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Turnlund, A.

Publications and source records attributed to Turnlund, A..

2 recordsLinked to original sources

Quantifying Feral Pig Interactions to Inform Disease Transmission Networks

Feral pigs threaten biodiversity in 54 countries worldwide and cause an estimated $120 billion of damage annually in the United States of America (USA). Feral pigs imperil over 600 native species and have directly driven 14 species into extinction. Moreover, feral pig populations pose a significant zoonotic disease threat to humans such as Japanese encephalitis, and act as reservoir for endemic pathogens such as Brucella and leptospirosis. Efforts to understand and control disease spread by feral pigs rely on models of social dynamics, how the animals interact with one another. Yet social dynamics are known to vary enormously from place to place, so knowledge generated for example in USA and Europe might not easily transfer to locations such as Australia. Here, we fill a continental gap in our understanding of feral pig social dynamics by developing a proximity-based social network analysis approach to rapidly assess social interactions using animal tracking data. This method, applied to the continent of Australia, included 146 GPS-monitored feral pigs, and revealed distinct patterns influenced by sex and season, with females demonstrating higher group cohesion (female-female) and males acting as crucial connectors between independent groups. Contact rates are remarkably high within groups, indicating rapid intra-group disease spread that contrasts with much slower potential for inter-group disease spread. Seasonal variations further complicate this dynamic, with contact rates being much higher in summer. The results show that, in Australia, targeting adult males in feral pig control programs could enhance efforts to contain disease outbreaks. Concern over the economic and human health impacts of animal diseases is higher than ever before. We urge a rapid global effort to use models of feral pig social interactions to develop efficient control strategies tailored to local conditions.

ecology↗

Predicting phenotype to mechanotype relationships in cells based on intra-cellular signaling network

Cells originating from the same tissue can respond differently to external signals depending on the genotypic and phenotypic state of the cell and its local environment. We have developed a semi-quantitative-computational model to analyze the intracellular signaling network and its outcome in the presence of multiple external signals including growth factors, hormones, and extracellular matrix. We use this model to analyze the cells mechanical response to external stimuli and identify the key internal elements of the network that drive specific outcomes within the response space. The model is built upon the Boolean approach to network modeling, where the state of any given node is determined using the state of the connecting nodes and Boolean logic. This allows us to analyze the network behavior without the need to estimate all the various interaction rates between different cellular components. However, such an approach is limited in its ability to predict network dynamics and temporal evolution of the cell state. So, we introduce modularity in the model and incorporate dynamical aspects, mass-action kinetics, and chemo-mechanical effects on only certain transition rates within specific modules as required, creating a Boolean-Hybrid-Modular (BoHyM) signal transduction model. We present this model as a comprehensive, cell-type agnostic, user-modifiable tool to investigate how extra-and intra-cellular signaling can regulate cellular cytoskeletal components and consequently influence cell-substrate interactions, force generation, and migration. Using this tool, we show how slight changes in signaling network architectures due to phenotypic changes can alter cellular response to stress hormone signaling in an environment-dependent manner. The tool also allows isolating effector proteins driving specific cellular mechanical responses. Ultimately, we show the utility of the tool in analyzing transient chemo-mechanical dynamics of cells in response to time-varying chemical stimuli.

systems biology↗