We propose a stochastic dynamical model of noisy neural networks with complex architectures and discuss activation of neural networks by a stimulus, pacemakers, and spontaneous activity. This model has a complex phase diagram with self-organized active neural states, hybrid phase transitions, and a rich array of behaviors. We show that if spontaneous activity (noise) reaches a threshold level then global neural oscillations emerge. Stochastic resonance is a precursor of this dynamical phase transition. These oscillations are an intrinsic property of even small groups of 50 neurons.

Peroxisomes and mitochondria show a much closer interrelationship than previously anticipated. They cooperate in the metabolism of fatty acids and reactive oxygen species, but also share components of their fission machinery. If peroxisomes -like mitochondriaalso fuse in mammalian cells is a matter of debate and was not yet systematically investigated. To examine potential peroxisomal fusion and interactions in mammalian cells, we established an in vivo fusion assay based on hybridoma formation by cell fusion. Fluorescence microscopy in time course experiments revealed a merge of different peroxisomal markers in fused cells. However, live cell imaging revealed that peroxisomes were engaged in transient and long-term contacts, without exchanging matrix or membrane markers. Computational analysis showed that transient peroxisomal interactions are complex and can potentially contribute to the homogenization of the peroxisomal compartment. However, peroxisomal interactions do not increase after fatty acid or H 2 O 2 treatment. Additionally, we provide the first evidence that mitochondrial fusion proteins do not localize to peroxisomes. We conclude that mammalian peroxisomes do not fuse with each other in a mechanism similar to mitochondrial fusion. However, they show an extensive degree of interaction, the implication of which is discussed.

Nature abounds with helical filaments designed for specific tasks. For instance, some plants use tendrils to coil and attach to the surroundings, while Spiroplasma, a helical bacterium, moves by inverting the helical handedness along the filament axis. Therefore, developing methods to shape filaments on demand to exhibit a diversity of physical properties and shapes could be of interest to many fields, such as the textile industry, biomedicine or nanotechnology. Electrospinning is a simple and versatile technique that allows the production of micro and nanofibres with many different helical shapes. In this work, we review the different electrospinning procedures that can be used to obtain helical shapes similar to those found in natural materials. These techniques also demonstrate that the creation of helical shapes at the micro/nanoscale is not limited by the chirality of the building blocks at the molecular level, a finding which opens new horizons on filament shaping.

It is still unclear whether the adaptive immune system can perform accurate self-nonself discrimination and what could influence its performance. Starting from simple cellular interaction rules we show that it is possible to achieve perfect self-nonself discrimination in a consistent framework provided positive and negative selection operate during repertoire education, and costimulation and anergy are also considered during T cell activation. In this theory T cell receptors diversity is required for cells to sense differently different peptides; positive selection is needed to guarantee maximal lymphocyte's interactivity and to allow negative selection to reduce conjugation lifetimes maximally; costimulation is necessary to signal that an antigen presenting cell established an uncommon rate of long lived conjugations when presenting foreign peptides; anergy is required to guarantee that these stable contacts involved different T cells and not always the same. These results suggest that accurate self-nonself discrimination can have shaped the adaptive immune system.

A fundamental problem in immunology is that of understanding how the immune system selects promptly which cells to kill without harming the body. This problem poses an apparent paradox. Strong reactivity against pathogens seems incompatible with perfect tolerance towards self. We propose a different view on cellular reactivity to overcome this paradox: effector functions should be seen as the outcome of cellular decisions which can be in conflict with other cells' decisions. We argue that if cellular systems are frustrated, then extensive cross-reactivity among the elements in the system can decrease the reactivity of the system as a whole and induce perfect tolerance. Using numerical and mathematical analyses, we discuss two simple models that perform optimal pathogenic detection with no autoimmunity if cells are maximally frustrated. This study strongly suggests that a principle of maximal frustration could be used to build artificial immune systems. It would be interesting to test this principle in the real adaptive immune system.