Functional models for large-scale gene regulation networks: realism and fiction

High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as realistic and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.

A basic swimmer at low Reynolds number

Swimming and pumping at low Reynolds numbers are subject to the Scallop theorem, which states that there will be no net fluid flow for time reversible motions. Living organisms such as bacteria and cells are subject to this constraint, and so are existing and future artificial nano-bots or microfluidic pumps. We study a very simple mechanism to induce fluid pumping, based on the forced motion of three colloidal beads through a cycle that breaks time-reversal symmetry. Optical tweezers are used to vary the inter-bead distance. This model is inspired by a strut-based theoretical swimmer proposed by Najafi and Golestanian [Phys.Rev. E, 69, 062901, 2004], but in this work the relative softness of the optical trapping potential introduces a new control parameter. We show that this system is able to generate flow in a controlled fashion, characterizing the model experimentally and numerically.