No abstract

Immunological studies frequently analyze individual components (e.g., signaling pathways) of immune systems in a reductionist manner. In contrast, systems immunology aims to give a synthetic understanding of how these components function together as a whole. While immunological research involves in vivo and in vitro experiments, systems immunology research can also be conducted in silico. With an increasing interest in systems-level studies spawned by high-throughput technologies, many immunologists are looking forward to insights provided by computational modeling and simulation. However, modeling and simulation research has mainly been conducted in computational fields, and therefore, little material is available or accessible to immunologists today. This survey is an attempt at bridging the gap between immunologists and systems immunology modeling and simulation. Modeling and simulation refer to building and executing an in silico replica of an immune system. Models are specified within a mathematical or algorithmic framework called formalism and then implemented using software tools. A plethora of modeling formalisms and software tools are reported in the literature for systems immunology. However, it is difficult for a new entrant to the field to know which of these would be suitable for modeling an immunological application at hand. This paper covers three aspects. First, it introduces the field of system immunology emphasizing on the modeling and simulation components. Second, it gives an overview of the principal modeling formalisms, each of which is illustrated with salient applications in immunological research. This overview of formalisms and applications is conducted not only to illustrate their power but also to serve as a reference to assist immunologists in choosing the best formalism for the problem at hand. Third, it lists major software tools, which can be used to practically implement models in these formalisms. Combined, these aspects can help immunologists to start experimenting with in silico models. Finally, future research directions are discussed. Particularly, we identify integrative frameworks to facilitate the coupling of different modeling formalisms and modeling the adaptation properties through evolution of immune systems as the next key research efforts necessary to further develop the multidisciplinary field of systems immunology.

We employ a cellular-automata to reconstruct the land use patterns of cities that we characterize by two measures of spatial heterogeneity: (a) a variant of spatial entropy, which measures the spread of residential, business, and industrial activity sectors, and (b) an index of dissimilarity, which quantifies the degree of spatial mixing of these land use activity parcels. A minimalist and bottom-up approach is adopted that utilizes a limited set of three parameters which represent the forces which determine the extent to which each of these sectors spatially aggregate into clusters. The dispersion degrees of the land uses are governed by a fixed pre-specified power-law distribution based on empirical observations in other cities. Our method is then used to reconstruct land use patterns for the city state of Singapore and a selection of North American cities. We demonstrate the emergence of land use patterns that exhibit comparable visual features to the actual city maps defining our case studies whilst sharing similar spatial characteristics. Our work provides a complementary approach to other measures of urban spatial structure that differentiate cities by their land use patterns resulting from bottom-up dispersion and aggregation processes.

Abstract-Nature is a source of inspiration for computational techniques which have been successfully applied to a wide variety of complex application domains. In keeping with this we examine Cell Signaling Networks (CSN) which are chemical networks responsible for coordinating cell activities within their environment. Through evolution they have become highly efficient for governing critical control processes such as immunological responses, cell cycle control or homeostasis. Realising (and evolving) Artificial Cell Signaling Networks (ACSNs) may provide new computational paradigms for a variety of application areas. Our abstraction of Cell Signaling Networks focuses on four characteristic properties distinguished as follows: Computation, Evolution, Crosstalk and Robustness. These properties are also desirable for potential applications in the control systems, computation and signal processing field. These characteristics are used as a guide for the development of an ACSN evolutionary simulation platform. In this paper we present a novel evolutionary approach named Molecular Classifier System (MCS) to simulate such ACSNs. The MCS that we have designed is derived from Holland's Learning Classifier System. The research we are currently involved in is part of the multi disciplinary European funded project, ESIGNET, with the central question of the study of the computational properties of CSNs by evolving them using methods from evolutionary computation, and to re-apply this understanding in developing new ways to model and predict real CSNs.