Boolean Dynamics with Random Couplings

This paper reviews a class of generic dissipative dynamical systems called N-K models. In these models, the dynamics of N elements, defined as Boolean variables, develop step by step, clocked by a discrete time variable. Each of the N Boolean elements at a given time is given a value which depends upon K elements in the previous time step. We review the work of many authors on the behavior of the models, looking particularly at the structure and lengths of their cycles, the sizes of their basins of attraction, and the flow of information through the systems. In the limit of infinite N, there is a phase transition between a chaotic and an ordered phase, with a critical phase in between. We argue that the behavior of this system depends significantly on the topology of the network connections. If the elements are placed upon a lattice with dimension d, the system shows correlations related to the standard percolation or directed percolation phase transition on such a lattice. On the other hand, a very different behavior is seen in the Kauffman net in which all spins are equally likely to be coupled to a given spin. In this situation, coupling loops are mostly suppressed, and the behavior of the system is much more like that of a mean field theory. We also describe possible applications of the models to, for example, genetic networks, cell differentiation, evolution, democracy in social systems and neural networks.Comment: 69 pages, 16 figures, Submitted to Springer Applied Mathematical Sciences Serie

Reduced expression of chemokine (C-C motif) ligand-2 (CCL2) in ovarian adenocarcinoma

Chemokine (C-C motif) ligand-2 (CCL2) is a chemoattractant and activator of macrophages and is a key determinant of the macrophage infiltrate into tumours. We demonstrate here that CCL2 is expressed in normal human ovarian surface epithelium ( HOSE) cells and is silenced in most ovarian cancer cell lines, and silenced or downregulated in the majority of primary ovarian adenocarcinomas. Analysis of the CCL2 locus at 17q11.2-q12 showed loss of heterozygosity (LOH) in 70% of primary tumours, and this was significantly more common in tumours of advanced stage or grade. However, we did not detect any mutations in the CCL2 coding sequence in 94 primary ovarian adenocarcinomas. These data support the hypothesis that CCL2 may play a role in the pathobiology of ovarian cancers, but additional studies will be required to evaluate this possibility

Computational history : from big data to big simulations

The first section of this chapter gives an overview on how big data and their mathematical calculation enter in the historical discourse. It introduces the two main issues that prevent ‘big’ results from emerging so far. Firstly, the input is problematic because historical records cannot be easily and comprehensively decomposed into unambiguous fields, except for the population and taxation ones, which are rare and scattered throughout space and time till the nineteenth century. Secondly, even if we run machine-learning tools on properly structured data, big results cannot emerge until we built formal models, with explanatory and predictive powers. The second section of the chapter presents a complex network, data-driven approach to mining historical sources and supporting the perennial historical chase for truth. In the time-integrated network obtained by overlaying all records from the historians’ databases, the nodes are actors, while the links are actions. The third section explains how this tool allows historians to deal with historical data issues (e.g., source criticism, facts validation, trade-conflict-diplomacy relationships, etc.), and take advantage of automatic extraction of key narratives to formulate and test their hypotheses on the courses of history in other actions or in additional data sets. The conclusions describe the vision of how this narrative-driven analysis of historical big data can lead to the development of multiscale agent-based models and simulations to generate ensembles of counterfactual histories that would deepen our understanding of why our actual history developed the way it did and how to treasure these human experiences.Accepted versio