4 research outputs found
The construction of transcription factor networks through natural selection
Transcription regulation plays a key role in determining cellular function, response to external
stimuli and development. Regulatory proteins orchestrate gene expression through thousands of
interactions resulting in large, complex networks. Understanding the principles on which these
networks are constructed can provide insight into the way the expression patterns of different
genes co-evolve.
One method by which this question can be addressed is to focus on the evolution of the structure
of transcription factor networks (TFNs). In order to do this, a model for their evolution through
cis mutation, trans mutation, gene duplication and gene deletion is constructed. This model is
used to determine the circumstances under which the asymmetrical in and out degree distributions
observed in real networks are reproduced. In this way it is possible to draw conclusions about the
contributions of these different evolutionary processes to the evolution of TFNs. Conclusions are
also drawn on the way rates of evolution vary with the position of gene in the network.
Following this, the contributions of cis mutations, which occur in the promoters of regulated
genes, and trans mutations, which occur in the coding reign of transcription factors, to the evolution
of TFNs are investigated. A space of neutral genotypes is constructed, and the evolution of TFNs
through cis and trans mutations in this space is characterised. The results are then used to account
for large scale rewiring observed in the yeast sex determination network.
Finally the principles governing the evolution of autoregulatory motifs are investigated. It is
shown that negative autoregulation, which functions as a noise reduction mechanism in haploid
TFNs, is not evolvable in diploid TFNs. This is attributed to the effects of dominance in diploid
TFNs. The fate of duplicates of autoregulating genes in haploid networks is also investigated. It
is shown that such duplicates are especially prone to loss of function mutations. This is used to
account for the lack of observed autoregulatory duplicates participating in network motifs.
From this work, it is concluded that the relative rates of different evolutionary processes are responsible for shaping the global statistical properties of TFN structure. However, the more
detailed TFN structure, such as network motif distribution, is strongly influenced by the population
genetic details of the system being considered. In addition, extensive neutral evolution is shown to
be possible in TFNs. However, the effects of neutral evolution on network structure are shown to
depend strongly on the structure of the space on neutral genotypes in which the TFN is evolving
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Adaptive and Non-Adaptive Evolution of the Control of Gene Expression
Non-adaptive evolution refers to evolutionary processes that are primarily driven not by natural selection, but by factors such as a bias towards generating certain mutations over others. Although non-adaptive evolution is supported by abundant data, it is obscure outside the field of evolutionary biology, potentially for historical reasons. Considering non-adaptive evolution helps us to understand the origins and roles of traits at molecular and cellular levels, where research is often dominated by adaptationist assumptions. To demonstrate that a balanced view on evolution is necessary, my thesis research asks how adaptive and non-adaptive evolution shape the control of gene expression. I start by simulating the evolution of mechanisms for quality control of gene expression. I show that the error rate associated with gene expression is determined by both the mutational bias that tends to increase the error rate and by the effective population size of the species, which determines the strength of natural selection on the error rate. This offers an explanation for the observed non-monotonic relationship between transcriptional error rate and effective population size. I next study the evolution of transcriptional regulatory networks (TRNs). The adaptationist view hypothesizes that the enrichment of a subnetwork called coherent type 1 feed-forward loops (C1-FFLs) in TRNs is an adaptation for filtering out short spurious signals, but this and similar hypotheses about other enriched subnetworks are widely questioned by evolutionary biologists, because the adaptive hypothesis fails to consider network topologies that evolve non-adaptively. To help resolve this debate, I developed a highly mechanistic computational model that captures non-adaptive factors that can shape the topology of TRNs. I show that functional C1-FFLs evolve readily under selection for filtering out a spurious signal, but not under control selection conditions. While this result supports the adaptive origin of C1-FFLs, I show that non-adaptive subnetworks can also be enriched in TRNs evolved for filtering out a spurious signal, suggesting that inferring functions of TRNs from topology alone can be problematic. A further complication comes from the fact that a subnetwork that is topologically different from C1-FFLs also evolves to filter out spurious signals. In conclusion, I argue that non-adaptive evolution can explain the origins and roles of traits that are difficult to understand under adaptationism, and that considering non-adaptive evolution is necessary to carry out scientific research in all fields of biology. Molecular and cellular biologists should actively consider non-adaptive evolution in their research