistic explanationMultilayer networksMultilayer perceptron

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Area of Science:

  • Systems biology
  • Systems neuroscience
  • Philosophy of science

Background:

  • Design principles in systems biology and neuroscience are often viewed as abstract characterizations of mechanisms.
  • A counterargument suggests these principles offer non-mechanistic explanations due to their generality and description of non-causal dependencies.

Purpose of the Study:

  • To propose that design principles facilitate mechanistic explanation by presenting abstract characterizations of mechanisms.
  • To examine the multilayer perceptron as a design principle in systems neuroscience.
  • To address the contention that design principles provide non-mechanistic explanations.

Main Methods:

  • Conceptual analysis of design principles in systems biology and neuroscience.
  • Examination of the multilayer perceptron as a case study.
  • Argumentative response to counterarguments regarding generality and causality.

Main Results:

  • Design principles provide abstract characterizations that facilitate mechanistic explanation.
  • Mechanisms, by nature, are general or abstract.
  • Many design principles represent causal systems, not merely non-causal dependencies.

Conclusions:

  • Design principles are valuable tools for mechanistic explanation in systems biology and neuroscience.
  • The generality of design principles does not preclude them from being mechanistic.
  • Design principles, particularly those representing causal systems, are integral to understanding complex biological and neural mechanisms.