Related Experiment Video
Updated: May 9, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Published on: January 31, 2014
Explanations for failures in designed and evolved systems
Randolph M Nesse1,2, Jay B Labov3, Guru Madhavan3
1Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85287, USA.
This study compares why machines and organisms are vulnerable to failure. While some reasons overlap, fundamental differences exist, particularly in design trade-offs and the absence of a perfect blueprint in biology, challenging the machine metaphor for living systems.
Area of Science:
- Evolutionary Biology
- Engineering
- Philosophy of Science
Background:
- Engineers analyze machine failure origins; biologists are newly investigating organismal disease susceptibility.
- Vulnerability in machines and organisms shares some global explanations like design flaws and environmental factors.
Purpose of the Study:
- To compare explanations for machine failure with those for biological vulnerability.
- To explore the implications of the machine metaphor for understanding biological complexity.
Main Methods:
- Comparative analysis of failure explanations in engineering and biology.
- Examination of global categories of vulnerability (e.g., design deficiencies, trade-offs).
Main Results:
- Shared explanations include design deficiencies, corrupted plans, assembly variations, environmental factors, and trade-offs.
- Key differences lie in machines adhering to blueprints versus species lacking them, and distinct trade-off objectives (performance vs. gene transmission).
Conclusions:
- A common framework for failure analysis is potentially valuable but requires acknowledging fundamental biological differences.
- The 'body as a designed machine' metaphor can obscure the nature of complex biological systems and foster misconceptions.
Related Concept Videos
Random and Systematic Errors
Control Systems
At the heart...
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Limits to Natural Selection
Classification of Systems-I
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Types of Errors: Detection and Minimization
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...

