Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

2.9K
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
2.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

292
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
292
Classification of Systems-I01:26

Classification of Systems-I

552
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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:
552
Storage01:23

Storage

365
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
365
Fundamental Theorem of Algebra01:30

Fundamental Theorem of Algebra

240
The Fundamental Theorem of Algebra is central to the study of polynomial equations, asserting that every non-constant polynomial with complex coefficients has at least one complex zero. This means that a polynomial of degree n ≥ 1, written as:  with an ≠ 0, has at least one solution in the complex number system. Since the set of real numbers is a subset of complex numbers, this theorem applies equally to polynomials with real coefficients.Building on this result, the...
240
Castigliano's Theorem: Problem Solving01:14

Castigliano's Theorem: Problem Solving

1.2K
The deflection of a simply supported beam that carries a central point load can be analyzed using structural mechanics principles, particularly by applying Castigliano's theorem. This theorem relates the displacement at the load application point to the partial derivatives of the strain energy in the structure. The simply supported beam with a point load at its center has symmetric reaction forces at the supports, each bearing half of the load. The bending moment at any point along the beam is...
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dimensional Evolution from a Giant Molybdenum-Red Cage-like {Mo<sub>200</sub>} to 1D Chains Enabling Ultrahigh Proton Conduction.

Journal of the American Chemical Society·2026
Same author

Bioenergetic trophic trade-offs determine mass-dependent extinction thresholds across the Cenozoic.

Ecology·2026
Same author

Energy constraint on human health.

Trends in endocrinology and metabolism: TEM·2026
Same author

When energy is scarce, evolution selects dormancy.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Chemputer and chemputation-A universal chemical compound synthesis machine.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Verification and execution of the scientific literature via chemputation augmented by large language models.

Communications chemistry·2026
Same journal

Priority effects inhibit the repeated evolution of phototrophy.

Npj complexity·2026
Same journal

Adaptive transcriptional remodeling of <i>Streptococcus mutans</i> under simulated microgravity and silver stress reveals evolutionary innovation in artificial environments.

Npj complexity·2026
Same journal

Habit learning is associated with efficiently controlled network dynamics in naive macaque monkeys.

Npj complexity·2026
Same journal

The case against efficiency: friction in social media.

Npj complexity·2026
Same journal

Uncovering complementary information sharing in spider monkey collective foraging using higher-order spatial networks.

Npj complexity·2026
Same journal

Copy or collaborate? How networks impact collective problem solving.

Npj complexity·2025
See all related articles

Related Experiment Video

Updated: Jan 18, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K

Assembly theory and its relationship with computational complexity.

Christopher P Kempes1, Michael Lachmann1,2, Andrew Iannaccone3

  • 1The Santa Fe Institute, Santa Fe, NM USA.

Npj Complexity
|September 8, 2025
PubMed
Summary
This summary is machine-generated.

Assembly theory (AT) quantifies complexity using the assembly index, measuring the causal steps needed to build objects. This approach distinguishes selection-driven complexity from random generation, offering a physically measurable framework.

Keywords:
AstrobiologyInformation theory and computationOrigin of life

More Related Videos

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
08:32

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Published on: September 5, 2019

5.9K
Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
10:46

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

2.2K

Related Experiment Videos

Last Updated: Jan 18, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K
Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
08:32

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Published on: September 5, 2019

5.9K
Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
10:46

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

2.2K

Area of Science:

  • Physics
  • Information Theory
  • Theoretical Biology

Background:

  • Selection is a key mechanism driving complexity in nature.
  • Existing complexity measures often focus on compressibility, not causal history.
  • A physically measurable framework for complexity is needed.

Purpose of the Study:

  • To introduce Assembly Theory (AT) as a novel framework for quantifying complexity.
  • To differentiate AT from traditional computational complexity and compression algorithms.
  • To establish AT's foundation in measurable physical causation.

Main Methods:

  • Developed the assembly equation using assembly index and copy number.
  • Formulated mathematical examples to illustrate distinctions.
  • Provided theoretical proofs on computational complexity classes.

Main Results:

  • Assembly index quantifies causal steps, distinct from compressibility metrics (e.g., Shannon entropy, LZW).
  • Assembly index belongs to a different computational complexity class than compression algorithms.
  • Assembly theory provides a physically measurable approach to complexity.

Conclusions:

  • Assembly theory offers a new way to quantify complexity based on causal history and selection.
  • AT provides a robust, empirically grounded alternative to abstract complexity measures.
  • This framework has implications for understanding complex systems, including life.