Spatial Description of Biochemical Networks
Many biological behaviors require that biochemical species be distributed spatially throughout the cell or across a number of cells. To explain these situations accurately requires a spatial description of the underlying network. At the continuum level, this is usually done using reaction-diffusion equations. Here we demonstrate how this class of models arises. We also show how the framework is used in two popular models proposed to explain spatial patterns during development.
KeywordsDiffusion Morphogen gradient Pattern formation Reaction-diffusion Turing instability
Cells are complex environments consisting of spatially segregated entities, including the nucleus and various other organelles. Even within these compartments, the concentrations of various biochemical species are not homogeneous, but can vary significantly. The proper localization of proteins and other biochemical species to their respective sites is important for proper cell function. This can be because the spatial distribution of signaling molecules itself confers information, such as when a cell needs to respond to a spatially graded cue to guide its motion (Iglesias and Devreotes 2008) or growth pattern (Lander 2013). Alternatively, information that is obtained in one part of the cell must be transmitted to another part of the cell, as when receptor-ligand binding at the cell surface leads to transcriptional responses in the nucleus. Frequently, describing the action of a biological network accurately requires not only that one account for the chemical interactions between the different components but that the spatial distribution of the signaling molecules also be considered.
Accounting for Spatial Distribution in Models
Mathematical models of biological networks usually assume that reactions take place in well-stirred vessels in which the concentrations of the interacting species are spatially homogeneous and hence need not be accounted for explicitly. These systems also assume that the volume is constant. When the spatial location of molecules in cells is important, the concentration of species changes in both time and space.
One way to account for spatial distribution of signaling components is through compartmental models. As the name suggests, in these models the cell is divided into different regions that are segregated by membranes. Within each compartment, the concentration of the network species is assumed to be spatially homogeneous. The membranes in these models can be assumed to be either permeable or impermeable. In permeable membranes, information passes through small openings, such as ion channels or nuclear pores, which allow molecules to move from one side of the membrane to the other. With impermeable membranes, information must be transduced by transmembrane signaling elements, such as cell-surface receptors, that bind to a signaling molecule in one side of the membrane and release a secondary effector on the other side. Note that in this case, the membrane itself acts as a third compartment.
Diffusion and Advection
Measuring Diffusion Coefficients
Diffusion-Limited Reaction Rates
The effect of spatial heterogeneities has been of long interest to developmental biologists, who study how spatial patterns arise. Two distinct models have been proposed to explain how this patterning can arise. Here we introduce these models and discuss their relative merits. Though usually seen as competing models, there is recent evidence suggesting that both models may play complementary roles during development (Reth et al. 2012).
A morphogen is a diffusible molecule that is produced or secreted at one end of an organism. Diffusion away from the localized source forms a concentration gradient along the spatial dimension. Morphogens are used to control gene expression of cells lying along this spatial domain. Thus, a morphogen gradient gives rise to spatially dependent expression profiles that can account for spatial developmental patterns (Rogers and Schier 2011).
Though morphogen gradients are commonly used to describe signaling during development, where the gradient can extend across a number of cells, the mathematics described above are equally suitable for describing concentration gradients of intracellular proteins. In this case, the dimension of the cell has a significant effect on the shape of the gradient (Meyers et al. 2006).
As discussed above, morphogen gradients are established in an open-loop mode. As such, the actual concentration experienced at a point downstream of the source of the morphogen will vary depending on a number of parameters, including the flux j 0 and the rate of degradation k. Moreover, because the concentration of the morphogen decreases as the distance from the source grows, the relative stochastic fluctuations will increase. How to manage this uncertainty is an active area of research (Rogers and Schier 2011; Lander 2013).
In 1952, Alan Turing proposed a model of how patterns could arise in biological systems (Turing 1952). His interest was in explaining how an embryo, initially spherical, could give rise to a highly asymmetric organism. He posited that the breaking of symmetry could be a result of the change in the stability of the homogeneous state of the network which would amplify small fluctuations inherent in the initial symmetry. Turing sought to explain how these instabilities could arise using only reaction-diffusion systems.
These requirements in the sign pattern of the two molecules lead to one of two classes of systems. In the first class, known as activator/inhibitor systems, the activator (assume species 1) is autocatalytic (a 11 > 0) and also stimulates the inhibitor (a 21 > 0), which negatively regulates the activator (a 12 < 0). In the other class, known as substrate-depletion systems, a product (species 1) is autocatalytic (a 11 > 0), but in its production consumes (a 21 < 0) the substrate (species 2) whose presence is needed for formation of the product (a 12 > 0). Note that both systems involve an autocatalytic positive feedback loop (a 11 > 0), as well as a negative feedback loop involving both species (a 12 a 21 < 0).
These conditions are necessary, but not sufficient. They ensure that the parabola defined by Eq. 1 has real roots. However, when diffusion takes place in finite domains, the parameter q can only take discrete values \(q = 2\pi n/L\) for integers n. Thus, for a spatial mode to be unstable, it must be that det(A − q 2 D) < 0 at specific values of q corresponding to integers n. If the dimension of the domain is changing, as would be expected in a growing domain, the parameter q 2 will decrease over time suggesting that higher modes may lose stability. Thus, the nature of the pattern may evolve over time.
Over the years, Turing’s framework has been a popular model among theoretical biologists and has been used to explain countless patterns seen in biological systems. It has not had the same level of acceptance among biologists, likely because of the difficulty of mapping a complex biological system involving numerous interacting species into the simple nature of the theoretical model (Kondo and Miura 2010).
Summary and Future Directions
Spatial aspects of biochemical signaling are increasingly playing a role in the study of cellular signaling systems. Part of this interest is the desire to explain spatial patterns seen in subcellular localizations observed through live cell imaging using fluorescently tagged proteins. The ever-increasing computational power available for simulations is also facilitating this progress. Specially built spatial simulation software, such as the Virtual Cell, is freely available and tailor-made for biological simulations enabling simulation of spatially varying reaction networks in cells of varying size and shape (Cowan et al. 2012).
Of course, cell shapes are not static, but evolve in large part due to the effect of the underlying biochemical system. This requires simulation environments that solve reaction-diffusion systems in changing morphologies. This has received considerable interest in modeling cell motility (Holmes and Edelstein-Keshet 2013).
Another aspect of spatial models that is only now being addressed is the role of mechanics in driving spatially dependent models. For example, it has recently been shown that the interaction between biochemistry and biomechanics can itself drive Turing-like instabilities (Goehring and Grill 2013).
Finally, we note that our discussion of spatially heterogeneous signaling has been based on continuum models. As with spatially invariant systems, this approach is only valid if the number of molecules is sufficiently large that the stochastic nature of the chemical reactions can be ignored. In fact, spatial heterogeneities may lead to localized spots requiring a stochastic approach, even though the molecule numbers are such that a continuum approach would be acceptable if the cell were spatially homogeneous. The analysis of stochastic interactions in these systems is still much in its infancy and is likely to be an increasingly important area of research (Mahmutovic et al. 2012).