Systems Modeling at Multiple Levels of Regulation: Linking Systems and Genetic Networks to Spatially Explicit Plant Populations

Abstract

Selection and adaptation of individuals to their underlying environments are highly dynamical processes, encompassing interactions between the individual and its seasonally changing environment, synergistic or antagonistic interactions between individuals and interactions amongst the regulatory genes within the individual. Plants are useful organisms to study within systems modeling because their sedentary nature simplifies interactions between individuals and the environment, and many important plant processes such as germination or flowering are dependent on annual cycles which can be disrupted by climate behavior. Sedentism makes plants relevant candidates for spatially explicit modeling that is tied in with dynamical environments. We propose that in order to fully understand the complexities behind plant adaptation, a system that couples aspects from systems biology with population and landscape genetics is required. A suitable system could be represented by spatially explicit individual-based models where the virtual individuals are located within time-variable heterogeneous environments and contain mutable regulatory gene networks. These networks could directly interact with the environment, and should provide a useful approach to studying plant adaptation.

Keywords: gene regulatory networks; landscape genetics; population genetics; simulation; spatial individual based modeling; systems biology.

Figure 1

The levels of regulation within the proposed modeling framework. The five levels are: the genic level, genome, individual, population and environment. At the genic level, the genes interact with each other as a gene-regulatory networks (GRN) to produce a phenotype. At the genome level, these genes are arranged into chromosomes, which segregate at meiosis, and the comprising genes mutate and recombine, altering their function. The individual has various life cycle histories and if sufficiently fit from its comprising genetic material, reproduces with other individuals to produce progeny. The individuals make up the populations, which through admix through migration, and can lead to differentiation through bottlenecks and founder effects. The environment contains parameters that change cyclically (or unexpectedly), which is fed into the GRN of the individuals.

Figure 2

Vernalization and photoperiodicity in Barley. Gene Vrn2 negatively down-regulates gene Vrn1, preventing flowering. During periods of cold, short days, Vrn2 is down-regulated. However a period of long days is required to activate genes Ppd1 and Vrn2, which activate flowering.

Figure 3

Example network motifs. (A) Single input module (B), multi-input module, (C) coherent feed-forward loop: The motif consists of a direct and an indirect pathway to activate the final gene. (D) Incoherent feed-forward loop: The overall sign of the indirect and direct paths differ. (E) Three-cycle positive feedback loop, (F) three-cycle negative feedback loop, (F) bi-fan motif, (G) double-positive feedback loop.

Figure 4

Example of a Boolean network. (A) AND motif: Genes A and B co-regulate each other, therefore Genes A and B must be active to activate Gene C (B) OR motif, either Gene A or B is sufficient to activate Gene C (C) NOT motif: Gene B down-regulates Gene C, therefore must be “off” to allow activation of Gene C.