Simulation study on effects of signaling network structure on the developmental increase in complexity

Abstract

The developmental increase in structural complexity in multicellular lifeforms depends on local, often non-periodic differences in gene expression. These depend on a network of gene-gene interactions coded within the organismal genome. To better understand how genomic information generates complex expression patterns, I have modeled the pattern forming behavior of small artificial genomes in virtual blastoderm embryos. I varied several basic properties of these genomic signaling networks, such as the number of genes, the distributions of positive (inductive) and negative (repressive) interactions, and the strengths of gene-gene interactions, and analyzed their effects on developmental pattern formation. The results show how even simple genomes can generate complex non-periodic patterns under suitable conditions. They also show how the frequency of complex patterns depended on the numbers and relative arrangements of positive and negative interactions. For example, negative co-regulation of signaling pathway components increased the likelihood of (complex) patterns relative to differential negative regulation of the pathway components. Interestingly, neither quantitative differences either in strengths of signaling interactions nor multiple response thresholds to signal concentration (as in morphogen gradients) were essential for formation of multiple, spatially unique cell types. Thus, with combinatorial code of gene regulation and hierarchical signaling interactions, it is theoretically possible to organize metazoan embryogenesis with just a small fraction of the metazoan genome.Because even small networks can generate complex patterns when they contain a suitable set of connections, evolution of metazoan complexity may have depended more on selection for favourable configurations of signaling interactions than on the increase in numbers of regulatory genes.JTB version 1 2

Evolution of Networks for Body Plan Patterning; Interplay of Modularity, Robustness and Evolvability

“…In addition, we studied whether evolved modularity and robustness influence future evolvability. Indeed, we could have used a much more general fitness criterion for body plan patterning, for example maximizing the number of celltypes [65]–[67] or the amount of positional information [68], to study these issues. Instead, we decided to use a more specific fitness criterion that ‘invites’ modularity to evolve, by independently selecting for two functions, segmentation and differentiation.…”

“…Previous simulation studies on the evolution of body plan patterning have modeled the evolution of either segmentation [41], [42], [69] or differentiation [43], [65]–[67], [70] alone. The major aim of these studies was to gain an understanding of how natural developmental mechanisms might have evolved.…”

LinMap: Visualizing Complexity Gradients in Evolutionary Landscapes

“…The network component is based on a standard recurrent neural network architecture [12,20]. Such networks have been widely used in previous models of developmental dynamics [38,14,26,46,47]. The cell lineage component interprets the dynamics of this network in terms of developmental control.…”

“…Within the domain of development and evolution, one possibility would be to extend the scope of LinMap to encompass more detailed developmental models. While the cell lineage representation was particularly useful for the purposes of our study, two-or three-dimensional models incorporating spatial information, signaling, and morphogenesis could be substituted (e.g., [26,23]). One challenge associated with extending LinMap in this direction would be maintaining a balance between the computational expense of evaluating the developmental process and the responsiveness of the visualization.…”

“…Some of these models enable complexity to be quantitatively measured (e.g., [5,29,26]). Other models have bridged the divide between the genetic and developmental mechanisms that build complex forms and the evolutionary mechanisms with which they interact [23,40].…”