Biology & Physics  (BP) Session 1

Abstract: All known life on Earth shares a set of common core reactions, used to synthesize and sustain every living individual. The network structure of these core reactions, and the corresponding peripheral reactions, have been analyzed in organisms in all three domains of life. These analyses have revealed similarities in the organization of their chemical reaction networks, which are quantified by topological measures such as diameter, degree distribution, and hierarchical modularity. We expand on this work with the analysis of an additional 21,000 bacterial genomes, 800 archaeal genomes, tens of metagenomes, and all documented biologically catalyzed reactions. We show that networks constructed from communities of individuals are distinguishable from individual organismal networks using some measures, but indistinguishable using others. Additionally, we show that real, coevolved metabolic communities are distinct from synthetic metabolic communities, which are constructed from randomly assembling individual organisms that have not jointly evolved. We find that regardless of organizational scale and whether or not communities are jointly evolved, these biological networks have heterogeneous degree distributions, which are associated with robustness to random mutation. Finally, we construct artificial networks that are topologically identical to individual metabolic networks, but differ in the distribution of chemical pathways relative to their topology. We show that in contrast to the real and synthetic communal networks, communities of these artificial networks do not exhibit scale-invariant properties. Interpreted in a network theoretic sense, this implies that networks which can sum together and maintain certain scale-invariant features must have highly constrained subgraphs. In the context of biological systems, these results suggest that the robustness of a communal metabolic network is highly sensitive to the particular chemical pathways present within individuals that constitute the community, and thus that a robust biosphere requires all its organisms to share a common core biochemistry. Close

Abstract: The complexity of emergent systems can arise both from an intricate interplay of interacting parts and from the dynamical patterns performed by the system as a whole. But how do we find the dominant collective modes and how do we capture the dynamics of these modes with models amenable to analysis? Here we address these questions in the living movement of the nematode C. elegans. We apply a low-dimensional yet complete "eigenworm" representation of body shape to construct a principled parameterization of 2D postural movements. We use this representation to systematically explore the space of behavior by means of a local linear model and we develop a novel algorithm in which temporal locality is provided by the system itself by adaptively selecting the window of the local approximation. We apply our procedure to an example in which a heat shock is briefly administered to the worm’s head and we find a fine-scale description of the worm behavior which is remarkably more structured than previous, coarse-grained characterizations. We believe that our approach will be useful in dissecting other complex systems into more interpretable behaviors. Close

Abstract: We investigate the emergence of chimera states in hierarchically connected networks, in a system undergoing a Hopf bifurcation. We show that under specific conditions the chimera states (characterized by coexisting, alternating, coherent and incoherent domains), acquire nested mean phase velocity distribution and can be traveling. The single oscillator dynamics follows the Lattice Limit Cycle (LLC) model which describes a prey-predator cyclic scheme among three species, presents a fourth order nonlinearity and gives rise to a limit cycle via a Hopf bifurcation. If LLC oscillators are arranged on a ring network topology with nonlocal interactions, stationary multi-chimera states emerge when the system is far from the Hopf bifurcation[1]. Hierarchical coupling connectivity [2] is introduced to the network in such a way that each LLC oscillator is coupled to all elements belonging to a Cantor set arranged around the ring. We provide evidence that this coupling scheme causes alterations to the structure of the coherent and incoherent regions. As space-time plots show, the (in)coherent regions present nested structures which travel around the ring keeping their profiles statistically stable in time. By recording how the position (i.e. the node number) of the maximum concentration value periodically changes in time, we calculate the corresponding frequency via the Fourier transform. We find that the speed of this motion decreases with increasing coupling strength [1]. Complex nested chimera structures, when regarded from the viewpoint of population dynamics, exemplify the rich organization which arises in communities of nonlocally interacting populations due to correlations in the connectivity rules. [1] Hizanidis, J., Panagakou, E., Omelchenko, I., Schöll, E., Hövel, P., Provata, A., Phys. Rev. E, vol. 92, 012915 (2015). [2] Omelchenko, I., Provata, A., Hizanidis, J., Schöll, E., Hövel, P., Phys. Rev. E, vol 91, 022917 (2015). Close

Abstract: The ability of atomic, colloidal, and nanoparticles to self organize into highly ordered crystalline structures makes the prediction of crystal structures in these systems an important challenge for science. The question itself is deceivingly simple: assuming that the underlying interaction between constituent particles is known, which crystal structures are stable. In this talk, I will describe a Monte Carlo simulation method [1] combined with a triangular tesselation method [2] to describe the surface of arbitrarily shaped particles that can be employed to predict close-packed crystal structures in colloidal hard-particle systems. I will show that particle shape alone can give rise to a wide variety of crystal structures with unusual properties, e.g., photonic band gap structures or highly diffusive crystals, but combining the choice of particle shape with external fields, like confinement [5], or solvent effects [6] can enlarge the number of possible structures even more. [1] L. Filion, M. Marechal, B. van Oorschot, D. Pelt, F. Smallenburg, and M. Dijkstra, Physical Review Letters 103, 188302 (2009). [2] J. de Graaf, R. van Roij and M. Dijkstra, Physical Review Letters 107, 155501 (2011). [3] K. Miszta, J. de Graaf, G. Bertoni, D. Dorfs, R. Brescia, S. Marras, L. Ceseracciu, R. Cingolani, R. van Roij, M. Dijkstra and L. Manna, Nature Materials 10, 872-876 (2011). [4] A.P. Gantapara, J. de Graaf, R. van Roij, and M. Dijkstra, Physical Review Letters 111, 015501 (2013). [5] B. de Nijs, S. Dussi, F. Smallenburg, J.D. Meeldijk, D.J. Groenendijk, L. Filion, A. Imhof, A. van Blaaderen, and M. Dijkstra, Nature Materials 14, 56-60 (2015). [6] J.R. Edison, N. Tasios, S. Belli, R. Evans, R. van Roij, and M. Dijkstra, Physical Review Letters 114, 038301 (2015) Close

Abstract: The target of our study is to approximate numerically and, in some particular physically relevant cases, also analytically, the residence time of particles undergoing a biased motion on a two--dimensional vertical strip. The model is of some relevance to crowd dynamics, when high density of people at exits has to be prevented. The sources of asymmetry are twofold: (i) the choice of the boundary conditions (different reservoir levels) and (ii) the possible strong anisotropy from a drift nonlinear in density with prescribed directionality. The motion is modelled by the simple exclusion process on the square two-dimensional lattice. We focus on the effect on residence time due to jamming induced both by high reservoir levels at the strip exit end and by the presence of an impenetrable barrier placed at the middle of the strip. In both cases we find unexpected non-linear behavior of the residence time with respect to natural parameters of the model, such as the lateral movements probability and the width of the obstacle. We analyze our numerical results by means of two theoretical models, a Mean Field and a one-dimensional Birth and Death model. In most cases we find good agreement between theoretical predictions and numerical results. Close