Evolvability of feed-forward loop architecture biases its abundance in transcription networks

Background: Transcription networks define the core of the regulatory machinery of cellular life and are largely responsible for information processing and decision making. At the small scale, interaction motifs have been characterized based on their abundance and some seemingly general patterns have been described. In particular, the abundance of different feed-forward loop motifs in gene regulatory networks displays systematic biases towards some particular topologies, which are much more common than others. The causative process of this pattern is still matter of debate. Results: We analyzed the entire motif-function landscape of the feed-forward loop using the formalism developed in a previous work. We evaluated the probabilities to implement possible functions for each motif and found that the kurtosis of these distributions correlate well with the natural abundance pattern. Kurtosis is a standard measure for the peakedness of probability distributions. Furthermore, we examined the functional robustness of the motifs facing mutational pressure in silico and observed that the abundance pattern is biased by the degree of their evolvability. Conclusions: The natural abundance pattern of the feed-forward loop can be reconstructed concerning its intrinsic plasticity. Intrinsic plasticity is associated to each motif in terms of its capacity of implementing a repertoire of possible functions and it is directly linked to the motif's evolvability. Since evolvability is defined as the potential phenotypic variation of the motif upon mutation, the link plausibly explains the abundance pattern.This work was supported by the EU grant CELLCOMPUT, the EU 6th Framework project SYNLET (NEST 043312), the James McDonnell Foundation, the Marcelino Botín Foundation, the University of Vienna and by the Santa Fe Institut

Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks

The gene regulatory network (GRN) is the central decision‐making module of the cell. We have developed a theory called Buffered Qualitative Stability (BQS) based on the hypothesis that GRNs are organised so that they remain robust in the face of unpredictable environmental and evolutionary changes. BQS makes strong and diverse predictions about the network features that allow stable responses under arbitrary perturbations, including the random addition of new connections. We show that the GRNs of E. coli, M. tuberculosis, P. aeruginosa, yeast, mouse, and human all verify the predictions of BQS. BQS explains many of the small- and large‐scale properties of GRNs, provides conditions for evolvable robustness, and highlights general features of transcriptional response. BQS is severely compromised in a human cancer cell line, suggesting that loss of BQS might underlie the phenotypic plasticity of cancer cells, and highlighting a possible sequence of GRN alterations concomitant with cancer initiation. DOI: http://dx.doi.org/10.7554/eLife.02863.00

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

A meta-analysis of Boolean network models reveals design principles of gene regulatory networks

Gene regulatory networks (GRNs) describe how a collection of genes governs the processes within a cell. Understanding how GRNs manage to consistently perform a particular function constitutes a key question in cell biology. GRNs are frequently modeled as Boolean networks, which are intuitive, simple to describe, and can yield qualitative results even when data is sparse. We generate an expandable database of published, expert-curated Boolean GRN models, and extracted the rules governing these networks. A meta-analysis of this diverse set of models enables us to identify fundamental design principles of GRNs. The biological term canalization reflects a cell's ability to maintain a stable phenotype despite ongoing environmental perturbations. Accordingly, Boolean canalizing functions are functions where the output is already determined if a specific variable takes on its canalizing input, regardless of all other inputs. We provide a detailed analysis of the prevalence of canalization and show that most rules describing the regulatory logic are highly canalizing. Independent from this, we also find that most rules exhibit a high level of redundancy. An analysis of the prevalence of small network motifs, e.g. feed-forward loops or feedback loops, in the wiring diagram of the identified models reveals several highly abundant types of motifs, as well as a surprisingly high overabundance of negative regulations in complex feedback loops. Lastly, we provide the strongest evidence thus far in favor of the hypothesis that GRNs operate at the critical edge between order and chaos.Comment: 12 pages, 8 figure

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

Evolutionarily stable and fragile modules of yeast biochemical network

Gene and protein interaction networks have evolved to precisely specify cell fates and functions. Here, we analyse whether the architecture of these networks affects evolvability. We find evidence to suggest that in yeast these networks are mainly acyclic, and that evolutionary changes in these parts do not affect their global dynamic properties. In contrast, feedback loops strongly influence dynamic behaviour and are often evolutionarily conserved. Feedback loops are often found to reside in a clustered manner by means of coupling and nesting with each other in the molecular interaction network of yeast. In these clusters some feedback mechanisms are biologically vital for the operation of the module and some provide auxiliary functional assistance. We find that the biologically vital feedback mechanisms are highly conserved in both transcription regulation and protein interaction network of yeast. In particular, long feedback loops and oscillating modules in protein interaction networks are found to be biologically vital and hence highly conserved. These data suggest that biochemical networks evolve differentially depending on their structure with acyclic parts being permissive to evolution while cyclic parts tend to be conserved

Structure and topology of transcriptional regulatory networks and their applications in bio-inspired networking

Biological networks carry out vital functions necessary for sustenance despite environmental adversities. Transcriptional Regulatory Network (TRN) is one such biological network that is formed due to the interaction between proteins, called Transcription Factors (TFs), and segments of DNA, called genes. TRNs are known to exhibit functional robustness in the face of perturbation or mutation: a property that is proven to be a result of its underlying network topology. In this thesis, we first propose a three-tier topological characterization of TRN to analyze the interplay between the significant graph-theoretic properties of TRNs such as scale-free out-degree distribution, low graph density, small world property and the abundance of subgraphs called motifs. Specifically, we pinpoint the role of a certain three-node motif, called Feed Forward Loop (FFL) motif in topological robustness as well as information spread in TRNs. With the understanding of the TRN topology, we explore its potential use in design of fault-tolerant communication topologies. To this end, we first propose an edge rewiring mechanism that remedies the vulnerability of TRNs to the failure of well-connected nodes, called hubs, while preserving its other significant graph-theoretic properties. We apply the rewired TRN topologies in the design of wireless sensor networks that are less vulnerable to targeted node failure. Similarly, we apply the TRN topology to address the issues of robustness and energy-efficiency in the following networking paradigms: robust yet energy-efficient delay tolerant network for post disaster scenarios, energy-efficient data-collection framework for smart city applications and a data transfer framework deployed over a fog computing platform for collaborative sensing --Abstract, page iii