Dynamics on expanding spaces: modeling the emergence of novelties

Novelties are part of our daily lives. We constantly adopt new technologies, conceive new ideas, meet new people, experiment with new situations. Occasionally, we as individuals, in a complicated cognitive and sometimes fortuitous process, come up with something that is not only new to us, but to our entire society so that what is a personal novelty can turn into an innovation at a global level. Innovations occur throughout social, biological and technological systems and, though we perceive them as a very natural ingredient of our human experience, little is known about the processes determining their emergence. Still the statistical occurrence of innovations shows striking regularities that represent a starting point to get a deeper insight in the whole phenomenology. This paper represents a small step in that direction, focusing on reviewing the scientific attempts to effectively model the emergence of the new and its regularities, with an emphasis on more recent contributions: from the plain Simon's model tracing back to the 1950s, to the newest model of Polya's urn with triggering of one novelty by another. What seems to be key in the successful modelling schemes proposed so far is the idea of looking at evolution as a path in a complex space, physical, conceptual, biological, technological, whose structure and topology get continuously reshaped and expanded by the occurrence of the new. Mathematically it is very interesting to look at the consequences of the interplay between the "actual" and the "possible" and this is the aim of this short review.Comment: 25 pages, 10 figure

The origin of large molecules in primordial autocatalytic reaction networks

Large molecules such as proteins and nucleic acids are crucial for life, yet their primordial origin remains a major puzzle. The production of large molecules, as we know it today, requires good catalysts, and the only good catalysts we know that can accomplish this task consist of large molecules. Thus the origin of large molecules is a chicken and egg problem in chemistry. Here we present a mechanism, based on autocatalytic sets (ACSs), that is a possible solution to this problem. We discuss a mathematical model describing the population dynamics of molecules in a stylized but prebiotically plausible chemistry. Large molecules can be produced in this chemistry by the coalescing of smaller ones, with the smallest molecules, the `food set', being buffered. Some of the reactions can be catalyzed by molecules within the chemistry with varying catalytic strengths. Normally the concentrations of large molecules in such a scenario are very small, diminishing exponentially with their size. ACSs, if present in the catalytic network, can focus the resources of the system into a sparse set of molecules. ACSs can produce a bistability in the population dynamics and, in particular, steady states wherein the ACS molecules dominate the population. However to reach these steady states from initial conditions that contain only the food set typically requires very large catalytic strengths, growing exponentially with the size of the catalyst molecule. We present a solution to this problem by studying `nested ACSs', a structure in which a small ACS is connected to a larger one and reinforces it. We show that when the network contains a cascade of nested ACSs with the catalytic strengths of molecules increasing gradually with their size (e.g., as a power law), a sparse subset of molecules including some very large molecules can come to dominate the system.Comment: 49 pages, 17 figures including supporting informatio

Lung adenocarcinoma originates from retrovirus infection of proliferating type 2 pneumocytes during pulmonary post-natal development or tissue repair

Jaagsiekte sheep retrovirus (JSRV) is a unique oncogenic virus with distinctive biological properties. JSRV is the only virus causing a naturally occurring lung cancer (ovine pulmonary adenocarcinoma, OPA) and possessing a major structural protein that functions as a dominant oncoprotein. Lung cancer is the major cause of death among cancer patients. OPA can be an extremely useful animal model in order to identify the cells originating lung adenocarcinoma and to study the early events of pulmonary carcinogenesis. In this study, we demonstrated that lung adenocarcinoma in sheep originates from infection and transformation of proliferating type 2 pneumocytes (termed here lung alveolar proliferating cells, LAPCs). We excluded that OPA originates from a bronchioalveolar stem cell, or from mature post-mitotic type 2 pneumocytes or from either proliferating or non-proliferating Clara cells. We show that young animals possess abundant LAPCs and are highly susceptible to JSRV infection and transformation. On the contrary, healthy adult sheep, which are normally resistant to experimental OPA induction, exhibit a relatively low number of LAPCs and are resistant to JSRV infection of the respiratory epithelium. Importantly, induction of lung injury increased dramatically the number of LAPCs in adult sheep and rendered these animals fully susceptible to JSRV infection and transformation. Furthermore, we show that JSRV preferentially infects actively dividing cell in vitro. Overall, our study provides unique insights into pulmonary biology and carcinogenesis and suggests that JSRV and its host have reached an evolutionary equilibrium in which productive infection (and transformation) can occur only in cells that are scarce for most of the lifespan of the sheep. Our data also indicate that, at least in this model, inflammation can predispose to retroviral infection and cancer

Boolean network model predicts cell cycle sequence of fission yeast

A Boolean network model of the cell-cycle regulatory network of fission yeast (Schizosaccharomyces Pombe) is constructed solely on the basis of the known biochemical interaction topology. Simulating the model in the computer, faithfully reproduces the known sequence of regulatory activity patterns along the cell cycle of the living cell. Contrary to existing differential equation models, no parameters enter the model except the structure of the regulatory circuitry. The dynamical properties of the model indicate that the biological dynamical sequence is robustly implemented in the regulatory network, with the biological stationary state G1 corresponding to the dominant attractor in state space, and with the biological regulatory sequence being a strongly attractive trajectory. Comparing the fission yeast cell-cycle model to a similar model of the corresponding network in S. cerevisiae, a remarkable difference in circuitry, as well as dynamics is observed. While the latter operates in a strongly damped mode, driven by external excitation, the S. pombe network represents an auto-excited system with external damping.Comment: 10 pages, 3 figure

Is a Calorie Really a Calorie? Metabolic Advantage of Low-Carbohydrate Diets

The first law of thermodynamics dictates that body mass remains constant when caloric intake equals caloric expenditure. It should be noted, however, that different diets lead to different biochemical pathways that are not equivalent when correctly compared through the laws of thermodynamics. It is inappropriate to assume that the only thing that counts in terms of food consumption and energy balance is the intake of dietary calories and weight storage. Well-controlled studies suggest that calorie content may not be as predictive of fat loss as is reduced carbohydrate consumption. Biologically speaking, a calorie is certainly not a calorie. The ideal weight loss diet, if it even exists, remains to be determined, but a high-carbohydrate/low-protein diet may be unsatisfactory for many obese individuals

Change escalation processes and complex adaptive systems: from incremental reconfigurations to discontinuous restructuring

This study examines when “incremental” change is likely to trigger “discontinuous” change, using the lens of complex adaptive systems theory. Going beyond the simulations and case studies through which complex adaptive systems have been approached so far, we study the relationship between incremental organizational reconfigurations and discontinuous organizational restructurings using a large-scale database of U.S. Fortune 50 industrial corporations. We develop two types of escalation process in organizations: accumulation and perturbation. Under ordinary conditions, it is perturbation rather than the accumulation that is more likely to trigger subsequent discontinuous change. Consistent with complex adaptive systems theory, organizations are more sensitive to both accumulation and perturbation in conditions of heightened disequilibrium. Contrary to expectations, highly interconnected organizations are not more liable to discontinuous change. We conclude with implications for further research, especially the need to attend to the potential role of managerial design and coping when transferring complex adaptive systems theory from natural systems to organizational systems

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems. \ud \u

Combining Network Modeling and Gene Expression Microarray Analysis to Explore the Dynamics of Th1 and Th2 Cell Regulation

Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease

Composite Higgs Search at the LHC

The Higgs boson production cross-sections and decay rates depend, within the Standard Model (SM), on a single unknown parameter, the Higgs mass. In composite Higgs models where the Higgs boson emerges as a pseudo-Goldstone boson from a strongly-interacting sector, additional parameters control the Higgs properties which then deviate from the SM ones. These deviations modify the LEP and Tevatron exclusion bounds and significantly affect the searches for the Higgs boson at the LHC. In some cases, all the Higgs couplings are reduced, which results in deterioration of the Higgs searches but the deviations of the Higgs couplings can also allow for an enhancement of the gluon-fusion production channel, leading to higher statistical significances. The search in the H to gamma gamma channel can also be substantially improved due to an enhancement of the branching fraction for the decay of the Higgs boson into a pair of photons.Comment: 32 pages, 16 figure

The interpretations and uses of fitness landscapes in the social sciences

__Abstract__ This working paper precedes our full article entitled “The evolution of Wright’s (1932) adaptive field to contemporary interpretations and uses of fitness landscapes in the social sciences” as published in the journal Biology & Philosophy (http://link.springer.com/article/10.1007/s10539-014-9450-2). The working paper features an extended literature overview of the ways in which fitness landscapes have been interpreted and used in the social sciences, for which there was not enough space in the full article. The article features an in-depth philosophical discussion about the added value of the various ways in which fitness landscapes are used in the social sciences. This discussion is absent in the current working paper. Th