XB-ART-36669BMC Syst Biol October 22, 2007; 1 46.
Multiscale computational analysis of Xenopus laevis morphogenesis reveals key insights of systems-level behavior.
Tissue morphogenesis is a complex process whereby tissue structures self-assemble by the aggregate behaviors of independently acting cells responding to both intracellular and extracellular cues in their environment. During embryonic development, morphogenesis is particularly important for organizing cells into tissues, and although key regulatory events of this process are well studied in isolation, a number of important systems-level questions remain unanswered. This is due, in part, to a lack of integrative tools that enable the coupling of biological phenomena across spatial and temporal scales. Here, we present a new computational framework that integrates intracellular signaling information with multi-cell behaviors in the context of a spatially heterogeneous tissue environment. We have developed a computational simulation of mesendoderm migration in the Xenopus laevis explant model, which is a well studied biological model of tissue morphogenesis that recapitulates many features of this process during development in humans. The simulation couples, via a JAVA interface, an ordinary differential equation-based mass action kinetics model to compute intracellular Wnt/beta-catenin signaling with an agent-based model of mesendoderm migration across a fibronectin extracellular matrix substrate. The emergent cell behaviors in the simulation suggest the following properties of the system: maintaining the integrity of cell-to-cell contact signals is necessary for preventing fractionation of cells as they move, contact with the Fn substrate and the existence of a Fn gradient provides an extracellular feedback loop that governs migration speed, the incorporation of polarity signals is required for cells to migrate in the same direction, and a delicate balance of integrin and cadherin interactions is needed to reproduce experimentally observed migratory behaviors. Our computational framework couples two different spatial scales in biology: intracellular with multicellular. In our simulation, events at one scale have quantitative and dynamic impact on events at the other scale. This integration enables the testing and identification of key systems-level hypotheses regarding how signaling proteins affect overall tissue-level behavior during morphogenesis in an experimentally verifiable system. Applications of this approach extend to the study of tissue patterning processes that occur during adulthood and disease, such as tumorgenesis and atherogenesis.
PubMed ID: 17953751
PMC ID: PMC2190763
Article link: BMC Syst Biol
Species referenced: Xenopus laevis
Genes referenced: ctnnb1 fn1
GO keywords: cell migration
Article Images: [+] show captions
|Figure 1. Xenopus laevis mesendoderm migration. (Top Left) Image of Xenopus laevis mesendoderm explant, viewed from the top down. (Bottom) Schematic of mesendoderm cell shingling, where cells in the leading edge of the explant overlap the neighboring cells trailing behind them. The key parameters of the multicell model are shown, including: cadherins, integrins, and fibronectin (key located top right).|
|Figure 2. Multicell ABM simulation environment. (Top Left) Simulation space where pixels containing Fn are represented by grayscale coloration (bottom right), and nine sub-cellular agents (blue) comprise a single simulated mesendodermal cell (bottom left). (Top Right) Sliders, buttons, and graphical outputs where the user can adjust parameter levels and quantitatively monitor emergent behavior, such as the Fn concentration and accumulation of polarity signals in space and time.|
|Figure 3. Screenshots of mesendoderm migration in ABM at different time points. Blue cells migrate in the direction of the red arrow over the ~2.5 hour time window. Cell degradation of the Fn matrix is visible in the darker pixels left behind as they move forward. Global thinning of the explant in the lateral direction (left to right) and lengthening in the longitudinal direction (top to bottom) is also evident.|
|Figure 4. Calculating cell force vectors from fibronectin gradients. (A) Fn concentration across a single cell. As cells move forward, a gradient of fibronectin is established from the leading edge to the trailing edge. (B) A Fn gradient is also established across the entire explant length.|
|Figure 5. Velocity and displacement of individual cells and the explant tissue. (A) Velocity (μm/hr) of single mesendodermal cell (blue) vs. explant average (red). The oscillations in single cell velocity reflect the temporal balance between cadherin and integrin signaling, as well as the Fn gradient under the cell, which is in a gradient across the tissue, but still somewhat random from pixel to pixel. The velocity returns to 0 at the end of the simulation because the leading cell reaches the boundary of the substrate. (B) Displacement (μm) of single cells over time. Each colored tracing represents the displacement of a single cell in the explant, defined as the distance traveled from the cell's initial position in the simulation space.|
|Figure 6. Schematic depicting the multiscale computational framework. The ABM outputs Fn levels at each pixel to a text file and opens a JAVA interface, which opens Matlab. Matlab imports the text file containing Fn concentrations, and the intracellular model calculates the amount of β-catenin signaling in each cell. These levels are input into the multi-cell model, and impact the extent of cadherin activation in each cell.|
|Figure 7. Predictions of the multiscale model. (Top Row) Screenshots from the multiscale model at different simulation time points show that cells migrate in a uniform direction while maintaining contact with one another. (Bottom Row) As cells migrate toward the top of the simulation space, a Fn matrix gradient is established from the leading edge to trailing edge of the mesendoderm explant, and this can also be seen in the darkening of pixels behind migrating cells.|
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