Abstract: Infectious disease outbreaks recapitulate biology, emerging from the multi-level interaction of hosts, pathogens, and their shared environment. Therefore, predicting when and where diseases will spread requires a complex systems approach to modeling. However, it remains to be demonstrated that such complex systems are fundamentally predictable. To investigate this question, I study the intrinsic predicability of a diverse set of diseases. However, instead of relying on methods which require an assumed knowledge of the data generating model, I utilize permutation entropy as a model independent metric of predicability. By studying the permutation entropy of a large collection of historical outbreaks--including, influenza, dengue, measles, polio, whooping cough, Ebola, and Zika--I identify fundamental limits to our ability to forecast outbreaks. Specifically, most diseases appear to be unpredictable beyond narrow time-horizons. These results have clear implications for the emerging field of disease forecasting and highlight the need for broader studies on the predictability of complex systems.

Abstract: One of the most impressive features of fish schools is their ability to perform spontaneous changes in travel direction without central coordination. A striking example is the emergence of collective U-turns. The causes that trigger these U-turns and the mechanisms by which information is propagated within a school are not yet understood. One challenging problem is the estimation of the effective neighborhood, i.e. the number and position of neighbors that affect the behavior of a focal fish. Another important issue is the quantification of social interactions between fish. Here we combine experimental and computational approaches to address these questions. Experiments have been conducted in a ring-shaped tank with groups of 2, 4, 5, 8 and 10 individuals of the species Hemigrammus rhodostomus, a small tropical fish that exhibits schooling behavior. Empirical results show that most collective U-turns occur after the group has slowed down, and that they are usually initiated and propagated from the front to the back of the group. Moreover fish perform less U-turns as group size is increasing. We then investigate with a computational model the consequences of interactions between fish on their collective swimming behavior. We first implement in the model the characteristic burst-and-coast swimming of H. rhodostomus: individuals control the strength of acceleration and the duration of the coasting phase depending on the presence of walls and individuals close by. Then we use the model to investigate effects of different effective neighborhoods on the propagation of information during collective U-turns and we compare the simulation results to the experimental data in the same conditions.

Abstract: How species richness relates to environmental gradients at large extents is commonly investigated aggregating local site data to coarser grains. However, such relationships often change with the grain of analysis, potentially hiding the local signal. We introduced a new index related to potential species richness, which revealed large scale patterns by including at the local community level information about species distribution throughout the dataset (i.e., the network). The method effectively removed noise, identifying how far site richness was from potential. When applying it to study woody species richness patterns in Spain, we observed that annual precipitation and mean annual temperature explained large parts of the variance of the newly defined species richness, highlighting that, at the local scale, communities in drier and warmer areas were potentially the species richest. Our method went far beyond what geographical upscaling of the data could unfold, and the insights obtained strongly suggested that it is a powerful instrument to detect key factors underlying species richness patterns, and that it could have numerous applications in ecology and other fields.

Abstract: Rainforests, coral reefs and other very large ecosystems seem to be the most stable in nature, but this has long been regarded as mathematically paradoxical. More generally, the relationship between structure and dynamics in complex systems is the subject of much debate. I will discuss how 'trophic coherence', a recently identified property of food webs and other directed networks, is key to understanding many dynamical and structural features of complex systems. In particular, it allows networks to become more stable with increasing size and complexity [1], determines whether a given system will be in a regime of high or negligible feedback [2], and influences spreading processes such as epidemics or cascades of neural activity [3]. [1] S. Johnson, V. Domínguez-García, L. Donetti, and M.A. Muñoz, "Trophic coherence determines food-web stability", PNAS 111, 17923 (2014) [2] S. Johnson and N.S. Jones, "Spectra and cycle structure of trophically coherent graphs", arXiv:1505.07332 (2015) [3] J. Klaise and S. Johnson, "From neurons to epidemics: How trophic coherence affects spreading processes", Chaos (in press), arXiv:1603.00670 (2016)

Abstract: Network Science has provided predictive models of many complex systems from molecular biology to social interactions. Most of this success is achieved by reducing multivariate dynamics to a graph of static interactions. Such network structure approach has provided many insights about the organization of complex systems. However, there is also a need to understand how to control them; for example, to revert a diseased cell to a healthy state or a mature cell to a pluripotent state in systems biology models of biochemical regulation. Based on recent work [1], using various systems biology models of biochemical regulation and large ensembles of network motifs, we show that the control of complex networks cannot be predicted from structure alone. Structure-only methods (structural controllability and minimum dominating set theory) both undershoot and overshoot the number and which sets of variables actually control these models, highlighting the importance of dynamics in determining control. We also show that canalization−measured as logical redundancy in automata transition functions models [2]−plays a very important role in the extent to which structure predicts dynamics. To further understand how canalization influences the controllability of multivariate dynamics, we introduce the concept of effective structure, obtained by removing all redundancy from the (discrete) dynamics of models of biochemical regulation. We show how such effective structure reveals the dynamical modularity [3] and robustness in such models [2]. Furthermore, we demonstrate that the connectivity of the effective graph is an order parameter of Boolean Network dynamics, and a major factor in network controllability [4]. [1] A. Gates and L.M. Rocha. [2016]. Scientific Reports 6, 24456. [2] M. Marques-Pita and L.M.Rocha [2013]. PLOS One, 8(3): e55946. [3] A. Kolchinsky, A. Gates and L.M. Rocha. [2015] Phys. Rev. E. 92, 060801(R). [4] M. Marques-Pita, S. Manicka and L.M.Rocha. [2016]. In Preparation.