Biology subjects

Bode, M.

Publications and source records attributed to Bode, M..

12 recordsLinked to original sources

bioRxiv2026

A relational architecture of the human genome

Complex systems exhibit organization not only through their components but also via their interdependencies. Local nucleotide dependencies underpin regulatory motifs and RNA structures, yet principles governing a higher-order organization across the human genome remain unknown. Here we systematically perturbed every position of the com- plete telomere-to-telomere human genome using Nucleotide Transformer v3 with an ex- ascale compute cluster, generating nucleotide-resolution dependency maps that reveal a directed, weighted network of position-to-position genomic relationships. This network recapitulates known biological features and uncovers recurrent, directional architec- tures termed 'relons', spanning coding, non-coding, and unannotated regions. These re- lational modules reflect evolutionary and functional constraints and associate with hu- man genetic variation and pathogenic variants. Our findings suggest a higher-order rela- tional layer of genome organization, providing a framework to interpret DNA as a complex system of dependencies beyond linear and 3D structure, with implications for under- standing genetic variation and disease.

genomics↗

bioRxiv2025

O-GlcNAcylation and an importin-β radial gradient keep the FG barrier liquid in live-cell nuclear pores

Summary ParagraphThe nuclear pore complex (NPC) regulates the molecular traffic between nucleus and cytoplasm.1,2 Its permeability barrier is formed by intrinsically disordered proteins (IDPs) known as FG-nucleoporins (FG-NUPs), whose physical state has long been debated.3-13 Deciphering how FG-NUPs behave inside living cells is crucial for understanding how the NPC achieves selective and rapid transport. Here, by combining site-specific labelling with picosecond time-resolved fluorescence anisotropy, we reveal that FG domains exhibit nanosecond-scale, liquid-like mobility in live cells, yet undergo a liquid-to-solid transition in vitro. Experiments and coarse-grained molecular dynamics simulations further show that importin-{beta}, a major nuclear transport receptor, and O-linked {beta}-N-acetylglucosamine (O-GlcNAc), a key post-translational modification of FG-NUPs together stabilise the dynamic FG network. Finely balanced FG-FG interactions modulated by O-GlcNAcylation, along with FG-importin-{beta} interactions, maintain liquidity and enrich importin-{beta} near the NPC periphery, while extended FG domains remain in the central channel to form the transport barrier. These findings reconcile conflicting models of FG-NUP organisation4,6,8,10,14,15 and explain the recent observations in high-resolution MINFLUX studies of importin-{beta} depletion from the pore centre in mammalian cells.16 Beyond resolving debates over FG-NUP behaviour, our study underscores the importance of studying IDPs in their cellular context, with broader implications for understanding IDP-related diseases, including viral infections, cancer, and neurodegenerative disorders.

biophysics↗

bioRxiv2025

Predicting animal movement with deepSSF: a deep learning step selection framework

O_LIPredictions of animal movement are vital for understanding and managing wild populations. However, the fine-scale, complex decision-making of animals can pose challenges for the accurate prediction of trajectories. Step selection functions (SSFs), a common tool for inferring relationships between animal movement and the environment, are also increasingly used to simulate animal trajectories for prediction. Although admitting a lot of flexibility, the SSF framework is limited to its reliance on pre-defined functional forms for fitting to data, and SSFs that involve complex functional forms to model detailed processes can be prohibitively difficult to fit and interpret. C_LIO_LIHere, we present deepSSF, an approach to fit and predict animal movement data using deep learning. Whilst not specific to any particular model, we denote the deepSSF approach as building and training a neural network architecture that receives multiple environmental layers and scalar values as inputs, and outputs a single layer representing the next-step probability. To demonstrate a deepSSF model, we build a model in PyTorch that has distinct but interacting habitat selection and movement subnetworks, which allows for explicit representation of both processes and interpretable intermediate outputs. We apply our model to GPS data of introduced water buffalo (Bubalus bubalis) in the tropical savannas of Northern Australia. C_LIO_LIOur deepSSF model was able to learn features that are present in the habitat covariate layers, such as linear features (rivers, forest edges), and the composition of certain habitat areas, without having to specify them pre-emptively within the SSF framework. It was also able to capture the complex interactions between the habitat covariates as well as temporal dynamics across time of day and year. C_LIO_LIWe expect that the deepSSF approach will generate accurate and informative animal movement trajectories, which can be used for deepening our understanding of animal-environment systems and for the practical management of species. Considering the wide range of deep learning tools, the deepSSF approach could be extended to represent memory and social dynamic processes, with the potential for integrating non-spatial data sources such as accelerometers and physiological sensors. C_LI

ecology↗

bioRxiv2024

Optimising crown-of-thorns starfish control effort on the Great Barrier Reef

Outbreaks of crown-of-thorns starfish Acanthaster planci (COTS), a disruptive coral-eating predator, are responsible for almost half of total coral cover loss on Australias Great Barrier Reef. As the pressures of climate change continue to intensify the frequency and severity of disturbance events such as cyclones and coral bleaching, efficiently managing COTS outbreaks is essential for reef protection. We aim to understand how the spatial distribution and intensity of crown-of-thorns starfish control - specifically manual culling of COTS by human divers - can impact coral cover on the GBR. We construct a metapopulation model based on a predator-prey model with larval dispersal and removal of crown-of-thorns starfish to simulate and compare spatial control strategies. When outbreaks begin on reefs between Cairns and Cooktown, we found the best strategy is to target those reefs at the source of the COTS outbreak. Increasing the spatial spread of control results in a larger spatial area protected across the GBR, but a lower total coral cover on the GBR. Our findings suggest that carefully targeting future control by considering larval connectivity patterns and spatial control strategies could lead to more efficient crown-of-thorns management. With the increasing pressures of climate change, any efficiency gains in reef management will prove beneficial for the Great Barrier Reef.

ecology↗

bioRxiv2024

Simulating animal movement trajectories from temporally dynamic step selection functions

Understanding and predicting animal movement is fundamental to ecology and conservation management. Models that estimate and then predict animal movement and habitat selection parameters underpin diverse conservation applications, from mitigating invasive species spread to enhancing landscape connectivity. However, many predictive models overlook fine-scale temporal dynamics within their predictions, despite animals often displaying fine-scale behavioural variability that might significantly alter their movement, habitat selection and distribution over time. Incorporating fine-scale temporal dynamics, such as circadian rhythms, within predictive models might reduce the averaging out of such behaviours, thereby enhancing our ability to make predictions in both the short and long term. We tested whether the inclusion of fine-scale temporal dynamics improved both fine-scale (hourly) and long-term (seasonal) spatial predictions for a significant invasive species of Northern Australia, the water buffalo (Bubalus bubalis). Water buffalo require intensive management actions over vast, remote areas and display distinct circadian rhythms linked to habitat use. To inform management operations we generated hourly and dry season prediction maps by simulating trajectories from static and temporally dynamic step selection functions (SSFs) that were fitted to the GPS data of 13 water buffalo. We found that simulations generated from temporally dynamic models replicated the buffalos crepuscular movement patterns and dynamic habitat selection, resulting in more informative and accurate hourly predictions. Additionally, when the simulations were aggregated into long-term predictions, the dynamic models were more accurate and better able to highlight areas of concentrated habitat use that might indicate high-risk areas for environmental damage. Our findings emphasise the importance of incorporating fine-scale temporal dynamics in predictive models for species with clear dynamic behavioural patterns. By integrating temporally dynamic processes into animal movement trajectories, we demonstrate an approach that can enhance conservation management strategies and deepen our understanding of ecological and behavioural patterns across multiple timescales.

ecology↗

bioRxiv2023

Do not let the beginning trap you! On inhibition, associative creative chains, and hopfield neural networks

Creative thinking stems from the cognitive process that fosters new ideas and problem-solving solutions. Creative cognition in emerging artificial intelligence systems and neural models may reduce complexity in understanding creative cognition. Hopfield Neural Networks (HNN) is a simple neural model known for its biological plausibility to store and retrieve neuron patterns. The primary objective is to demonstrate that ideas, symbolized as patterns of ones and zeros representing clusters of neurons that synchronize their firing, can be stored within HNN and establish connections through correlation. The network can converge towards these ideas by manually adjusting specific state parameters, effectively controlling the overall network activity. When the second closest stored pattern deviated significantly from the input pattern, the networks ability to converge decreased, enabling it to connect the input and patterns. Thus, we suggest employing HNN for the first time to create a model that emulates creative thinking processes, including making meaningful links between seemingly unrelated ideas. We implemented certain modifications to the original HNN, including introducing pattern weight control, which provides a robust representation for content addressable memory and illustrates conceptual links in stored data, a step towards the larger framework of creativity. We have made progress in identifying two mechanisms that could assist in managing the dynamics of the network and the formation of associative links. These mechanisms are related to the activation threshold of the neurons and the inhibitory stimulus on the stored patterns.

neuroscience↗

bioRxiv2023

Reconstructing Creative Thoughts: Hopfield Neural Networks

From a brain processing perspective, the perception of creative thinking is rooted in the underlying cognitive process, which facilitates exploring and cultivating novel avenues and problem-solving strategies. However, it is challenging to emulate the intricate complexity of how the human brain presents a novel way to uncover unique solutions. One potential approach to mitigate this complexity is incorporating creative cognition within the evolving artificial intelligence systems and associated neural models. Hopfield Neural Networks (HNN) are commonly acknowledged as a simplified neural model, renowned for their biological plausibility to store and retrieve information, specifically patterns of neurons. Therefore, we propose for the first time using HNN to generate a potential model that emulates specific cognitive processes of creative thinking based on generating meaningful links between seemingly disparate concepts.

neuroscience↗

bioRxiv2022

Fish Aggregating Devices could enhance the effectiveness of blue water MPAs

In the past two decades, drifting fish aggregation devices (FADs) have revolutionised pelagic fisheries, and are now responsible for the majority of tuna purse seine catches. Here, we argue that by taking advantage of the same proven aggregative properties, FADs could be used to enhance the benefits provided by blue water Marine Protected Areas (MPAs). Using models of commercially-targeted fish populations, we explore the potential benefits that could be achieved if unfished conservation FADs were positioned within blue water MPAs. Our results suggest that conservation FADs could deliver benefits, both to target species and the broader ecosystem. By increasing the residence time of exploited species, conservation FADs will reduce average mortality rates inside MPAs. By increasing the local density of species whose populations are depressed by exploitation, FADs can also improve the function of ecosystems in blue water MPAs. Conservation FADs could therefore amplify the benefits of blue water MPAs. We find this amplification is largest in those contexts where blue water MPAs have attracted the most criticism - when their area is small compared to both the open ocean and the distribution of fish stocks that move through them.

ecology↗

bioRxiv2021

Prioritising the eradication of invasive species from island archipelagos with high reinvasion risk

Eradicating invasive species from islands is a proven method for safeguarding threatened and endangered species from extinction. Island eradications can deliver lasting benefits, but require large up-front expenditure of limited conservation resources. The choice of islands must therefore be prioritised. Numerous tools have been developed to prioritise island eradications, but none fully account for the risk of those eradicated species later returning to the island: reinvasion. In this paper, we develop a prioritisation method for island eradications that accounts for the complexity of the reinvasion process. By merging spatially-explicit metapopulation modelling with stochastic dynamic optimisation techniques, we construct a decision-support tool that optimises conservation outcomes in the presence of reinvasion risk. We applied this tool to two different case studies - rat (Rattus rattus) invasions in the Seaforth archipelago in New Zealand, and cane toad (Rhinella marina) invasions in the Dampier archipelago in Australia - to illustrate how state-dependent optimal policies can maximise expected conservation gains. In both case studies, incorporating reinvasion risk dramatically altered the optimal order of island eradications, and improved the potential conservation benefits. The increase in benefits was larger in Dampier than Seaforth (42% improvement versus 6%), as a consequence of both the characteristics of the invasive species, and the arrangement of the islands. Our results illustrate the potential consequences of ignoring reinvasion risk, and demonstrate that including reinvasion in eradication prioritisation can dramatically improve conservation outcomes.

ecology↗

bioRxiv2021

Choosing optimal trigger points for ex situ, in toto conservation of single population threatened species

Many endangered species exist in only a single population, and almost all species that go extinct will do so from their last remaining population. Understanding how to best conserve these single population threatened species (SPTS) is therefore a distinct and important task for threatened species conservation science. As a last resort, managers of SPTS may consider taking the entire population into captivity - ex situ, in toto conservation. In the past, this choice has been taken to the great benefit of the SPTS, but it has also lead to catastrophe. Here, we develop a decision-support tool for planning when to trigger this difficult action. Our method considers the uncertain and ongoing decline of the SPTS, the possibility that drastic ex situ action will fail, and the opportunities offered by delaying the decision. Specifically, these benefits are additional time for ongoing in situ actions to succeed, and opportunities for the managers to learn about the system. To illustrate its utility, we apply the decision tool to four retrospective case-studies of declining SPTS. As well as offering support to this particular decision, our tool illustrates why trigger points for difficult conservation decisions should be formulated in advance, but must also be adaptive. A trigger-point for the ex situ, in toto conservation of a SPTS, for example, will not take the form of a simple threshold abundance.

ecology↗

bioRxiv2019

Modelling the effects of leaky predator-exclusion fences and their surrounding halo

Terrestrial fauna of the southern hemisphere, particularly Australia and New Zealand, have suffered significant declines and extinctions due to predation by introduced red foxes Vulpes vulpes and cats Felis catus. Predator-exclusion fences offer protection to these threatened species and allow their populations to persist and even flourish within their boundaries. These fences have traditionally been designed to stop the movement of both the invasive predators (into the fence), and the native animals (out of the fence). However, recent theory and evidence suggest that when native animals are able to move across the fence, they can create a population beyond the fence boundary. This phenomena has been called a \"halo effect\", and has the potential to both expand the direct and indirect benefits of predator-exclusion fences, and to reduce their negative effects. However, the conditions under which such an effect can be achieved are uncertain. They include questions about which native species could support a meaningful halo, what levels of predation outside the fence can be tolerated, and how permeable the fence would need to be. Here, we formulate this problem as both a simple two-patch model and a spatial partial differential equation model. We use the two approaches to explore the conditions under which a halo can deliver conservation benefits, and offer clear insights into the problem.

ecology↗

bioRxiv2019

Superadditive and subadditive dynamics are not inherent to the types of interacting threat

Species and ecosystems usually face more than one threat. The damage caused by these multiple threats can accumulate nonlinearly: either subadditively, when the joint damage of combined threats is less than the damages of both threats individually added together, or superadditively, when the joint damage is greater than the two individual damages added together. These additivity dynamics are commonly attributed to the nature of the threatening processes, but conflicting empirical observations challenge this assumption. Here, we use a theoretical model to demonstrate that the additivity of threats can change with different magnitudes of threat impacts (effect of a threat on the population parameter, like growth rate). We use a harvested single-species population model to integrate the effects of multiple threats on equilibrium abundance. Our results reveal that threats do not always display consistent additive behavior, even in simple systems. Instead, their additivity depends on the magnitudes of the impacts of two threats, and the population parameter that is impacted by each threat. In our model specifically, when multiple threats have a low impact on the growth rate of a population, they display superadditive dynamics. In contrast, threats that impact the species carrying capacity are always additive or subadditive. These dynamics can be understood by reference to the curvature of the relationship between a given population parameter (e.g., growth) and equilibrium population size. Our results suggest that management actions can achieve amplified benefits if they target low-amplitude threats that affect the growth rate, since these will be in a superadditive phase. More generally, our results suggest that cumulative impact theory should focus more than previously on the magnitude of the impact on the population parameter, and should be cautious about attributing additive dynamics to particular threat combinations.

ecology↗