An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems

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 intervent...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:iScience Ročník 19; s. 1160 - 1172
Hlavní autori: Zenil, Hector, Kiani, Narsis A., Marabita, Francesco, Deng, Yue, Elias, Szabolcs, Schmidt, Angelika, Ball, Gordon, Tegnér, Jesper
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Inc 27.09.2019
Elsevier
Predmet:
Algorithms
Complex Systems
Computer Science
Gene Network
Systems Biology
Algorithms
Systems Biology
Complex Systems
Gene Network
Computer Science
ISSN:2589-0042, 2589-0042
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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. [Display omitted] •Use of algorithmic randomness to steer systems in dynamical space to control and reprogram them•Applying series of controlled interventions we reprogram systems, programs, and networks•The method reconstructs the phase space and generative rules of discrete systems•We validate on a number of networks with different topologies, and on biological networks Gene Network; Systems Biology; Complex Systems; Computer Science; Algorithms
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Lead Contact
These authors contributed equally
Senior author
ISSN:2589-0042
2589-0042
DOI:10.1016/j.isci.2019.07.043