An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
| Autor: | Angelika Schmidt, Szabolcs Elias, Gordon Ball, Jesper Tegnér, Francesco Marabita, Hector Zenil, Yue Deng, Narsis A. Kiani |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
FOS: Computer and information sciences Theoretical computer science Dynamical systems theory Computer science Systems biology Computer Science - Information Theory Complex system Gene regulatory network Algorithmic randomness 02 engineering and technology Network topology Article Linear dynamical system Causality (physics) 03 medical and health sciences Calculus Causation lcsh:Science Dynamical system (definition) Randomness Biological data Multidisciplinary business.industry Information Theory (cs.IT) Systems Biology Computability Complex Systems 021001 nanoscience & nanotechnology Other Quantitative Biology (q-bio.OT) Causality Quantitative Biology - Other Quantitative Biology Cellular automaton Living systems 030104 developmental biology Gene Network Phase space FOS: Biological sciences Computer Science Probability distribution lcsh:Q Artificial intelligence 0210 nano-technology business Algorithms Biological network |
| Zdroj: | iScience iScience, Vol 19, Iss, Pp 1160-1172 (2019) |
| Popis: | Summary 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. Graphical Abstract Highlights • 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 |
| Databáze: | OpenAIRE |
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