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Shai Pilosof

Publications and source records attributed to Shai Pilosof.

5 recordsLinked to original sources

Interactively visualizing biological multilayer networks using MiRA

Multilayer networks are widely used across biology to represent systems in which complex networks vary across space, time, or interaction types. However, interactive visualization tools remain limited. We present MiRA (Multilayer Interactive Rendering Application), a browser-based, installation-free web application for visualizing biological multilayer networks. MiRA offers seven complementary visualization modes and interactive features that enable researchers to visually navigate the high complexity of multilayer networks for research and education.

cs.SI

Learning graph topology from metapopulation epidemic encoder-decoder

Metapopulation epidemic models are a valuable tool for studying large-scale outbreaks. With the limited availability of epidemic tracing data, it is challenging to infer the essential constituents of these models, namely, the epidemic parameters and the relevant mobility network between subpopulations. Either one of these constituents can be estimated while assuming the other; however, the problem of their joint inference has not yet been solved. Here, we propose two encoder-decoder deep learning architectures that infer metapopulation mobility graphs from time-series data, with and without the assumption of epidemic model parameters. Evaluation across diverse random and empirical mobility networks shows that the proposed approach outperforms the state-of-the-art topology inference. Further, we show that topology inference improves dramatically with data on additional pathogens. Our study establishes a robust framework for simultaneously inferring epidemic parameters and topology, addressing a persistent gap in modeling disease propagation.

cs.LG

Heterogeneity drives plasmid maintenance in large microbial communities

Microbiomes are complex systems comprised of many interacting species. Species can survive harsh or changing conditions by rapid adaptation, a process accelerated by the exchange of genetic material between different species through horizontal gene transfer. Conjugative plasmids are ubiquitous mobile genetic elements that mediate such exchanges both within and between species. Therefore, predicting whether a plasmid can invade and be maintained by a microbial community is critical, for example when assessing the risks of antimicrobial resistance gene spread in commensal or environmental microbiomes. However, existing theory developed to assist such predictions has generally focused on the balance among plasmid costs, benefits, and infection rates, overlooking other relevant factors such as the inherent dynamics and diversity of microbiomes. Here, we hypothesize that plasmid persistence in the absence of positive selection can arise purely from the heterogeneity present in large and diverse microbial communities. We introduce a generic model that integrates population-level dynamics with plasmid conjugation. Using this model, we show that we can predict plasmid maintenance, and that the probability for a plasmid to be maintained depends on traits of the plasmid, most importantly the conjugation rate, and the species abundance distribution of the community. Then, using both empirical abundance data and extensive numerical simulations, we demonstrate that the inherent randomness of ecological interactions and conjugation rates enables plasmid persistence -- even in the absence of positive selection. Our findings thus suggest that natural microbial communities are likely to maintain plasmids indefinitely, offering a new perspective on the spread, maintenance, and ubiquity of plasmids.

q-bio.PE

The Multilayer Nature of Ecological Networks

Although networks provide a powerful approach to study a large variety of ecological systems, their formulation does not typically account for multiple interaction types, interactions that vary in space and time, and interconnected systems such as networks of networks. The emergent field of `multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it specifically allows one to differentiate and model `intralayer' and `interlayer' connectivity. The framework provides a set of concepts and tools that can be adapted and applied to ecology, facilitating research on high-dimensional, heterogeneous systems in nature. Here, we formally define ecological multilayer networks based on a review of previous and related approaches, illustrate their application and potential with analyses of existing data, and discuss limitations, challenges, and future applications. The integration of multilayer network theory into ecology offers largely untapped potential to further address ecological complexity, to ultimately provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.

q-bio.QM

Asymmetric disease dynamics in multihost interconnected networks

Epidemic spread in single-host systems strongly depends on the population's contact network. However, little is known regarding the spread of epidemics across networks representing populations of multiple hosts. We explored cross-species transmission in a multilayer network where layers represent populations of two distinct hosts, and disease can spread across intralayer (within-host) and interlayer (between-host) edges. We developed an analytic framework for the SIR epidemic model to examine the effect of (i) source of infection and (ii) between-host asymmetry in infection probabilities, on disease risk. We measured risk as outbreak probability and outbreak size in a focal host, represented by one network layer. Numeric simulations were used to validate the analytic formulations. We found that outbreak probability is determined by a complex interaction between source of infection and between-host infection probabilities, whereas outbreak size is mainly affected by the non-focal host to focal host infection probability alone. Hence, inter-specific asymmetry in infection probabilities shapes disease dynamics in multihost networks. These results expand current theory of monolayer networks, where outbreak size and probability are considered equal, highlighting the importance of considering multiple measures of disease risk. Our study advances understanding of multihost systems and non-biological systems with asymmetric flow rates.

q-bio.PE