Reconstructing phylogeny from RNA secondary structure via simulated evolution

DNA sequences of genes encoding functional RNA molecules (e.g., ribosomal RNAs) are commonly used in phylogenetics (i.e. to infer evolutionary history). Trees derived from ribosomal RNA (rRNA) sequences, however, are inconsistent with other molecular data in investigations of deep branches in the tree of life. Since much of te functional constraints on the gene products (i.e. RNA molecules) relate to three-dimensional structure, rather than their actual sequences, accumulated mutations in the gene sequences may obscure phylogenetic signal over very large evolutionary time-scales. Variation in structure, however, may be suitable for phylogenetic inference even under extreme sequence divergence. To evaluate qualitatively the manner in which structural evolution relates to sequence change, we simulated the evolution of RNA sequences under various constraints on structural change

Modelling gene regulatory networks: systems biology to complex systems

Draft literature review on approaches to modelling gene regulatory networks

Developmental motifs reveal complex structure in cell lineages

Many natural and technological systems are complex, with organisational structures that exhibit characteristic patterns, but defy concise description. One effective approach to analysing such systems is in terms of repeated topological motifs. Here, we extend the motif concept to characterise the dynamic behaviour of complex systems by introducing developmental motifs, which capture patterns of system growth. As a proof of concept, we use developmental motifs to analyse the developmental cell lineage of the nematode Caenorhabditis elegans, revealing a new perspective on its complex structure. We use a family of computational models to explore how biases arising from the dynamics of the developmental gene network, as well as spatial and temporal constraints acting on development, contribute to this complex organisation

Self-organising agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes

USING TRANSACTION COST ECONOMICS SAFEGUARDING TO REDUCE THE DIFFUSION OF DISINFORMATION ON SOCIAL MEDIA

Human users contribute to the spread of disinformation on Social Media. To reduce the spread, we apply Transaction Cost Economics (TCE) Safeguarding, which penalises the sharing of disinformation. Using the economic theory TCE positions Social Media platforms as free markets, in which actors are motivated to protect their assets and peer reputation. We conducted a study exploring TCE Safeguarding as a market correction mechanism to change the disinformation diffusion behaviour of users. Our findings show that users will be less likely to post a comment and more likely to correct their previous disinformation diffusion actions when TCE Safeguarding is applied. Focusing on Social Media as a market rather than its individual components may provide a mechanism to address the fake news phenomenon

USING TRANSACTION COST ECONOMICS SAFEGUARDING TO REDUCE THE DIFFUSION OF DISINFORMATION ON SOCIAL MEDIA

Human users contribute to the spread of disinformation on Social Media. To reduce the spread, we apply Transaction Cost Economics (TCE) Safeguarding, which penalises the sharing of disinformation. Using the economic theory TCE positions Social Media platforms as free markets, in which actors are motivated to protect their assets and peer reputation. We conducted a study exploring TCE Safeguarding as a market correction mechanism to change the disinformation diffusion behaviour of users. Our findings show that users will be less likely to post a comment and more likely to correct their previous disinformation diffusion actions when TCE Safeguarding is applied. Focusing on Social Media as a market rather than its individual components may provide a mechanism to address the fake news phenomenon

Emergence of heterogeneity and political organization in information exchange networks

We present a simple model of the emergence of the division of labor and the development of a system of resource subsidy from an agent-based model of directed resource production with variable degrees of trust between the agents. The model has three distinct phases, corresponding to different forms of societal organization: disconnected (independent agents), homogeneous cooperative (collective state), and inhomogeneous cooperative (collective state with a leader). Our results indicate that such levels of organization arise generically as a collective effect from interacting agent dynamics, and may have applications in a variety of systems including social insects and microbial communities.Comment: 10 pages, 6 figure

Bow-tie architecture of gene regulatory networks in species of varying complexity

The gene regulatory network (GRN) architecture plays a key role in explaining the biological differences between species. We aim to understand species differences in terms of some universally present dynamical properties of their gene regulatory systems. A network architectural feature associated with controlling system-level dynamical properties is the bow-tie, identified by a strongly connected subnetwork, the core layer, between two sets of nodes, the in and the out layers. Though a bow-tie architecture has been observed in many networks, its existence has not been extensively investigated in GRNs of species of widely varying biological complexity. We analyse publicly available GRNs of several well-studied species from prokaryotes to unicellular eukaryotes to multicellular organisms. In their GRNs, we find the existence of a bow-tie architecture with a distinct largest strongly connected core layer. We show that the bow-tie architecture is a characteristic feature of GRNs. We observe an increasing trend in the relative core size with species complexity. Using studied relationships of the core size with dynamical properties like robustness and fragility, flexibility, criticality, controllability and evolvability, we hypothesize how these regulatory system properties have emerged differently with biological complexity, based on the observed differences of the GRN bow-tie architectures

Three Preventative Interventions to Address the Fake News Phenomenon on Social Media

Fake news on social media undermines democracies and civil society. To date the research response has been message centric and reactive. This does not address the problem of fake news contaminating social media populations with disinformation, nor address the fake news producers and disseminators who are predominantly human social media users. Our research proposes three preventative interventions - two that empower social media users and one social media structural change to reduce the spread of fake news. Specifically, we investigate how i) psychological inoculation; ii) digital media literacy and iii) Transaction Cost Economy safeguarding through reputation ranking could elicit greater cognitive elaboration from social media users. Our research provides digital scalable preventative interventions to empower social media users with the aim to reduce the population size exposed to fake news