Implications of systemic dysfunction for the etiology of malignancy

The current approach to treatment in oncology is to replace the generally cytotoxic chemotherapies with pharmaceutical treatment which inactivates specific molecular targets associated with cancer development and progression. The goal is to limit cellular damage to pathways perceived to be directly responsible for the malignancy. Its underlying assumptions are twofold: (1) that individual pathways are the cause of malignancy; and (2) that the treatment objective should be destruction-either of the tumor or the dysfunctional pathway. However, the extent to which data actually support these assumptions has not been directly addressed. Accumulating evidence suggests that systemic dysfunction precedes the disruption of specific genetic/molecular pathways in most adult cancers and that targeted treatments such as kinase inhibitors may successfully treat one pathway while generating unintended changes to other, non-targeted pathways. This article discusses (1) the systemic basis of malignancy; (2) better profiling of pre-cancerous biomarkers associated with elevated risk so that preventive lifestyle modifications can be instituted early to revert high-risk epigenetic changes before tumors develop; (3) a treatment emphasis in early stage tumors that would target the restoration of systemic balance by strengthening the body's innate defense mechanisms; and (4) establishing better quantitative models of systems to capture adequate complexity for predictability at all stages of tumor progression.

Boolean networks as modeling framework

In a network, the components of a given system are represented as nodes, the interactions are abstracted as links between the nodes. Boolean networks refer to a class of dynamics on networks, in fact it is the simplest possible dynamics where each node has a value 0 or 1. This allows to investigate extensively the dynamics both analytically and by numerical experiments. The present article focuses on the theoretical concept of relevant components and their immediate application in plant biology. References for more in-depth treatment of the mathematical details are also given.

An overview of existing modeling tools making use of model checking in the analysis of biochemical networks

Model checking is a well-established technique for automatically verifying complex systems. Recently, model checkers have appeared in computer tools for the analysis of biochemical (and gene regulatory) networks. We survey several such tools to assess the potential of model checking in computational biology. Next, our overview focuses on direct applications of existing model checkers, as well as on algorithms for biochemical network analysis influenced by model checking, such as those using binary decision diagrams (BDDs) or Boolean-satisfiability solvers. We conclude with advantages and drawbacks of model checking for the analysis of biochemical networks.

Intelligibility in microbial complex systems: Wittgenstein and the score of life

Knowledge in microbiology is reaching an extreme level of diversification and complexity, which paradoxically results in a strong reduction in the intelligibility of microbial life. In our days, the "score of life" metaphor is more accurate to express the complexity of living systems than the classic "book of life." Music and life can be represented at lower hierarchical levels by music scores and genomic sequences, and such representations have a generational influence in the reproduction of music and life. If music can be considered as a representation of life, such representation remains as unthinkable as life itself. The analysis of scores and genomic sequences might provide mechanistic, phylogenetic, and evolutionary insights into music and life, but not about their real dynamics and nature, which is still maintained unthinkable, as was proposed by Wittgenstein. As complex systems, life or music is composed by thinkable and only showable parts, and a strategy of half-thinking, half-seeing is needed to expand knowledge. Complex models for complex systems, based on experiences on trans-hierarchical integrations, should be developed in order to provide a mixture of legibility and imageability of biological processes, which should lead to higher levels of intelligibility of microbial life.

Selection of Mesenchymal-Like Metastatic Cells in Primary Tumors - An in silico Investigation

In order to metastasize, cancer cells must undergo phenotypic transition from an anchorage-dependent form to a motile form via a process referred to as epithelial to mesenchymal transition. It is currently unclear whether metastatic cells emerge late during tumor progression by successive accumulation of mutations, or whether they derive from distinct cell populations already present during the early stages of tumorigenesis. Similarly, the selective pressures that drive metastasis are poorly understood. Selection of cancer cells with increased proliferative capacity and enhanced survival characteristics may explain how some transformations promote a metastatic phenotype. However, it is difficult to explain how cancer cells that disseminate can emerge due to such selective pressure, since these cells usually remain dormant for prolonged periods of time. In the current study, we have used in silico modeling and simulation to investigate the hypothesis that mesenchymal-like cancer cells evolve during the early stages of primary tumor development, and that these cells exhibit survival and proliferative advantages within the tumor microenvironment. In an agent-based tumor microenvironment model, cancer cell agents with distinct sets of attributes governing nutrient consumption, proliferation, apoptosis, random motility, and cell adhesion were allowed to compete for space and nutrients. These simulation data indicated that mesenchymal-like cancer cells displaying high motility and low adhesion proliferate more rapidly and display a survival advantage over epithelial-like cancer cells. Furthermore, the presence of mesenchymal-like cells within the primary tumor influences the macroscopic properties, emergent morphology, and growth rate of tumors.

Complexity in built environment, health, and destination walking: a neighborhood-scale analysis

This study investigates the relationships between the built environment, the physical attributes of the neighborhood, and the residents' perceptions of those attributes. It focuses on destination walking and self-reported health, and does so at the neighborhood scale. The built environment, in particular sidewalks, road connectivity, and proximity of local destinations, correlates with destination walking, and similarly destination walking correlates with physical health. It was found, however, that the built environment and health metrics may not be simply, directly correlated but rather may be correlated through a series of feedback loops that may regulate risk in different ways in different contexts. In particular, evidence for a feedback loop between physical health and destination walking is observed, as well as separate feedback loops between destination walking and objective metrics of the built environment, and destination walking and perception of the built environment. These feedback loops affect the ability to observe how the built environment correlates with residents' physical health. Previous studies have investigated pieces of these associations, but are potentially missing the more complex relationships present. This study proposes a conceptual model describing complex feedback relationships between destination walking and public health, with the built environment expected to increase or decrease the strength of the feedback loop. Evidence supporting these feedback relationships is presented.

Automated detection of proliferative retinopathy in clinical practice

Timely intervention for diabetic retinopathy (DR) lessens the possibility of blindness and can save considerable costs to health systems. To ensure that interventions are timely and effective requires methods of screening and monitoring pathological changes, including assessing outcomes. Fractal analysis, one method that has been studied for assessing DR, is potentially relevant in today’s world of telemedicine because it provides objective indices from digital images of complex patterns such as are seen in retinal vasculature, which is affected in DR. We introduce here a protocol to distinguish between nonproliferative (NPDR) and proliferative (PDR) changes in retinal vasculature using a fractal analysis method known as local connected dimension (D_conn) analysis. The major finding is that compared to other fractal analysis methods, D_conn analysis better differentiates NPDR from PDR (p = 0.05). In addition, we are the first to show that fractal analysis can be used to differentiate between NPDR and PDR using automated vessel identification. Overall, our results suggest this protocol can complement existing methods by including an automated and objective measure obtainable at a lower level of expertise that experts can then use in screening for and monitoring DR.

The non-random brain: efficiency, economy, and complex dynamics

Modern anatomical tracing and imaging techniques are beginning to reveal the structural anatomy of neural circuits at small and large scales in unprecedented detail. When examined with analytic tools from graph theory and network science, neural connectivity exhibits highly non-random features, including high clustering and short path length, as well as modules and highly central hub nodes. These characteristic topological features of neural connections shape non-random dynamic interactions that occur during spontaneous activity or in response to external stimulation. Disturbances of connectivity and thus of neural dynamics are thought to underlie a number of disease states of the brain, and some evidence suggests that degraded functional performance of brain networks may be the outcome of a process of randomization affecting their nodes and edges. This article provides a survey of the non-random structure of neural connectivity, primarily at the large scale of regions and pathways in the mammalian cerebral cortex. In addition, we will discuss how non-random connections can give rise to differentiated and complex patterns of dynamics and information flow. Finally, we will explore the idea that at least some disorders of the nervous system are associated with increased randomness of neural connections.

Physics and complexity

This paper is concerned with complex macroscopic behaviour arising in many-body systems through the combinations of competitive interactions and disorder, even with simple ingredients at the microscopic level. It attempts to indicate and illustrate the richness that has arisen, in conceptual understanding, in methodology and in application, across a large range of scientific disciplines, together with a hint of some of the further opportunities that remain to be tapped. In doing so, it takes the perspective of physics and tries to show, albeit rather briefly, how physics has contributed and been stimulated.

Thermodynamic analysis of biodegradation pathways

Microorganisms provide a wealth of biodegradative potential in the reduction and elimination of xenobiotic compounds in the environment. One useful metric to evaluate potential biodegradation pathways is thermodynamic feasibility. However, experimental data for the thermodynamic properties of xenobiotics is scarce. The present work uses a group contribution method to study the thermodynamic properties of the University of Minnesota Biocatalysis/Biodegradation Database. The Gibbs free energies of formation and reaction are estimated for 914 compounds (81%) and 902 reactions (75%), respectively, in the database. The reactions are classified based on the minimum and maximum Gibbs free energy values, which accounts for uncertainty in the free energy estimates and a feasible concentration range relevant to biodegradation. Using the free energy estimates, the cumulative free energy change of 89 biodegradation pathways (51%) in the database could be estimated. A comparison of the likelihood of the biotransformation rules in the Pathway Prediction System and their thermodynamic feasibility was then carried out. This analysis revealed that when evaluating the feasibility of biodegradation pathways, it is important to consider the thermodynamic topology of the reactions in the context of the complete pathway. Group contribution is shown to be a viable tool for estimating, a priori, the thermodynamic feasibility and the relative likelihood of alternative biodegradation reactions. This work offers a useful tool to a broad range of researchers interested in estimating the feasibility of the reactions in existing or novel biodegradation pathways.

The lexical restructuring hypothesis and graph theoretic analyses of networks based on random lexicons

PURPOSE

The mental lexicon of words used for spoken word recognition has been modeled as a complex network or graph. Do the characteristics of that graph reflect processes involved in its growth (M. S. Vitevitch, 2008) or simply the phonetic overlap between similar-sounding words?

METHOD

Three pseudolexicons were generated by randomly selecting phonological segments from a fixed set. Each lexicon was then modeled as a graph, linking words differing by one segment. The properties of those graphs were compared with those of a graph based on real English words.

RESULTS

The properties of the graphs built from the pseudolexicons matched the properties of the graph based on English words. Each graph consisted of a single large island and a number of smaller islands and hermits. The degree distribution of each graph was better fit by an exponential than by a power function. Each graph showed short path lengths, large clustering coefficients, and positive assortative mixing.

CONCLUSION

The results suggest that there is no need to appeal to processes of growth or language acquisition to explain the formal properties of the network structure of the mental lexicon. These properties emerged because the network was built based on the phonetic overlap of words.

An integrative framework for stochastic, size-structured community assembly

We present a theoretical framework to describe stochastic, size-structured community assembly, and use this framework to make community-level ecological predictions. Our model can be thought of as adding biological realism to Neutral Biodiversity Theory by incorporating size variation and growth dynamics, and allowing demographic rates to depend on the sizes of individuals. We find that the species abundance distribution (SAD) is insensitive to the details of the size structure in our model, demonstrating that the SAD is a poor indicator of size-dependent processes. We also derive the species biomass distribution (SBD) and find that the form of the SBD depends on the underlying size structure. This leads to a prescription for testing multiple, intertwined ecological predictions of the model, and provides evidence that alternatives to the traditional SAD are more closely tied to certain ecological processes. Finally, we describe how our framework may be extended to make predictions for more general types of community structure.

Incipient criticality in ecological communities

In ecology, there have been attempts to establish links between the relative species abundance (RSA), the fraction of species in a community with a given abundance, and a power-law form of the species area relationship (SAR), the dependence of species richness on sampling area. However the SAR and other patterns in ecology often do not exhibit power-law behavior over an appreciable range of scales. This raises the question whether a scaling framework can be applied when the system under analysis does not exhibit power-law behavior. Here, we derive a general finite-size scaling framework applicable to such systems that can be used to identify incipient critical behavior and links the scale dependence of the RSA and the SAR. We confirm the generality of our theory by using data from a serpentine grassland plot, which exhibits a power-law SAR, and the Barro Colorado Island plot in Panama, whose SAR shows deviations from power-law behavior at every scale. Our results demonstrate that scaling provides a model-independent framework for analyzing and unifying ecological data and that, despite the absence of power laws, ecosystems are poised in the vicinity of a critical point.

Findings from an organizational network analysis to support local public health management

We assessed the feasibility of using organizational network analysis in a local public health organization. The research setting was an urban/suburban county health department with 156 employees. The goal of the research was to study communication and information flow in the department and to assess the technique for public health management. Network data were derived from survey questionnaires. Computational analysis was performed with the Organizational Risk Analyzer. Analysis revealed centralized communication, limited interdependencies, potential knowledge loss through retirement, and possible informational silos. The findings suggested opportunities for more cross program coordination but also suggested the presences of potentially efficient communication paths and potentially beneficial social connectedness. Managers found the findings useful to support decision making. Public health organizations must be effective in an increasingly complex environment. Network analysis can help build public health capacity for complex system management.

The meaning of integrated care: a systems approach

Introduction

In all well developed societies, such as those that we live in, there tend to be strong borders or barriers between different organisations and different professions. People with different kinds of knowledge are kept well apart. So how can we—should we—manage health and social services that are located in different organisations? If we are to improve the capability of a health care organisation to function as an integrated part of a locally driven health and social service system, we need a new model. Traditional models view systems as machines. Instead, we perhaps should approach them as constantly changing living organisms. This is the importance of Complexity science. It helps us understand what happens in dynamic living systems, where many agents are interconnected.

Complex adaptive systems (CASs)

The term ‘complex system’ emphasizes that the necessary competence to perform a task is not owned by any one part, but comes as a result of co-operation within the system. ‘Adaptive’ means that system change occurs through successive adaptations. A CAS consists of several subsystems called agents, which act in dependence of one another. They are interdependent. They may either compete or co-operate according to their sense of their interests and what will bring them an advantage. Complex Adaptive Systems are distinguished by self-organisation. Self-organisation is about creating order or increasing the regularity of the system without help from the outside. Good examples would be the ant-hill, the human immune defence, the financial market and the surgical operating theatre team. When we study a CAS, the focus is on the interaction and communication between agents. Contrary to the old cliché, that the whole is greater than the sum of the parts, the whole is the relations between the parts. Order, innovation and progress arise naturally from interactions within a CAS. They do not need to be prescribed from ‘higher’ levels or from the environment. It has been found that for purposes of fostering connectivity among diverse agents, effective coupling of structures, ideas and innovations, and ensuring that they are neither too loose nor too tightly interdependent, complex systems are better led by indirect than by direct leadership behaviours.

Conclusions

The CAS approach helps the management to understand why the traditional top down way of managing may meet with problems in organisations with complex tasks. An important discussion is about how the top management in fact executes its steering function. As a leader in a CAS you will accept complexity instead of trying to reduce it, formulate few simple and concrete goals, communicate and give feedback and measure on performance. As we begin to see health care organisations as CASs we should gain more insight into the processes that go on within and between organisations. But best of all, this organic mental model opens up for greater success in implementing strategies.

Individual basis for collective behaviour in the termite, Cornitermes cumulans

Interactions among individuals in social groups lead to the emergence of collective behaviour at large scales by means of multiplicative non-linear effects. Group foraging, nest building and task allocation are just some well-known examples present in social insects. However the precise mechanisms at the individual level that trigger and amplify social phenomena are not fully understood. Here we show evidence of complex dynamics in groups of the termite, Cornitermes cumulans (Kollar) (Isoptera: Termitidae), of different sizes and qualitatively compare the behaviour observed with that exhibited by agent-based computer models. It is then concluded that certain aspects of social behaviour in insects have a universal basis common to interconnected systems and that this may be useful for understanding the temporal dynamics of systems displaying social behaviour in general.

High-precision frequency measurements: indispensable tools at the core of the molecular-level analysis of complex systems

This perspective article provides an assessment of the state-of-the-art in the molecular-resolution analysis of complex organic materials. These materials can be divided into biomolecules in complex mixtures (which are amenable to successful separation into unambiguously defined molecular fractions) and complex nonrepetitive materials (which cannot be purified in the conventional sense because they are even more intricate). Molecular-level analyses of these complex systems critically depend on the integrated use of high-performance separation, high-resolution organic structural spectroscopy and mathematical data treatment. At present, only high-precision frequency-derived data exhibit sufficient resolution to overcome the otherwise common and detrimental effects of intrinsic averaging, which deteriorate spectral resolution to the degree of bulk-level rather than molecular-resolution analysis. High-precision frequency measurements are integral to the two most influential organic structural spectroscopic methods for the investigation of complex materials-NMR spectroscopy (which provides unsurpassed detail on close-range molecular order) and FTICR mass spectrometry (which provides unrivalled resolution)-and they can be translated into isotope-specific molecular-resolution data of unprecedented significance and richness. The quality of this standalone de novo molecular-level resolution data is of unparalleled mechanistic relevance and is sufficient to fundamentally advance our understanding of the structures and functions of complex biomolecular mixtures and nonrepetitive complex materials, such as natural organic matter (NOM), aerosols, and soil, plant and microbial extracts, all of which are currently poorly amenable to meaningful target analysis. The discrete analytical volumetric pixel space that is presently available to describe complex systems (defined by NMR, FT mass spectrometry and separation technologies) is in the range of 10(8-14) voxels, and is therefore capable of providing the necessary detail for a meaningful molecular-level analysis of very complex mixtures. Nonrepetitive complex materials exhibit mass spectral signatures in which the signal intensity often follows the number of chemically feasible isomers. This suggests that even the most strongly resolved FTICR mass spectra of complex materials represent simplified (e.g. isomer-filtered) projections of structural space.

Biological solutions to transport network design

Transport networks are vital components of multicellular organisms, distributing nutrients and removing waste products. Animal and plant transport systems are branching trees whose architecture is linked to universal scaling laws in these organisms. In contrast, many fungi form reticulated mycelia via the branching and fusion of thread-like hyphae that continuously adapt to the environment. Fungal networks have evolved to explore and exploit a patchy environment, rather than ramify through a three-dimensional organism. However, there has been no explicit analysis of the network structures formed, their dynamic behaviour nor how either impact on their ecological function. Using the woodland saprotroph Phanerochaete velutina, we show that fungal networks can display both high transport capacity and robustness to damage. These properties are enhanced as the network grows, while the relative cost of building the network decreases. Thus, mycelia achieve the seemingly competing goals of efficient transport and robustness, with decreasing relative investment, by selective reinforcement and recycling of transport pathways. Fungal networks demonstrate that indeterminate, decentralized systems can yield highly adaptive networks. Understanding how these relatively simple organisms have found effective transport networks through a process of natural selection may inform the design of man-made networks.

Structure and tie strengths in mobile communication networks

Electronic databases, from phone to e-mails logs, currently provide detailed records of human communication patterns, offering novel avenues to map and explore the structure of social and communication networks. Here we examine the communication patterns of millions of mobile phone users, allowing us to simultaneously study the local and the global structure of a society-wide communication network. We observe a coupling between interaction strengths and the network's local structure, with the counterintuitive consequence that social networks are robust to the removal of the strong ties but fall apart after a phase transition if the weak ties are removed. We show that this coupling significantly slows the diffusion process, resulting in dynamic trapping of information in communities and find that, when it comes to information diffusion, weak and strong ties are both simultaneously ineffective.

Stomatal patchiness and task-performing networks

BACKGROUND

Patchy stomatal conductance is a poorly understood and little-studied phenomenon. It is relatively common, yet it appears to be detrimental to water-use efficiency under some conditions and has no immediately obvious physiological function of any kind. Much of the difficulty in studying patchy stomatal conductance is tied to its unpredictability, both in occurrence and in characteristics.

SCOPE AND CONCLUSIONS

Statistical analyses of the variability of stomatal patchiness reveal remarkable similarities to structures and behaviours found in locally connected networks of dynamic units that perform tasks. Such systems solve problems that reside at the level of the entire network despite the absence of a central processor or a mechanism for directly sharing information over the entire system. Frequently, task performance is emergent, in the sense that no unit independently performs the task. Because each unit in the network can communicate with only its immediate neighbours, problem solving is accomplished by the states of the individual units self-organizing into synchronized, collective patterns. In some cases, patches of states form and move coherently over the network, thus providing a means for distantly separated parts of the network to communicate. Often, exactly what form these patches take and how they move as the units synchronize is highly unpredictable. In analogy with such networks, it is suggested that stomatal patchiness may be a signature that plants optimize gas exchange in a more sophisticated and adaptive manner than if performed by their individual stomata independently.

Spin glasses and fragile glasses: statics, dynamics, and complexity

In this paper I will briefly review some theoretical results that have been obtained in recent years for spin glasses and fragile glasses. I will concentrate my attention on the predictions coming from the so called broken replica symmetry approach and on their experimental verifications. I will also mention the relevance or these results for other fields, and in general for complex systems.

The role of the airline transportation network in the prediction and predictability of global epidemics

The systematic study of large-scale networks has unveiled the ubiquitous presence of connectivity patterns characterized by large-scale heterogeneities and unbounded statistical fluctuations. These features affect dramatically the behavior of the diffusion processes occurring on networks, determining the ensuing statistical properties of their evolution pattern and dynamics. In this article, we present a stochastic computational framework for the forecast of global epidemics that considers the complete worldwide air travel infrastructure complemented with census population data. We address two basic issues in global epidemic modeling: (i) we study the role of the large scale properties of the airline transportation network in determining the global diffusion pattern of emerging diseases; and (ii) we evaluate the reliability of forecasts and outbreak scenarios with respect to the intrinsic stochasticity of disease transmission and traffic flows. To address these issues we define a set of quantitative measures able to characterize the level of heterogeneity and predictability of the epidemic pattern. These measures may be used for the analysis of containment policies and epidemic risk assessment.