Biology  (B) Session 1

Abstract: Protein-protein interactions have been well known to be one of the most studied complex system. This is because as one of the most fundamental molecules in human bodies, proteins have been linked to various vital functions of the body. Protein aggregation in particular, has been central to a lot of diseases such as Alzheimer and Parkinson disease. Many studies have been done on the aggregation of proteins of the same species. However, our research here focus on understanding the interactions between two peptides that share no similarity both sequentially and structurally. One of the peptides is amylin (IAPP) linked to diabetes, while the other peptide is a prion fragment (PrP106-126) linked to prion disease. Extensive molecular dynamics simulation consisting ~ 22,000 atoms was carried out using enhanced sampling method (Replica exchange molecular dynamics) to simulate the two peptides in solution to elucidate the interaction mechanism between them. Results show that the two peptides form structurally diverse complexes. Hotspots within the sequences of the proteins with high contact probabilities were identified. Extension of the simulation using coarse-grained modelling to simulate large scale oligomers and also the effects of lipids may further provide detailed mechanism of the aggregation. As such, we hope to learn from various other approaches used to model proteins complexity through the conference which will complement our molecular dynamics study, while at the same time providing our own perspective in modelling proteins at the atomistic level and analysing simulation data. My first choice of track is Paper while the second choice of track is Ignite. Close

Abstract: Ant colonies are self-organised systems. Hence even large-scale colony functions (like foraging or nest construction) must be regulated locally by the workers engaged in it, via interaction with nestmates and the environment. We investigate whether such self-regulation mechanisms exist in one of the most striking collective feats in the ants: the construction of foraging trails in leaf-cutter ant colonies, which can span hundreds of metres. While most ant species rely on pheromone trails to guide their collective movements, Atta leaf-cutter ants build tangible trails cleared down to bare soil of all undergrowth and organic debris, a rare feature among ants. Such trails can greatly increase foraging efficiency despite the costs of construction and maintenance. While recent work investigated the function of such trails, nothing is known about the mechanism of trail construction. In laboratory experiments, we find two concurrent modes of trail clearing behaviour -- ‘one-off’ and ‘repeater’ clearing. By tracking ant movement and obstruction encounters in the field and in laboratory experiments, we identify the regulatory mechanism controlling the extent of trail clearing and the resulting trail dimension. We integrate these concurrent clearing behaviours and the regulatory mechanism in a parameterised stochastic model of the trail clearing dynamics. From this model, we make predictions of the trail clearing behaviour in subsequent experiments with varying foraging conditions, and test them against the empirical results. Close

Abstract: The human brain is a fascinating and paradigmatic example of a complex system and a natural candidate for network analysis. On one hand, the structural network given by the physical connections between the different brain regions, obtained by Diffusion Tensor Imaging (DTI), has been widely investigated. On the other, many studies have focused on the functional layer, where links represent correlation in the activity of different regions obtained through resting-state functional MRI (rs-fMRI). One of the new challenges in neuroscience is now trying to integrate this different information in order to better understand the interplay between the structure and the function of our brain. In this work we shed light on the mesoscale organization of the multilayer human brains constructed from structural and functional brain information on 21 healthy subjects. In particular, we focus on the following different mesoscopic structures: a) we investigate the presence of motifs, small subgraphs statistically over-represented in the real systems with respect to a suitable null model. In biological networks, the abundance of given subgraphs has been linked to the robustness of the system or to the stability of the dynamical or signalling circuit they represent; (b) we analyse the organization of communities across the two-layers, which gives rise to a non-trivial overlapping structure particularly remarkable for some brain regions associated to given tasks; (c) we propose a novel method to identify core-periphery structures in networks with links of different types, and apply it to extract the multiplex core of our brain, highlighting previously neglected regions of interest. Results indicate the existence of a complex interplay between the structural and functional networks of the human brain, and that, even if structural links appears to be somehow necessary to determine the co-activation of two brain regions, functional connectivity is non-trivially constrained by its underlying anatomical network. Close

Abstract: One of the most important problems in biology is to understand the principles underlying evolution of living systems from non-living systems. To do so, we need to identify universal features of living systems that can distinguish them from other classes of physical systems. On the other hand, recently developed frameworks for control theory on complex networks suggest that our ultimate understanding of the evolutionary principles allow us to control biological networks in terms of making them to converge to desired states. Here, I present our recent attempt to understand relationship between informational architecture as the universal feature, and controllability by using various biomolecular networks. Our previous study showed that scaling relation of information processing within biological networks differentiates them from their random network counterparts. Also, we provided an analysis which indicates that biologically distinctive patterns of informational flow is related to control kernel, a minimal subset governing the global dynamics of biological networks. In this paper, we quantify controllability of a network by the size of control kernel, which is the size of the subset to be controlled to drive it from one state to the desired state. We find that biological networks tend to be more difficult to control compared to random networks. This suggests that biological networks are evolved to be more resilient to environmental change. In addition to that, we measure informational flow within biological networks with snd without control to investigate how the observed resilience is related to the informational processing. Finally, we discuss the implications of informational architecture and its relationship with controllability in understanding evolutionary principles for living systems. Close

Abstract: We consider classical nonlinear oscillators like rotators and Kuramoto oscillators on hexagonal lattices of small or intermediate size. When the coupling between these elements is repulsive and the bonds are frustrated, we observe coexisting states, each one with its own basin of attraction. For special lattices sizes the multiplicity of stationary states gets extremely rich. When disorder is introduced into the system by additive or multiplicative Gaussian noise, we observe a noise-driven migration of oscillator phases in a rather rough potential landscape. Upon this migration, a multitude of different escape times from one metastable state to the next is generated [1]. Based on these observations, it does not come as a surprise that the set of oscillators shows physical aging. Physical aging is characterized by non-exponential relaxation after a perturbation, breaking of time-translation invariance, and dynamical scaling. When our system of oscillators is quenched from the regime of a unique fixed point towards the regime of multistable limit-cycle solutions, the autocorrelation functions depend on the waiting time after the quench, so that time translation invariance is broken, and dynamical scaling is observed for a certain range of time scales [2]. We point to open questions concerning a possible relation between physical and biological aging. References: [1] F.Ionita, D.Labavic, M.Zaks, and H.Meyer-Ortmanns, Eur. Phys.J.B 86(12), 511 (2013). [2] F.Ionita, H.Meyer-Ortmanns, Phys.Rev.Lett.112, 094101 (2014). Close