Advances in Complex Systems

We provide a systematic analysis of a biological system, the microbial pathogen Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and biocomputational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane and cytosol) of Mtb. We report the compartmentalization of 1,044 identified proteins using proteomics methods. Genome-based biological network analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.

Cooperation in spatial evolutionary game theory has revealed various interesting insights into the problem of the evolution and maintenance of cooperative behavior. In social dilemmas, cooperators create and maintain a common resource at some cost to themselves while defectors attempt to exploit the resource without contributing. This leads to classical conflicts of interest between the individual and the community with the prisoner's dilemma as the most prominent mathematical metaphor to describe such situations. The evolutionary fate of cooperators and defectors sensitively depends on the interaction structure of the population. In spatially extended populations, the ability to form clusters or collectives often supports cooperation by limiting exploitation to the cluster boundaries but often collectives formation may also inhibit or even eliminate cooperation by hindering the dispersal of cooperators. Another attempt at resolving the conflict of interest allows individuals to drop out of unpromising public enterprises and hence changes compulsory interactions into voluntary participation. This leads to a cyclic dominance of cooperators, defectors and loners that do not participate and gives rise to oscillatory dynamics which again subtly depends on the population structure. Here we review recent advances in the dynamics of cooperation in structured populations as well as in situations where cooperative investments vary continuously. In such continuous games, the evolutionary dynamics driven by mutation and selection can lead to spontaneous diversification and specialization into high and low investing individuals which provides a natural explanation for the origin of cooperators and defectors.

Swarm intelligence is widely recognized as a powerful paradigm of self-organized optimization, with numerous examples of successful applications in distributed artificial intelligence. However, the role of physical interactions in the organization of traffic flows in ants under crowded conditions has only been studied very recently. The related results suggest new ways of congestion control and simple algorithms for optimal resource usage based on local interactions and, therefore, decentralized control concepts. Here, we present a mathematical analysis of such concepts for an experiment with two alternative ways with limited capacities between a food source and the nest of an ant colony. Moreover, we carry out microscopic computer simulations for generalized setups, in which ants have more alternatives or the alternative ways are of different lengths. In this way and by variation of interaction parameters, we can get a better idea of how powerful congestion control based on local repulsive interactions may be. Finally, we discuss potential applications of this design principle to routing in traffic or data networks and machine usage in supply systems.

This paper reviews recent progress in modeling collective behaviors in myxobacteria using lattice gas cellular automata approach (LGCA). Myxobacteria are social bacteria that swarm, glide on surfaces and feed cooperatively. When starved, tens of thousands of cells change their movement pattern from outward spreading to inward concentration; they form aggregates that become fruiting bodies. Cells inside fruiting bodies differentiate into round, nonmotile, environmentally resistant spores. Traditionally, cell aggregation has been considered to imply chemotaxis, a long-range cell interaction. However, myxobacteria aggregation is the consequence of direct cell-contact interactions, not chemotaxis. In this paper, we review biological LGCA models based on local cell–cell contact signaling that have reproduced the rippling, streaming, aggregating and sporulation stages of the fruiting body formation in myxobacteria.

Human cancer may be thought of as Darwinian evolution of cells within the body (somatic evolution). As such, it may be modeled using evolutionary game theory. Winners in this game are able to maintain viable population numbers while losers go extinct. A model for somatic evolution is presented that shows how cancer can evolve to become a winner in this game. Associated with the model is an adaptive landscape that illustrates the evolutionary potential of cells as a function of adaptive parameters used by cells. Normal cells have a novel adaptive landscape that permits coexistence of invading mutant phenotypes. A mutant cellular population needs not immediately form a malignancy due to the existence of cellular constraints that results in limited growth and non-lethal coexistence. However, this coexistence deforms the local adaptive landscape resulting in an unoccupied fitness peak. The existence of this peak provides the potential for evolution to drive the mutant cells from simple coexistence to an evasive cancer. The model presented here suggests all that is required to accomplish this result is the relaxation of constraints that normally prohibit cells to evolve.

Recent work has shown how information theory extends conventional full-rationality game theory to allow bounded rational agents. The associated mathematical framework can be used to solve distributed optimization and control problems. This is done by translating the distributed problem into an iterated game, where each agent's mixed strategy (i.e. its stochastically determined move) sets a different variable of the problem. So the expected value of the objective function of the distributed problem is determined by the joint probability distribution across the moves of the agents. The mixed strategies of the agents are updated from one game iteration to the next so as to converge on a joint distribution that optimizes that expected value of the objective function. Here, a set of new techniques for this updating is presented. These and older techniques are then extended to apply to uncountable move spaces. We also present an extension of the approach to include (in)equality constraints over the underlying variables. Another contribution is that we show how to extend the Monte Carlo version of the approach to cases where some agents have no Monte Carlo samples for some of their moves, and derive an "automatic annealing schedule".