Two design patterns for visualising the parameter space of complex systems

A key feature of complex systems is that their behaviour can vary significantly depending on their location in parameter space. A major challenge for researchers is to understand how combinations of system parameters influence behaviour; that is, to understand the shape of parameter space. Tools for visualising the structure and dynamics of complex systems and the shape of their parameter spaces play an important role in addressing this challenge. Many of these tools are developed to address problems in specific domains. If complex systems share certain general properties that transcend their specific domain, it should be possible to share tools for understanding these systems between domains. One technique that has been proposed for achieving this is the use of design patterns

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

The Lords of Strategy: The Secret History of the new Corporate World

Walter Kiechel III, Boston, Harvard Business Press, 2010, ISBN 13: 978-1-59139-782-3 (hardcover), 347 pages, USD 26.95, AUD 53.95, GBP 18.99

Association of variants in APOL1, MYH9 and HMOX1 WITH micro-Albuminuria among Sickle Cell disease patients from Cameroon

Introduction: Sickle Cell Disease (SCD) is a monogenic, multi-organ hemoglobinopathy disorder that is highly prevalent in Africa, with nearly 300 000 newborn cases per year. The underlying pathophysiological mechanism of the disease involves alteration of the normal soft and biconcave disc shape of erythrocytes, to that of a rigid crescent. These abnormal red blood cells cause vaso-occlusion and intravascular hemolysis, resulting in a variety of clinical manifestations, including acute pain crises, anemia, and damage to various organs. Kidney disease is a clinical proxy of severity, developing only in a subset of patients, and is subject to modification by genetic variations. Indeed, reports have shown significant association between proteinuria and specific genetic variants in MYH9 and APOL1, and between estimated Glomerular Filtration Rate (eGFR) and End Stage Kidney Disease (ESKD) with HMOX1 variants among adult African Americans affected by SCD. However, the association between these variants and micro-albuminuria, a primary indicator of renal dysfunction, has not been investigated, nor has any study of these variants been performed among SCD patients in Africa. Aim: The aim of this study was to investigate the association of targeted single nucleotide polymorphisms (SNPs) in APOL1, MYH9 and HMOX1, as well as a 5' promoter dinucleotide repeat in HMOX1, with micro-albuminuria among SCD patients from Cameroon; and to compare the results to that from a cohort of non-SCD Cameroonian individuals affected by ESKD

Artificial Ontogenies: A Computational Model of the Control and Evolution of Development

Understanding the behaviour of biological systems is a challenging task. Gene regulation, development and evolution are each a product of nonlinear interactions between many individual agents: genes, cells or organisms. Moreover, these three processes are not isolated, but interact with one another in an important fashion. The development of an organism involves complex patterns of dynamic behaviour at the genetic level. The gene networks that produce this behaviour are subject to mutations that can alter the course of development, resulting in the production of novel morphologies. Evolution occurs when these novel morphologies are favoured by natural selection and survive to pass on their genes to future generations. Computational models can assist us to understand biological systems by providing a framework within which their behaviour can be explored. Many natural processes, including gene regulation and development, have a computational element to their control. Constructing formal models of these systems enables their behaviour to be simulated, observed and quantified on a scale not otherwise feasible. This thesis uses a computational simulation methodology to explore the relationship between development and evolution. An important question in evolutionary biology is how to explain the direction of evolution. Conventional explanations of evolutionary history have focused on the role of natural selection in orienting evolution. More recently, it has been argued that the nature of development, and the way it changes in response to mutation, may also be a significant factor. A network-lineage model of artificial ontogenies is described that incorporates a developmental mapping between the dynamics of a gene network and a cell lineage representation of a phenotype. Three series of simulation studies are reported, exploring: (a) the relationship between the structure of a gene network and its dynamic behaviour; (b) the characteristic distributions of ontogenies and phenotypes generated by the dynamics of gene networks; (c) the effect of these characteristic distributions on the evolution of ontogeny. The results of these studies indicate that the model networks are capable of generating a diverse range of stable behaviours, and possess a small yet significant sensitivity to perturbation. In the context of developmental control, the intrinsic dynamics of the model networks predispose the production of ontogenies with a modular, quasi-systematic structure. This predisposition is reflected in the structure of variation available for selection in an adaptive search process, resulting in the evolution of ontogenies biased towards simplicity. These results suggest a possible explanation for the levels of ontogenetic complexity observed in biological organisms: that they may be a product of the network architecture of developmental control. By quantifying complexity, variation and bias, the network-lineage model described in this thesis provides a computational method for investigating the effects of development on the direction of evolution. In doing so, it establishes a viable framework for simulating computational aspects of complex biological systems

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

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