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

Crosstalk and the Dynamical Modularity of Feed-Forward Loops in Transcriptional Regulatory Networks

Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to transcriptional regulatory networks, yet such properties are typically determined from their isolated study. We characterize the effects of crosstalk on FFL dynamics by modeling the cross regulation between two different FFLs and evaluate the extent to which these patterns occur in vivo. Analytical modeling suggests that crosstalk should overwhelmingly affect individual protein-expression dynamics. Counter to this expectation we find that entire FFLs are more likely than expected to resist the effects of crosstalk (approximate to 20% for one crosstalk interaction) and remain dynamically modular. The likelihood that cross-linked FFLs are dynamically correlated increases monotonically with additional crosstalk, but is independent of the specific regulation type or connectivity of the interactions. Just one additional regulatory interaction is sufficient to drive the FFL dynamics to a statistically different state. Despite the potential for modularity between sparsely connected network motifs, Escherichia coli (E. coli) appears to favor crosstalk wherein at least one of the cross-linked FFLs remains modular. A gene ontology analysis reveals that stress response processes are significantly overrepresented in the cross-linked motifs found within E. coli. Although the daunting complexity of biological networks affects the dynamical properties of individual network motifs, some resist and remain modular, seemingly insulated from extrinsic perturbations-an intriguing possibility for nature to consistently and reliably provide certain network functionalities wherever the need arise

Origin of bistability underlying mammalian cell cycle entry

Mammalian cell cycle entry is controlled at the restriction point by a bistable and resettable switch, which is shown to emerge from a minimal gene circuit containing a mutual-inhibition feedback loop between Rb and E2F modules, coupled with a feed-forward loop between Myc and E2F modules

Ageing as a price of cooperation and complexity: Self-organization of complex systems causes the ageing of constituent networks

The analysis of network topology and dynamics is increasingly used for the description of the structure, function and evolution of complex systems. Here we summarize key aspects of the evolvability and robustness of the hierarchical network-set of macromolecules, cells, organisms, and ecosystems. Listing the costs and benefits of cooperation as a necessary behaviour to build this network hierarchy, we outline the major hypothesis of the paper: the emergence of hierarchical complexity needs cooperation leading to the ageing of the constituent networks. Local cooperation in a stable environment may lead to over-optimization developing an ‘always-old’ network, which ages slowly, and dies in an apoptosis-like process. Global cooperation by exploring a rapidly changing environment may cause an occasional over-perturbation exhausting system-resources, causing rapid degradation, ageing and death of an otherwise ‘forever-young’ network in a necrosis-like process. Giving a number of examples we explain how local and global cooperation can both evoke and help successful ageing. Finally, we show how various forms of cooperation and consequent ageing emerge as key elements in all major steps of evolution from the formation of protocells to the establishment of the globalized, modern human society. Thus, ageing emerges as a price of complexity, which is going hand-in-hand with cooperation enhancing each other in a successful community

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 evolution of phenotypic correlations and “developmental memory”

Development introduces structured correlations among traits that may constrain or bias the distribution of phenotypes produced. Moreover, when suitable heritable variation exists, natural selection may alter such constraints and correlations, affecting the phenotypic variation available to subsequent selection. However, exactly how the distribution of phenotypes produced by complex developmental systems can be shaped by past selective environments is poorly understood. Here we investigate the evolution of a network of recurrent nonlinear ontogenetic interactions, such as a gene regulation network, in various selective scenarios. We find that evolved networks of this type can exhibit several phenomena that are familiar in cognitive learning systems. These include formation of a distributed associative memory that can “store” and “recall” multiple phenotypes that have been selected in the past, recreate complete adult phenotypic patterns accurately from partial or corrupted embryonic phenotypes, and “generalize” (by exploiting evolved developmental modules) to produce new combinations of phenotypic features. We show that these surprising behaviors follow from an equivalence between the action of natural selection on phenotypic correlations and associative learning, well-understood in the context of neural networks. This helps to explain how development facilitates the evolution of high-fitness phenotypes and how this ability changes over evolutionary time

Structural Discrimination of Robustness in Transcriptional Feedforward Loops for Pattern Formation

Signaling pathways are interconnected to regulatory circuits for sensing the environment and expressing the appropriate genetic profile. In particular, gradients of diffusing molecules (morphogens) determine cell fate at a given position, dictating development and spatial organization. The feedforward loop (FFL) circuit is among the simplest genetic architectures able to generate one-stripe patterns by operating as an amplitude detection device, where high output levels are achieved at intermediate input ones. Here, using a heuristic optimization-based approach, we dissected the design space containing all possible topologies and parameter values of the FFL circuits. We explored the ability of being sensitive or adaptive to variations in the critical morphogen level where cell fate is switched. We found four different solutions for precision, corresponding to the four incoherent architectures, but remarkably only one mode for adaptiveness, the incoherent type 4 (I4-FFL). We further carried out a theoretical study to unveil the design principle for such structural discrimination, finding that the synergistic action and cooperative binding on the downstream promoter are instrumental to achieve absolute adaptive responses. Subsequently, we analyzed the robustness of these optimal circuits against perturbations in the kinetic parameters and molecular noise, which has allowed us to depict a scenario where adaptiveness, parameter sensitivity and noise tolerance are different, correlated facets of the robustness of the I4-FFL circuit. Strikingly, we showed a strong correlation between the input (environment-related) and the intrinsic (mutation-related) susceptibilities. Finally, we discussed the evolution of incoherent regulations in terms of multifunctionality and robustness

Transcriptional robustness and protein interactions are associated in yeast

BackgroundRobustness to insults, both external and internal, is a characteristic feature of life. One level of biological organization for which noise and robustness have been extensively studied is gene expression. Cells have a variety of mechanisms for buffering noise in gene expression, but it is not completely clear what rules govern whether or not a given gene uses such tools to maintain appropriate expression.ResultsHere, we show a general association between the degree to which yeast cells have evolved mechanisms to buffer changes in gene expression and whether they possess protein-protein interactions. We argue that this effect bears an affinity to epistasis, because yeast appears to have evolved regulatory mechanisms such that distant changes in gene copy number for a protein-protein interaction partner gene can alter a gene's expression. This association is not unexpected given recent work linking epistasis and the deleterious effects of changes in gene dosage (i.e., the dosage balance hypothesis). Using gene expression data from artificial aneuploid strains of bakers' yeast, we found that genes coding for proteins that physically interact with other proteins show less expression variation in response to aneuploidy than do other genes. This effect is even more pronounced for genes whose products interact with proteins encoded on aneuploid chromosomes. We further found that genes targeted by transcription factors encoded on aneuploid chromosomes were more likely to change in expression after aneuploidy.ConclusionsWe suggest that these observations can be best understood as resulting from the higher fitness cost of misexpression in epistatic genes and a commensurate greater regulatory control of them

MicroRNAs and metazoan macroevolution: insights into canalization, complexity, and the Cambrian explosion

One of the most interesting challenges facing paleobiologists is explaining the Cambrian explosion, the dramatic appearance of most metazoan animal phyla in the Early Cambrian, and the subsequent stability of these body plans over the ensuing 530 million years. We propose that because phenotypic variation decreases through geologic time, because microRNAs (miRNAs) increase genic precision, by turning an imprecise number of mRNA transcripts into a more precise number of protein molecules, and because miRNAs are continuously being added to metazoan genomes through geologic time, miRNAs might be instrumental in the canalization of development. Further, miRNAs ultimately allow for natural selection to elaborate morphological complexity, because by reducing gene expression variability, miRNAs increase heritability, allowing selection to change characters more effectively. Hence, miRNAs might play an important role in shaping metazoan macroevolution, and might be part of the solution to the Cambrian conundrum

Payments Failure

The processing of retail payments traditionally has been the domain of regulated banks, but technologically sophisticated players like Venmo, AliPay, Bitcoin, and Ripple, and potentially, Facebook’s Libra, are making incursions into the market. Even within regulated banks, payments processing is becoming increasingly reliant on new technologies—JPMorgan Chase’s “JPMCoin” is just one example. Limited attention, however, has been paid to the new kinds of operational risks associated with these methods of processing retail payments. This Article argues that technological failures at a payments provider—either a bank or non-bank—could be amplified in unexpected ways as such failures interact with technological failures at other payments providers. In a worst-case scenario, a cascading failure of payments technologies could cause significant parts of the retail payments system to shut down—an eventuality that would harm the broader economy if people were unable to transact for a prolonged period of time. This Article is the first to raise the possibility of a financial crisis precipitated primarily by operational failures. Such a crisis would look more like a rolling blackout than a bank run. Because of this possibility, this Article argues that it is insufficient to approach the risk of payments failure with a purely prudential strategy. This Article therefore makes the case for a complementary “macro-operational” approach to regulation, rooted in complexity theory, to deal with the possibility that the systemic interactions of operational risks could hobble our retail payments system—and the broader economy. Using this framework, this Article analyzes the potential threats posed by different technologies and business models to the orderly functioning of our retail payments system. Further, this Article suggests the beginnings of what proactive macro-operational regulation of the retail payments system might look like