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Related Concept Videos

Stability of structures01:14

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In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
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Microtubules are hollow cylindrical filaments having a diameter of approximately 25 nm and a length that varies from 200 nm to 25 μm. GTP-bound tubulin subunits form αβ-heterodimers for microtubule assembly. These core building blocks interact longitudinally, polymerizing into protofilaments. The protofilaments then interact with one another through lateral bonding forces to form stable cylindrical microtubules. These cylindrical filaments are dynamic as they undergo repeated...
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In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
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The destabilization of microtubules can occur during different stages of the microtubule lifecycle, such as nucleation or elongation. It can take place at either end of the microtubule or in the microtubule lattices as a whole. The lifespan of individual microtubules within a cell varies according to the cell type and stage of the cell cycle. During interphase, the lifespan of the microtubule is about 30 minutes, while during cell division, it is about 15 minutes. In axonal microtubules of...
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The time response of a linear time-invariant (LTI) system can be divided into transient and steady-state responses. The transient response represents the system's initial reaction to a change in input and diminishes to zero over time. In contrast, the steady-state response is the behavior that persists after the transient effects have faded.
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Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
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Stability-driven assembly theory.

Dan Adler1

  • 1BNY, New York, NY, United States.

Journal of Theoretical Biology
|December 22, 2025
PubMed
Summary
This summary is machine-generated.

Stability-Driven Assembly (SDA) systems show how differential stability alone can create selection pressure. This drives systems toward ordered, low-entropy states without needing replication, offering insights into emergent complexity.

Keywords:
Abiotic evolutionChemical evolutionEmergent complexityEntropy reductionEvolutionary dynamicsPattern formationStability-driven selection

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Area of Science:

  • Theoretical physics
  • Systems biology
  • Origin of life research

Background:

  • The emergence of complexity and information is a central question across scientific disciplines.
  • Existing models often rely on replication or explicit information carriers.

Purpose of the Study:

  • To introduce the Stability-Driven Assembly (SDA) framework for modeling pattern evolution.
  • To investigate if differential stability can drive complexity without replication.

Main Methods:

  • Developed the theoretical SDA framework.
  • Conducted computational experiments on pattern evolution.
  • Performed controlled intervention experiments to test causality.
  • Utilized dynamic network visualization.

Main Results:

  • Stability differences alone can generate selection pressure, favoring lower-entropy states.
  • Emergent stable patterns recursively influence system dynamics.
  • Stability acts as a control parameter shaping the evolutionary landscape.
  • Causal link established between stability constraints and system evolution.

Conclusions:

  • SDA provides a mechanistic explanation for complexity and information emergence in abiotic systems.
  • The framework connects stochastic dynamical systems with prebiotic chemistry.
  • Stability-driven dynamics may precede autocatalysis, replication, and Darwinian evolution.