AI-driven Automated Discovery Tools Reveal Diverse Behavioral Competencies of Biological Networks

Abstract

Many applications in biomedicine and synthetic bioengineering depend on the ability to understand, map, predict, and control the complex, context-sensitive behavior of chemical and genetic networks. The emerging field of diverse intelligence has offered frameworks with which to investigate and exploit surprising problem-solving capacities of unconventional agents. However, for systems that are not conventional animals used in behavior science, there are few quantitative tools that facilitate exploration of their competencies, especially when their complexity makes it infeasible to use unguided exploration . Here, we formalize and investigate a view of gene regulatory networks as agents navigating a problem space. We develop automated tools to efficiently map the repertoire of robust goal states that GRNs can reach despite perturbations. These tools rely on two main contributions that we make in this paper: (1) Using curiosity-driven exploration algorithms, originating from the AI community to explore the range of behavioral abilities of a given system, that we adapt and leverage to automatically discover the range of reachable goal states of GRNs and (2) Proposing a battery of empirical tests inspired by implementation-agnostic behaviorist approaches to assess their navigation competencies. Our data reveal that models inferred from real biological data can reach a surprisingly wide spectrum of steady states, while showcasing various competencies that living agents often exhibit, in physiological network dynamics and that do not require structural changes of network properties or connectivity. Furthermore, we investigate the applicability of the discovered “behavioral catalogs” for comparing the evolved competencies across classes of evolved biological networks, as well as for the design of drug interventions in biomedical contexts or for the design of synthetic gene networks in bioengineering. Altogether, these automated tools and the resulting emphasis on behavior-shaping and exploitation of innate competencies open the path to better interrogation platforms for exploring the complex behavior of biological networks in an efficient and cost-effective manner. To read the interactive version of this paper, please visit https://developmentalsystems.org/curious-exploration-of-grn-competencies.

Evolutionary Implications of Multi-Scale Intelligence

“…However, as occurs with biomechanical 133 , biochemical 134,135 and bioelectric prepatterning 8,94,136 , the initial states of an NCA of moderate size could be seen as a coarse-grained scaffold, based on which an NCA of potentially much higher resolution can run its multi-scale competency-based developmental program to self-assemble a high-resolution target pattern 137 . Alternatively, we suggest utilizing a Compositional Pattern Producing Network (CPPN) 120,138 to indirectly encode the initial states of all cells on the grid of an NCA, allowing such a hybrid approach to perform in-silico morphogenesis at scale. Unfortunately, it has been proven difficult, if not unfeasible, to exactly reproduce predefined target patterns reliably with neuroevolution of CPPNs alone 139 , which is why we here refrained from this approach; we emphasize, however, that gradient-based methods such as Neural Radiance Fields (NeRF) 140 to train CPPN-like architectures might be an interesting workaround.…”

“…To study the effects of different types of decision-making machinery within a cell, we utilize two different architectures for the NCA's artificial neural networks (ANNs), a Feed Forward (FF) and a recurrent ANN inspired by gene regulatory networks [117][118][119][120] (RGRNs) 121 . Notably, the RGRN-agent architecture augments cells with an internal memory that is independent of their states in the NCA and can thus not be accessed by the cells' neighbors.…”

“…( 3) limits the scalability of our multi-scale competency approach of morphogenesis to significantly larger systems, as the size of the structural genome will grow correspondingly with the number of cells in the system. However, by utilizing Compositional Pattern Producing Networks (CPPNs) 120,138 the parameters, θ H , of a hyper-network, f (H) θ (•), could replace the structural genes in eq. ( 3) such that the initial states of each cell i are indirectly encoded by the hyper-network based on their relative spatial positions, (x i /N x , y i /N y ), on the Neural Cellular Automaton's (NCA's) grid via c i (0) = f (H) θ (x i /N x , y i /N y ).…”

Evolutionary Implications of Self-Assembling Cybernetic Materials with Collective Problem-Solving Intelligence at Multiple Scales

“…However, as occurs with biomechanical [136], biochemical [137,138] and bioelectric pre-patterning [8,94,139], the initial states of an NCA of moderate size could be seen as a coarse-grained scaffold, based on which an NCA of a potentially much higher resolution can run its multi-scale competency-based developmental program to self-assemble a high-resolution target pattern [140]. Alternatively, we suggest utilizing a Compositional Pattern Producing Network (CPPN) [120,141] to indirectly encode the initial states of all cells on the grid of an NCA, allowing such a hybrid approach to perform in silico morphogenesis at scale. Unfortunately, it has been proven difficult, if not unfeasible, to exactly reproduce predefined target patterns reliably with the neuroevolution of CPPNs alone [142], which is why we here refrained from this approach; we emphasize, however, that gradient-based methods such as Neural Radiance Fields (NeRFs) [143] to train CPPN-like architectures might be an interesting workaround.…”

“…To study the effects of different types of decision-making machinery within a cell, we utilize two different architectures for the NCA artificial neural networks (ANNs), a Feedforward (FF) and a recurrent ANN inspired by gene regulatory networks [117][118][119][120] (RGRNs). (The terminology FF and RGRN stems from the respective agents' Feedforward and Recurrent Gene Regulatory Network ANN controller layers (see Appendix A for details)).…”