Evidence map›Paper›PMID 29133455›Full record

ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2017

How causal analysis can reveal autonomy in models of biological systems.

William Marshall, Hyunju Kim, Sara I Walker, Giulio Tononi, Larissa Albantakis

Abstract read
In one paragraph

Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Intrinsic units: identifying a system's causal grain.Neuroscience of consciousness · 2026
    Article
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  6. System Integrated Information.Entropy (Basel, Switzerland) · 2023
    Article
  7. Article
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  13. The Multiple Paths to Multiple Life.Journal of molecular evolution · 2021
    Review
  14. The Grayness of the Origin of Life.Life (Basel, Switzerland) · 2021
    Article
  15. The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  16. Computing Integrated Information (Entropy (Basel, Switzerland) · 2020
    Article
  17. Article
  18. Four-Types of IIT-Induced Group Integrity ofEntropy (Basel, Switzerland) · 2020
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

William MarshallDepartment of Psychiatry, University of Wisconsin, 6001 Research Park Blvd, Madison, WI 53719, USA.ORCID http://orcid.org/0000-0002-4779-6734
Hyunju KimBEYOND: Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA.
Sara I WalkerBEYOND: Center for Fundamental Concepts in Science, Arizona State University, Tempe, AZ, USA.
Giulio TononiDepartment of Psychiatry, University of Wisconsin, 6001 Research Park Blvd, Madison, WI 53719, USA.
Larissa AlbantakisDepartment of Psychiatry, University of Wisconsin, 6001 Research Park Blvd, Madison, WI 53719, USA albantakis@wisc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Standard techniques for studying biological systems largely focus on their dynamical or, more recently, their informational properties, usually taking either a reductionist or holistic perspective. Yet, studying only individual system elements or the dynamics of the system as a whole disregards the organizational structure of the system-whether there are subsets of elements with joint causes or effects, and whether the system is strongly integrated or composed of several loosely interacting components. Integrated information theory offers a theoretical framework to (1) investigate the compositional cause-effect structure of a system and to (2) identify causal borders of highly integrated elements comprising local maxima of intrinsic cause-effect power. Here we apply this comprehensive causal analysis to a Boolean network model of the fission yeast (

Indexed as

Models, BiologicalCell CycleSchizosaccharomycesautonomycausalityintegrated informationorigin of life

Identifiers

PMID29133455
PMCPMC5686412

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.