Abstract
The characterization of the generic properties underlying the complex interplay ruling cell differentiation is one of the goals of modern biology. To this end, we rely on a powerful and general dynamical model of cell differentiation, which defines differentiation hierarchies on the basis of the stability of gene activation patterns against biological noise.
In particular, in this work we investigate the role of the topology (i.e. scale-free or random) and of the dynamical regime (i.e. ordered, critical or disordered) of gene regulatory networks on the model dynamics. Two real lineage commitment trees, i.e. intestinal crypts and hematopoietic cells, are compared with the hierarchies emerging from the dynamics of ensembles of randomly simulated networks.
Briefly, critical networks with random topology seem to display a wider range of possible behaviours as compared to the others, hence suggesting an intrinsic dynamical heterogeneity that may be fundamental in defining different differentiation trees. Conversely, scale-free networks show a generally more ordered dynamics, which limit the overall variability, yet containing the effect of possible genomic perturbations. Interestingly, a considerable number of networks across all types show emergent trees that are biologically plausible, suggesting that a relatively wide portion of the networks space may be suitable, without the need for a fine tuning of the parameters.
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Notes
- 1.
Given a GRN dynamical model, the long term evolution will confine the cellular states in a specific region of the state space, i.e. an attractor, in which the values of the variables can be fixed over time, can be characterized by oscillatory periodic regimes, or even by more particular non-periodic dynamics (in non finite-states deterministic models). Any GRN can be characterized by the presence of different attractors, reachable from distinct initial conditions. The attractors represent coherent activation patterns of genes.
- 2.
Cell differentiation is the process according to which the progeny of stem cells progressively develops into different and always more specialized cell types, crossing various intermediate stages.
- 3.
Excluding the high degree exponential cutoffs due to the limited size of the networks.
- 4.
We here use the so called quenched model [21], in which both the graph and the boolean functions do not change in time.
- 5.
In several processes, e.g., during the embryogenesis, cell differentiation is not stochastic but it is driven towards precise, repeatable types by specific chemical signals. In our model, it was shown that certain genes, called switch genes, if permanently perturbed and coupled with a change in the threshold always leads the system through the same differentiation pathway. In other words, nodes that uniquely determine to which TES the system will evolve, i.e. deterministic differentiation.
- 6.
For each level of the input tree, the algorithm compares the distribution of the number of children of the two trees. The histogram distance is then defined as the sum of the absolute value of the difference between the number of nodes in the first tree with \(i\) children in the two trees, from \(i =1\) to the maximum number of children. The overall distance is the sum of all the histogram distances of the distinct levels.
- 7.
It is worth noticing that an attractor has always been reached within the simulation time.
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Acknowledgements
This work was partially supported by the project SysBionet (12-4-5148000-15; Imp. 611/12; CUP: H41J12000060001; U.A. 53).
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Graudenzi, A. et al. (2014). Investigating the Role of Network Topology and Dynamical Regimes on the Dynamics of a Cell Differentiation Model. In: Pizzuti, C., Spezzano, G. (eds) Advances in Artificial Life and Evolutionary Computation. WIVACE 2014. Communications in Computer and Information Science, vol 445. Springer, Cham. https://doi.org/10.1007/978-3-319-12745-3_13
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