An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems

被引:0
|
作者
Zenil, Hector [1 ,2 ,3 ,4 ,5 ]
Kiani, Narsis A. [1 ,2 ,4 ,5 ]
Marabita, Francesco [2 ,4 ]
Deng, Yue [2 ]
Elias, Szabolcs [2 ,4 ]
Schmidt, Angelika [2 ,4 ]
Ball, Gordon [2 ,4 ]
Tegner, Jesper [2 ,4 ,6 ]
机构
[1] Karolinska Inst, Ctr Mol Med, Algorithm Dynam Lab, S-17176 Stockholm, Sweden
[2] Karolinska Inst, Dept Med, Ctr Mol Med, Unit Computat Med, S-17176 Stockholm, Sweden
[3] Oxford Immune Algorithm, Reading RG1 3EU, Berks, England
[4] Sci Life Lab, S-17165 Solna, Sweden
[5] LABORES Nat & Digital Sci, Algorithm Nat Grp, F-75006 Paris, France
[6] King Abdullah Univ Sci & Technol KAUST, Biol & Environm Sci & Engn Div, Comp Elect & Math Sci & Engn Div, Thuwal 239556900, Saudi Arabia
关键词
REGULATORY NETWORK; IDENTIFICATION; COMPLEXITY; PROGRAMS; DYNAMICS;
D O I
10.1016/j.isci.201907.043
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
We introduce and develop a method that demonstrates that the algorithmic information content of a system can be used as a steering handle in the dynamical phase space, thus affording an avenue for controlling and reprogramming systems. The method consists of applying a series of controlled interventions to a networked system while estimating how the algorithmic information content is affected. We demonstrate the method by reconstructing the phase space and their generative rules of some discrete dynamical systems (cellular automata) serving as controlled case studies. Next, the model-based interventional or causal calculus is evaluated and validated using (1) a huge large set of small graphs, (2) a number of larger networks with different topologies, and finally (3) biological networks derived from a widely studied and validated genetic network (E. coli) as well as on a significant number of differentiating (Th17) and differentiated human cells from a curated biological network data.
引用
收藏
页码:1160 / +
页数:41
相关论文
共 50 条
  • [21] Typing assumptions improve identification in causal discovery
    Brouillard, Philippe
    Taslakian, Perouz
    Lacoste, Alexandre
    Lachapelle, Sebastien
    Drouin, Alexandre
    CONFERENCE ON CAUSAL LEARNING AND REASONING, VOL 177, 2022, 177
  • [22] Computational Creativity and Aesthetics with Algorithmic Information Theory
    Mondol, Tiasa
    Brown, Daniel G.
    ENTROPY, 2021, 23 (12)
  • [23] Efficiency characterization of a large neuronal network: A causal information approach
    Montani, Fernando
    Deleglise, Emilia B.
    Rosso, Osvaldo A.
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2014, 401 : 58 - 70
  • [24] Algorithmic Metatheorems for Decidable LTL Model Checking over Infinite Systems
    To, Anthony Widjaja
    Libkin, Leonid
    FOUNDATIONS OF SOFTWARE SCIENCE AND COMPUTATIONAL STRUCTURES, PROCEEDINGS, 2010, 6014 : 221 - 236
  • [25] Optimal causal inference: Estimating stored information and approximating causal architecture
    Still, Susanne
    Crutchfield, James P.
    Ellison, Christopher J.
    CHAOS, 2010, 20 (03)
  • [26] Understanding how replication processes can maintain systems away from equilibrium using Algorithmic Information Theory
    Devine, Sean D.
    BIOSYSTEMS, 2016, 140 : 8 - 22
  • [27] Causal emergence from effective information: Neither causal nor emergent?
    Dewhurst, Joe
    THOUGHT-A JOURNAL OF PHILOSOPHY, 2021, 10 (03): : 158 - 168
  • [28] Nonlinear Causal Discovery via Dynamic Latent Variables
    Yang, Xing
    Lan, Tian
    Qiu, Hao
    Zhang, Chen
    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2025,
  • [29] Survey and Evaluation of Causal Discovery Methods for Time Series
    Assaad, Charles K.
    Devijver, Emilie
    Gaussier, Eric
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2022, 73 : 767 - 819
  • [30] Generalism drives abundance: A computational causal discovery approach
    Song, Chuliang
    Simmons, Benno, I
    Fortin, Marie-Josee
    Gonzalez, Andrew
    PLOS COMPUTATIONAL BIOLOGY, 2022, 18 (09)