Evolutionary Constraint in Artificial Gene Regulatory Networks

Turner, Alexander P.; Lacey, George; Schoene, Annika Marie; Dethlefs, Nina

doi:10.1007/978-3-319-97982-3_3

Cited by 2 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Discussionmentioning

confidence: 97%

“…The higher number of peripheral nodes compared to intermediate and hub nodes in both treatments involving heat stress suggests that, while the general stress response may be conserved, multiple components of the network may be activated in response to different external stressors (McClintock 1984). This perhaps reflects a trade-off between evolutionary robustness constraint and stressor-specific gene expression, an architectural requirement of evolvable systems (Kitano 2004) and a trait also seen in computational simulations of evolved artificial gene regulatory networks (Turner et al 2019;Turner and Wollenberg Valero 2021) The intermediate position of nodes responding to UV indicates that the innate response to UV is regulated via nodes that affect many other gene products through having the highest neighborhood connectivity (Wollenberg Valero 2020), which may cause activation of multiple genes in a pathway, potentially leading to survival consequences (Jeong et al 2001;Bonatto 2007). It needs to be explored further whether the differentially expressed genes are related to repair or damage processes, or both (Zagarese and Williamson 2001;Dong et al 2007;Chen et al 2020;Feugere et al, n.d.).…”

Section: Stress Response Genes Are Located Centrally In the Zebrafish...mentioning

confidence: 93%

See 1 more Smart Citation

Network architecture of transcriptomic stress responses in zebrafish embryos

Beine,

Feugere,

Turner

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Protein-protein interaction (PPI) network topology can contribute to explain fundamental properties of genes, from expression levels to evolutionary constraints. Genes central to a network are more likely to be both conserved and highly expressed, whereas genes that are able to evolve in response to selective pressures but expressed at lower levels are located on the periphery of the network. The stress response is likewise thought to be conserved, however, experimental evidence for these patterns is limited. We examined whether the transcriptomic response to two environmental stressors (heat, UV, and their combination) is related to PPI architecture in zebrafish (Danio rerio)embryos. We show that stress response genes are situated more centrally in the PPI network. The transcriptomic response to heat was located in both central and peripheral positions, whereas UV response occupied central to intermediate positions. Across treatments, differentially expressed genes in different parts of the network affected identical phenotypes. Our results indicate that the zebrafish stress response has mostly conserved but also some stressor-specific aspects. These properties can aid in better understanding the organismal response to diverse and co-occurring stressors. Network position was further linked to the magnitude of fold changes of genes and types and number of linked phenotype components. Given the speed of contemporary changes in aquatic ecosystems, our approach can aid in identifying novel key regulators of the systemic response to specific stressors.

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Stress Response Genes Are Located Centrally In the Zebrafish...mentioning

confidence: 93%

Network architecture of transcriptomic stress responses in zebrafish embryos

Beine,

Feugere,

Turner

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The artificial gene regulatory network (AGRN) is a computational model which takes inspiration form gene regulation in nature. The AGRN is a connectionist architecture specifically designed for computational problem solving [11]. The AGRN is comprised of a set of individual genes which are connected to one another in a typically non-symmetrical topology.…”

Section: Artificial Gene Regulatory Networkmentioning

confidence: 99%

“…AGRN's are typically deterministic in nature, but in this work we inject a stochastic element which allows only some of the network to be functional at any given time. This will introduce an element of constraint, which has been shown to affect robustness and adaptability in computer models [11] as well as in biological networks [15]. We can then compare this to a deterministic AGRN by optimising them using a genetic algorithm to solve two key tasks, the coupled inverted pendulums task and the rocket lander task [7,8].…”

Section: Introductionmentioning

confidence: 99%

Stochasticity Improves Evolvability in Artificial Gene Regulatory Networks

Turner

Valero

2021

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Biological organisms can adapt to unstable conditions. This adaptability results from a combination of heterogeneity and plasticity within a population, which means perturbations are unlikely to have the same effect on all population members, allowing time for adaptations to evolve. In this work we show that this behaviour can be duplicated by introducing stochasticity to generate functional heterogeneity in artificial gene regulatory networks, a biologically inspired tool used to solve computational problems.We show that by introducing this stochasticity, artificial gene regulatory networks are more robust to perturbations when applied to complex dynamical tasks meaning that they are less affected than their deterministic counterparts. Moreover, we analyse the structure of the artificial gene regulatory networks to understand what effect stochasticity has on the structure of the networks and whether we detect similarity to their biological counterparts.

show abstract

Evolutionary Constraint in Artificial Gene Regulatory Networks

Cited by 2 publications

References 21 publications

Network architecture of transcriptomic stress responses in zebrafish embryos

Network architecture of transcriptomic stress responses in zebrafish embryos

Stochasticity Improves Evolvability in Artificial Gene Regulatory Networks

Contact Info

Product

Resources

About