450 research outputs found
Modelling the evolution of transcription factor binding preferences in complex eukaryotes
Transcription factors (TFs) exert their regulatory action by binding to DNA
with specific sequence preferences. However, different TFs can partially share
their binding sequences due to their common evolutionary origin. This
`redundancy' of binding defines a way of organizing TFs in `motif families' by
grouping TFs with similar binding preferences. Since these ultimately define
the TF target genes, the motif family organization entails information about
the structure of transcriptional regulation as it has been shaped by evolution.
Focusing on the human TF repertoire, we show that a one-parameter evolutionary
model of the Birth-Death-Innovation type can explain the TF empirical
ripartition in motif families, and allows to highlight the relevant
evolutionary forces at the origin of this organization. Moreover, the model
allows to pinpoint few deviations from the neutral scenario it assumes: three
over-expanded families (including HOX and FOX genes), a set of `singleton' TFs
for which duplication seems to be selected against, and a higher-than-average
rate of diversification of the binding preferences of TFs with a Zinc Finger
DNA binding domain. Finally, a comparison of the TF motif family organization
in different eukaryotic species suggests an increase of redundancy of binding
with organism complexity.Comment: 14 pages, 5 figures. Minor changes. Final version, accepted for
publicatio
In silico transitions to multicellularity
The emergence of multicellularity and developmental programs are among the
major problems of evolutionary biology. Traditionally, research in this area
has been based on the combination of data analysis and experimental work on one
hand and theoretical approximations on the other. A third possibility is
provided by computer simulation models, which allow to both simulate reality
and explore alternative possibilities. These in silico models offer a powerful
window to the possible and the actual by means of modeling how virtual cells
and groups of cells can evolve complex interactions beyond a set of isolated
entities. Here we present several examples of such models, each one
illustrating the potential for artificial modeling of the transition to
multicellularity.Comment: 21 pages, 10 figures. Book chapter of Evolutionary transitions to
multicellular life (Springer
Neutrality and Robustness in Evo-Devo: Emergence of Lateral Inhibition
Embryonic development is defined by the hierarchical dynamical process that translates genetic information (genotype) into a spatial gene expression pattern (phenotype) providing the positional information for the correct unfolding of the organism. The nature and evolutionary implications of genotype–phenotype mapping still remain key topics in evolutionary developmental biology (evo-devo). We have explored here issues of neutrality, robustness, and diversity in evo-devo by means of a simple model of gene regulatory networks. The small size of the system allowed an exhaustive analysis of the entire fitness landscape and the extent of its neutrality. This analysis shows that evolution leads to a class of robust genetic networks with an expression pattern characteristic of lateral inhibition. This class is a repertoire of distinct implementations of this key developmental process, the diversity of which provides valuable clues about its underlying causal principles
Shapes in the Shadow: Evolutionary Dynamics of Morphogenesis
This article investigates the evolutionary dynamics
of morphogenesis. In this study, morphogenesis arises as a
side-effect of maximization of number of cell types. Thus, it
investigates the evolutionary dynamics of side-effects.
Morphogenesis is governed by the interplay between
differential cell adhesion, gene-regulation, and intercellular
signaling. Thus, it investigates the potential to generate
complex behavior by entanglement of relatively "boring"
processes, and the (automatic) coordination between these
processes.
The evolutionary dynamics shows all the hallmarks of
evolutionary dynamics governed by nonlinear genotype
phenotype mapping: for example, punctuated equilibria and
diffusion on neutral paths. More striking is the result that
interesting, complex morphogenesis occurs mainly in the
"shadow" of neutral paths which preserve cell differentiation,
that is, the interesting morphologies arise as mutants of the
fittest individuals.
Characteristics of the evolution of such side-effects in the
shadow appear to be the following: (1) The speci?c complex
morphologies are unique (or at least very rare) among the set
of de novo initiated evolutionary histories. (2) Similar
morphologies are reinvented at large temporal distances
during one evolutionary history and also when evolution is
restarted after the main cell differentiation pattern has been
established. (3) A mosaic-like evolution at the morphological
level, where different morphological features occur in many
combinations, while at the genotypic level recombination is
not implemented and genotypes diverge linearly and at a
constant rate
Network design meets in silico evolutionary biology
Cell fate is programmed through gene regulatory networks that perform several calculations to take the appropriate decision. In silico evolutionary optimization mimics the way Nature has designed such gene regulatory networks. In this review we discuss the basic principles of these evolutionary approaches and how they can be applied to engineer synthetic networks. We summarize the basic guidelines to implement an in silico evolutionary design method, the operators for mutation and selection that iteratively drive the network architecture towards a specified dynamical behavior. Interestingly, as it happens in natural evolution, we show the existence of patterns of punctuated evolution. In addition, we highlight several examples of models that have been designed using automated procedures, together with different objective functions to select for the proper behavior. Finally, we briefly discuss the modular designability of gene regulatory networks and its potential application in biotechnology.Supported by fellowships from Generalitat Valenciana and the European Molecular Biology Organization to G. R. and by grants from the Spanish Ministerio de Ciencia e Innovación to J.C. and S.F.E.Peer reviewe
Heterogeneity induces spatiotemporal oscillations in reaction-diffusions systems
We report on a novel instability arising in activator-inhibitor reaction-diffusion (RD) systems with a simple spatial heterogeneity. This instability gives rise to periodic creation, translation, and destruction of spike solutions that are commonly formed due to Turing instabilities. While this behavior is oscillatory in nature, it occurs purely within the Turing space such that no region of the domain would give rise to a Hopf bifurcation for the homogeneous equilibrium. We use the shadow limit of the Gierer-Meinhardt system to show that the speed of spike movement can be predicted from well-known asymptotic theory, but that this theory is unable to explain the emergence of these spatiotemporal oscillations. Instead, we numerically explore this system and show that the oscillatory behavior is caused by the destabilization of a steady spike pattern due to the creation of a new spike arising from endogeneous activator production. We demonstrate that on the edge of this instability, the period of the oscillations goes to infinity, although it does not fit the profile of any well known bifurcation of a limit cycle. We show that nearby stationary states are either Turing unstable, or undergo saddle-node bifurcations near the onset of the oscillatory instability, suggesting that the periodic motion does not emerge from a local equilibrium. We demonstrate the robustness of this spatiotemporal oscillation by exploring small localized heterogeneity, and showing that this behavior also occurs in the Schnakenberg RD model. Our results suggest that this phenomenon is ubiquitous in spatially heterogeneous RD systems, but that current tools, such as stability of spike solutions and shadow-limit asymptotics, do not elucidate understanding. This opens several avenues for further mathematical analysis and highlights difficulties in explaining how robust patterning emerges from Turing's mechanism in the presence of even small spatial heterogeneity
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