Biology  (B) Session 5

Abstract: Type A influenza poses a serious risk to the health of the global population, due to its ability to inhabit a diverse range of hosts and having many strains. Very occasionally, humans become infected with a virus derived from non-human sources. These are essentially novel to humans. Due to the viruses meeting with little or no established resistance, they can, following mutation and adaptation to their new host, spread relatively easily in the human species. This can give rise to a localised outbreak that may develop into a worldwide influenza pandemic. Despite this, there is a worrying gap in the modelling of spillover transmission from animals to humans, a crucial element of the system in the lead up to an influenza pandemic event. We explore developments to address the lack of established mathematical modelling tools in this area, with the applied aim of being able to evaluate the effectiveness of control strategies in reducing pandemic risk. In particular, we overview two data-driven studies: (i) a statistical analysis of the time periods between influenza pandemics since 1700, to determine whether the emergence of new pandemic strains is either a memoryless or history-dependent process; (ii) constructing a spatial model, incorporating poultry-to-poultry and poultry-to-human transmission, applied to H5N1 epidemics in Bangladesh occurring between 2007 to 2011. These studies provide insights into the risk to humans associated with avian influenza outbreaks and the control strategies that should be utilised, across both human and livestock species, in the event of future influenza epidemics. Close

Abstract: A major cause of regime shifts in lake is nutrient overloads, coming mostly from agricultural fertilizers. Moreover, intensive agriculture has weakened soils which leads to a greater leach of nutrients during heavy rains. The objective of the paper is to model the effect of rainfall variability on lake regime shift, despite lake regulation. IPCC (Intergovernmental Panel on Climate Change) reports show that increases in extreme rain events are expected. Therefore, the multiplication of significant floods could result in nutrient over-enrichment, disrupting the equilibrium of lakes and causing eutrophication. Our case-study is the lake Bourget in France. We build and calibrate a model based on annual phosphorus dynamics. Results show that the drought of the 2000s is fostering a return to an oligotrophic state of the lake. We also show that lake regulation has been effective in reducing phosphorus input enabling the compliance of the objective recommended by OECD (Organisation for Economic Co-operation and Development) in 2020. A return to previous rainfall variability affects in a limited way the probability of a regime shift in the short and long terms. However, increasing the variance of loading by 25% may decrease from 98% to 81% the probability to maintain the lake Bourget in an oligotrophic state until 2100. Close

Abstract: Most disorders are caused by a combination of multiple genetic and environmental factors. If two diseases are caused by the same mechanism, they often co-occur in patients. Here we disentangle how much genetic or environmental risk factors contribute to the pathogenesis of 358 individual diseases, respectively. We pool data on genetic, pathway-based, and toxicogenomic disease-causing mechanisms with co-occurrences obtained from almost two million patients. From this data we construct a multilayer network where nodes represent disorders that are connected by links that either represent phenotypic comorbidity or the joint involvement of certain mechanisms. We quantify the similarity of phenotypic and mechanism-based links for each disorder. Most diseases are dominated by genetic risk factors, while environmental influences prevail for disorders such as depressions, cancers, or dermatitis. The relevance of environmental risk factors for a given disease is inversely related to its broad-sense heritability and also inversely related to the rate at which new drugs for the disease are approved. This might be indicative of a lack of successful drug development for diseases with high environmental risks. Our approach allows to rule out certain types of disease-causing mechanisms when their implied comorbidities are not observed and might therefore be used to identify promising leverage points for the development of future therapies of multifactorial diseases. Close

Abstract: Infectious agents affect behaviour and vital rates of their hosts, by influencing the interactions between species in the community and in that way are potentially changing the stability of the ecosystem. Empirical examples show a variety of ways in which different types of infectious agents can affect their hosts. We take an indirect approach in investigating wider community effects of these influences on hosts at different trophic levels. By decreasing and increasing resource preferences of consumers, conversion efficiencies and growth rates, we mimic subclinical and clinical influence of an infection in the community. Via the influence of infectious agents on their hosts, food webs become more and less stable, as it was measured by the size of the largest real part of the eigenvalues of the community matrix. The potential effects of the infectious agents show various consequences for the stability of the system even in the same focal species and role of that species as a consumer or resource. Our results show that influence of infection on resource preference of consumers has more impact on the change of stability than the effect of infection on conversion efficiencies of consumers. Subclinical and clinical effects of infectious agents in focal species of hosts, more frequently lead to increase than to decrease in stability of the community. The study suggests that infectious agents may be important for the stability of ecosystems. Close

Abstract: In self-organized ecosystems, it is eminent that group-level properties emerge from large-scale spatial pattern formation that promote survival of the organisms within the population. However, how these emergent properties influence the evolution of self-organizing traits and thereby affect spatial pattern formation itself remains unknown. Here, we demonstrate that aggregation into clusters in self-organized mussel beds adds a group-level selection pressure, which can cause the evolution of labyrinth-producing behaviour in mussels. We use a modelling approach that includes a high amount of ecological detail to investigate the evolution of two self-organizing traits, cooperation and aggregative movement, in spatially patterned mussel beds, where mussels aggregate and attach byssus threads (a glue-like substance) to neighbouring conspecifics in order to decrease losses to predation and wave stress. We developed a mechanistic, individual-based model of spatial self-organization where individual strategies of movement and attachment generate spatial patterns, which in turn determine the fitness consequences of these strategies. By combining an individual-based simulation approach for studying spatial self-organization within generations with an analytical adaptive dynamics approach that studies selection pressures across generations, we are able to predict how the evolutionary outcome is affected by environmental conditions. When selection pressures on cooperation and movement are only governed by local interactions, that is, the attachment of individuals to their neighbours, evolution does typically not result in the labyrinth-like spatial patterns that are characteristic for mussel beds. However, when we include a second level of selection by considering the additional protection provided by the formation of mussel clumps, evolutionarily stable movement and attachment strategies lead to labyrinth-like patterns under a wide range of conditions. Close

Abstract: Typical brain networks consist of many peripheral regions and a few highly central ones, i.e. hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. We first show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. This result suggests that information from frequency bands which are tipically neglected might play a crucial role in understanding brain's function. By using node's versatility, i.e. the natural extension of the concept of node's centrality in classical networks to multilayer sistems, we then demonstrate that hubs in the multiplex network are in general different from those ones obtained after discarding or aggregating the measured signals as usual and provide a more accurate map of brain's most important functional regions. Finally, as a clinical application, we use the brain's versatility profile to distinguish between healthy and schizophrenic populations, achieving higher accuracy than conventional network approaches. Close