Combining Causal Bayes Nets and Cellular Automata: A Hybrid Modelling Approach to Mechanisms

Abstract

Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser ([2016]) pointed out—they have problems with capturing relevant spatial and structural information. In this article we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all the merits of a CBN representation of mechanisms. 1Introduction2Causal Bayes Nets and Cellular Automata2.1Causal Bayes nets2.2Cellular automata3A Hybrid Approach for Modelling Mechanisms4Modelling Spatio-structural Mechanisms Using CBN–CA Hybrids4.1The biophysics of a tiny peptide world4.2A hybrid model of receptor activation in the tiny peptide world5Conclusion