Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Rapid Sequestration of Leishmania mexicana by Neutrophils Contributes to the Development of Chronic Lesion.

The protozoan Leishmania mexicana parasite causes chronic non-healing cutaneous lesions in humans and mice with poor parasite control. The mechanisms preventing the development of a protective immune response against this parasite are unclear. Here we provide data demonstrating that parasite sequestration by neutrophils is responsible for disease progression in mice. Within hours of infection L. mexicana induced the local recruitment of neutrophils, which ingested parasites and formed extracellular traps without markedly impairing parasite survival. We further showed that the L. mexicana-induced recruitment of neutrophils impaired the early recruitment of dendritic cells at the site of infection as observed by intravital 2-photon microscopy and flow cytometry analysis. Indeed, infection of neutropenic Genista mice and of mice depleted of neutrophils at the onset of infection demonstrated a prominent role for neutrophils in this process. Furthermore, an increase in monocyte-derived dendritic cells was also observed in draining lymph nodes of neutropenic mice, correlating with subsequent increased frequency of IFNγ-secreting T helper cells, and better parasite control leading ultimately to complete healing of the lesion. Altogether, these findings show that L. mexicana exploits neutrophils to block the induction of a protective immune response and impairs the control of lesion development. Our data thus demonstrate an unanticipated negative role for these innate immune cells in host defense, suggesting that in certain forms of cutaneous leishmaniasis, regulating neutrophil recruitment could be a strategy to promote lesion healing.

Multimodal stimulus coding by a gustatory sensory neuron in Drosophila larvae.

Accurate perception of taste information is crucial for animal survival. In adult Drosophila, gustatory receptor neurons (GRNs) perceive chemical stimuli of one specific gustatory modality associated with a stereotyped behavioural response, such as aversion or attraction. We show that GRNs of Drosophila larvae employ a surprisingly different mode of gustatory information coding. Using a novel method for calcium imaging in the larval gustatory system, we identify a multimodal GRN that responds to chemicals of different taste modalities with opposing valence, such as sweet sucrose and bitter denatonium, reliant on different sensory receptors. This multimodal neuron is essential for bitter compound avoidance, and its artificial activation is sufficient to mediate aversion. However, the neuron is also essential for the integration of taste blends. Our findings support a model for taste coding in larvae, in which distinct receptor proteins mediate different responses within the same, multimodal GRN.