life inspired

"A quantitative description of a complex system is inherently limited by our ability to estimate the system’s internal state from experimentally accessible outputs. Although the simultaneous measurement of all internal variables, like all metabolite concentrations in a cell, offers a complete description of a system’s state, in practice experimental access is limited to only a subset of variables, or sensors. A system is called observable if we can reconstruct the system’s complete internal state from its outputs. Here, we adopt a graphical approach derived from the dynamical laws that govern a system to determine the sensors that are necessary to reconstruct the full internal state of a complex system. We apply this approach to biochemical reaction systems, finding that the identified sensors are not only necessary but also sufficient for observability. The developed approach can also identify the optimal sensors for target or partial observability, helping us reconstruct selected state variables from appropriately chosen outputs, a prerequisite for optimal biomarker design. Given the fundamental role observability plays in complex systems, these results offer avenues to systematically explore the dynamics of a wide range of natural, technological and socioeconomic systems." Full article @ PNAS

Labels: biocomplexity, complex systems, control

"Because there are still naysayers who question whether simple physical laws operate in living systems, we want to emphasize the existence of numerous examples in which the laws of physics have been used to provide mechanistic insights on complex behaviors of living organisms." Full letter @ Science

Labels: biocomplexity

"We present schema redescription as a methodology to characterize canalization in automata networks used to model biochemical regulation and signalling. In our formulation, canalization becomes synonymous with redundancy present in the logic of automata. This results in straightforward measures to quantify canalization in an automaton (micro-level), which is in turn integrated into a highly scalable framework to characterize the collective dynamics of large-scale automata networks (macro-level). This way, our approach provides a method to link micro- to macro-level dynamics -- a crux of complexity. Several new results ensue from this methodology: uncovering of dynamical modularity (modules in the dynamics rather than in the structure of networks), identification of minimal conditions and critical nodes to control the convergence to attractors, simulation of dynamical behaviour from incomplete information about initial conditions, and measures of macro-level canalization and robustness to perturbations. We exemplify our methodology with a well-known model of the intra- and inter cellular genetic regulation of body segmentation in Drosophila melanogaster. We use this model to show that our analysis does not contradict any previous findings. But we also obtain new knowledge about its behaviour: a better understanding of the size of its wild-type attractor basin (larger than previously thought), the identification of novel minimal conditions and critical nodes that control wild-type behaviour, and the resilience of these to stochastic interventions. Our methodology is applicable to any complex network that can be modelled using automata, but we focus on biochemical regulation and signalling, towards a better understanding of the (decentralized) control that orchestrates cellular activity -- with the ultimate goal of explaining how do cells and tissues 'compute'." Full pre-print:

M. Marques-Pita and L.M. Rocha [2013]. "Canalization and control in automata networks: body segmentation in Drosophila Melanogaster". PLOS ONE, In Press.

Labels: automata, biocomplexity, boolean networks, canalization, complex systems, modularity, self-organization

On December 11th, 2012, the Calouste Gulbenkian Foundation hosted The Alan Turing Centenary Symposium, organized by the Instituto Gulbenkian de Ciência, to celebrate Alan Turing's centenary and discuss his legacy. Among the invited speakers were Sidney Brenner, Nobel Laureate in Physiology or Medicine 2002, the writer David Leavitt, and the scientists Luís Rocha (Instituto Gulbenkian de Ciência and Indiana University), António Machiavelo (Universidade do Porto) and Christof Teuscher (Portland State University). A video library with all the talks at this conference is available. The videocasts are also shown below in three blocks:

Labels: artificial intelligence, Artificial Life, biocomplexity, computation, informatics, turing

"Bumblebees are remarkable navigators. While their flight paths may look scattered to the casual eye, all that buzzing about is anything but random. Like the travelling salesman in the famous mathematical problem of how to take the shortest path along multiple stops, bumblebees quickly find efficient routes among flowers. And once they find a good route, they stick to it. The same goes for other animals from hummingbirds to bats to primates that depend on patchy resources such as nectar and fruit. Perhaps this is not such a surprising feat for animals with relatively high brain power. But how do bumblebees, with their tiny brains, manage it? " Full synopsis @ PLOS Biology

Labels: biocomplexity, collective behavior, complex systems

"Theory suggests that the risk of critical transitions in complex systems can be revealed by generic indicators. A lab study of extinction in plankton populations provides experimental support for that principle." Full news article @ Nature. Se also the research article:

John M. Drake & Blaine D. Griffen [2010]. "Early warning signals of extinction in deteriorating environments". Nature 467 , 456–459

Labels: biocomplexity, complex systems, dynamical systems