Biology subjects

Park, S. W.

Publications and source records attributed to Park, S. W..

13 recordsLinked to original sources

bioRxiv2026

Predicting the long-term impact of AI assistants on collective intelligence

Artificial intelligence (AI) assistants can improve individual performance but may weaken unaided performance and limit interest diversity, raising questions about their long-term effects on collective intelligence (CI). Here, we consider an evolutionary model in which individuals predict, consult AI, and imitate better-performing peers. Classic reward structures that promote CI without AI can fail under Chatbot AI, whose predictions depend on users' interests, by reducing interest diversity or propagating bias. Directly rewarding or penalizing AI use does not reliably mitigate these effects. Instead, a balanced incentive that preserves minority expertise and rewards collective contributions can promote CI, but an additional incentive is needed to restore unaided collective accuracy. Our framework offers an explanation for emerging evidence of AI's societal impact and clarifies its long-term implications.

evolutionary biology↗

bioRxiv2026

Strategic coexistence theory for evolutionary games

Evolutionary game theory and ecological coexistence theory both seek to predict the outcome of competition between biological entities, be they strategies or species, but the two fields have relied on largely separate approaches. Replicator equations provide a foundation for analyzing strategy competition, yet they do not explicitly separate the mechanisms that stabilize competition from those that equalize fitness differences between strategies. Here, we extend modern coexistence theory from community ecology to develop strategic coexistence theory (SCT), a framework for quantifying strategic niche and fitness differences between competing strategies. SCT recovers the classic classification of two-strategy games, distinguishing competitive exclusion, coexistence, and priority effects within a shared niche-fitness difference space. Applying SCT to five mechanisms for the evolution of cooperation further reveals that these mechanisms promote cooperation through distinct dynamical routes: kin selection, network reciprocity, and group selection primarily reduce fitness differences, whereas direct and indirect reciprocity destabilize competition and generate priority effects. Finally, applying SCT to microbial public-goods game shows that nonlinear microbial growth can both stabilize and equalize competition between co-operators and defectors, allowing coexistence. Together, these results show that SCT provides a complementary framework for comparing evolutionary games and teasing apart the coexistence mechanisms underlying strategy competition.

evolutionary biology↗

bioRxiv2026

Unifying coexistence theory for ecological communities and pathogen strain competition

Predicting the outcome of species or pathogen strain competition is a fundamental aim in both community ecology and infectious disease dynamics. Recent work revealed major challenges in predicting strain co-circulation from ecological coexistence theory due to overcompensatory competition among pathogens for susceptible resources, which can prevent the re-invasion of other competing strains. This resource overcompensation is ubiquitous across host-pathogen systems, but not apparent in simple Lotka-Volterra competition system, highlighting fundamental differences between pathogen strain and species competition. To address this gap, we begin by deriving classical models of pathogen strain and species competition from a resource-consumer model. This generalization illustrates that the relative time scale between resource and consumer dynamics limits the degree of resource overcompensation and therefore dictates the outcome of stochastic competition. Moreover, by introducing a mathematical framework for quantifying pairwise and higher-order terms from general competition systems, we show that a simple, ecological competition model can accurately predict the equilibrium dynamics of strain competition. A case study of rotavirus strain competition reveals that the ability to predict the outcome of strain competition from ecological theory depends on the underlying cross immunity structure. This work synthesizes coexistence theory across two fields by providing a unifying framework for predicting the outcome of complex ecological competition.

ecology↗

bioRxiv2025

Susceptible host dynamics explain pathogen resilience to perturbations

Interventions to slow the spread of SARS-CoV-2 significantly disrupted the transmission of other pathogens. As interventions lifted, whether and when human pathogens would eventually return to their pre-pandemic dynamics remains to be answered. Here, we present a framework for estimating pathogen resilience based on how fast epidemic patterns return to their pre-pandemic dynamics. By analyzing time series data from Hong Kong, Canada, Korea, and the US, we quantify the resilience of common respiratory pathogens and further predict when each pathogen will eventually return to its pre-pandemic dynamics. Our predictions are able to distinguish which pathogens should have returned already, and deviations from these predictions reveal long-term impacts of pandemic perturbations. We find a faster rate of susceptible replenishment underlies pathogen resilience and sensitivity to both large and small perturbations. Overall, our analysis highlights the persistent nature of common respiratory pathogens compared to vaccine-preventable infections, such as measles. Significance StatementCOVID-19 interventions slowed the transmission of many respiratory pathogens in different ways, raising questions about the mechanisms driving the variation in responses to interventions. To address this gap, we characterized the sensitivity of pathogen transmission to perturbations by quantifying how fast each pathogen returned to its pre-pandemic circulation patterns. We analyzed data from Hong Kong, Canada, Korea, and the US, and showed that common respiratory pathogens are far less sensitive to perturbations than measles, a vaccine-preventable infection. Finally, we showed that the speed of replenishment of the susceptible population--for example, through waning immunity--largely determines this sensitivity, suggesting that the persistence of common respiratory pathogens is likely driven by rapid susceptible replenishment.

ecology↗

bioRxiv2024

Thermodynamic Parameter Estimation for Modified Oligonucleotides Using Molecular Dynamics Simulations

This study investigates the thermodynamic parameters of 1,300 RNA/DNA hybrid duplexes, including both natural and chemically modified forms, using molecular dynamics (MD) simulations. Modified duplexes consist of phosphorothioate (PS) backbones and 2'-O-methoxyethyl (MOE) modifications, both commonly used in therapeutic oligonucleotides. Hybridization enthalpy and entropy were calculated from MD trajectories using molecular mechanics Poisson-Boltzmann surface area (MMPBSA) and molecular mechanics generalized Born surface area (MMGBSA) approaches. To address discrepancies with experimental data, we established empirical relationships by comparing calculated values with known experimental results of natural hybrid duplexes, then extended these relationships to the entire dataset. The corrected parameters were subsequently used to generate nearest-neighbor (NN) models, allowing for experimentally reliable melting temperature predictions. In this process, MMGBSA demonstrated superior predictive performance with high convergence and consistency for both natural and modified duplexes. Specifically, MMGBSA captured the stabilizing effects of the MOE modification with minimal bias, while MMPBSA exhibited greater variability and limited reliability. These findings highlight the potential of MMGBSA for accurate thermodynamic modeling of both natural and modified nucleic acids, providing a robust framework and experimentally meaningful insights for applications in nucleic acid-based therapeutic design and biotechnology.

biochemistry↗

bioRxiv2024

An accelerated molecular dynamics study for investigating protein pathways using the bond-boost hyperdynamics method

Molecular dynamics (MD) simulation is an important tool for understanding protein dynamics and the thermodynamic properties of proteins. However, due to the high computational cost of MD simulations, it is still challenging to explore a wide conformational space. To solve this problem, a variety of accelerated MD schemes have been proposed over the past few decades. The bond-boost method (BBM) is one of such accelerated MD schemes, which expedites escape events from energy basins by adding a bias potential based on changes in bond length. In this paper, we present a new methodology based on the BBM for accelerating the conformational transition of proteins. In our modified BBM, the bias potential is constructed using the dihedral angle and hydrogen bond, which are more suitable variables to monitor the conformational change in proteins. Additionally, we have developed an efficient algorithm compatible with the LAMMPS package. The method is validated with the conformational change of Adenylate kinase (AdK) by comparing the conventional and accelerated MD simulation results. Based on the accelerated MD results, the characteristics of AdK are investigated by monitoring the conformational transition pathways and the behavior of interdomain salt bridges. Moreover, the free energy landscape calculated using umbrella sampling confirms all the states identified by the accelerated MD simulation are the free energy minima and the system makes transitions following the path indicated by the free energy landscape. Our efficient approach is expected to play a key role in investigating transition pathways in a wide range of protein simulations.

biophysics↗

bioRxiv2024

A unified theory for predicting pathogen mutual invasibility and co-circulation

A key aim in the dynamics and control of infectious diseases is predicting competitive outcomes of pathogen interactions. Observed pathogen community structure indicates both considerable coexistence of related variants and spectacular instances of replacement, notably in seasonal influenza and SARS-CoV-2. However, an overall comparative quantitative framework for invasion and coexistence remains elusive. Inspired by modern ecological coexistence theory, we address this gap by developing pathogen invasion theory (PIT) and test the resulting framework against empirical systems. PIT predicts near-universal mutual invasibility across major pathogen systems, including seasonal influenza strains and SARS-CoV-2 variants. Predicting co-circulation from mutual invasibility further depends on the extent of overcompensatory susceptible depletion dynamics. Our analyses highlight the central role of immuno-epidemiological factors in determining pathogen coexistence and community structure.

ecology↗

bioRxiv2023

Synchrony of Bird Migration with Avian Influenza Global Spread; Implications for Vulnerable Bird Orders

Highly pathogenic avian influenza virus (HPAIV) A H5 clade 2.3.4.4 has caused worldwide outbreaks in domestic poultry, occasional spillover to humans, and increasing deaths of diverse species of wild birds since 2014. Wild bird migration is currently acknowledged as an important ecological process contributing to the global dispersal of HPAIV H5. However, it is unclear how seasonal bird migration facilitates global virus dispersal, and which avian species are exposed to HPAI H5 clade 2.3.4.4 viruses and where. To shed light on ongoing global outbreaks, we sought to explore these questions through phylodynamic analyses based on empirical data of bird movement tracking and virus genome sequences. First, based on viral phylogeography and bird migration networks, we demonstrate that seasonal bird migration can explain salient features of the global dispersal of clade 2.3.4.4. Second, we detect synchrony between the seasonality of bird annual cycle phases and virus lineage movements. We reveal the differing vulnerable bird orders at geographical origins and destinations of HPAIV H5 lineage movements. Notably, we highlight the potential importance of relatively under-discussed Suliformes and Ciconiiformes, in addition to Anseriformes and Charadriiformes, in virus lineage movements. Our study provides a phylodynamic framework that links the bird movement ecology and genomic epidemiology of avian influenza; it highlights the importance of integrating bird behaviour and life history in avian influenza studies.

ecology↗

bioRxiv2020

Host-pathogen genetic interactions underlie tuberculosis susceptibility in genetically diverse mice

The outcome of an encounter with Mycobacterium tuberculosis depends on the pathogens ability to adapt to the variable immune pressures exerted by the host. Understanding this interplay has proven difficult, largely because experimentally tractable animal models do not recapitulate the heterogeneity of tuberculosis disease. We leveraged the genetically diverse Collaborative Cross (CC) mouse panel in conjunction with a library of Mtb mutants to associate bacterial genetic requirements with host genetics and immunity. We report that CC strains vary dramatically in their susceptibility to infection and produce qualitatively distinct immune states. Global analysis of Mtb mutant fitness across the CC panel revealed that many virulence pathways are only in specific host microenvironments, identifying the large fraction of the pathogens genome that has been maintained to ensure fitness in a diverse population. Both immunological and bacterial traits were associated with genetic variants distributed across the mouse genome, identifying the specific host-pathogen genetic interactions that influence pathogenesis.

genetics↗

bioRxiv2020

Speed and strength of an epidemic intervention

An epidemic can be characterized by its speed (i.e., the exponential growth rate r) and strength (i.e., the reproductive number [R]). Disease modelers have historically placed much more emphasis on strength, in part because the effectiveness of an intervention strategy is typically evaluated on this scale. Here, we develop a mathematical framework for this classic, strength-based paradigm and show that there is a corresponding speed-based paradigm which can provide complementary insights. In particular, we note that r = 0 is a threshold for disease spread, just like [R] = 1, and show that we can measure the speed and strength of an intervention on the same scale as the speed and strength of an epidemic, respectively. We argue that, just as the strength-based paradigm provides the clearest insight into certain questions, the speed-based paradigm provides the clearest view in other cases. As an example, we show that evaluating the prospects of "test-and-treat" interventions against the human immunodeficiency virus (HIV) can be done more clearly on the speed than strength scale, given uncertainty in the proportion of HIV spread that happens early in the course of infection. We suggest that disease modelers should avoid over-emphasizing the reproductive number at the expense of the exponential growth rate, but instead look at these as complementary measures.

ecology↗

bioRxiv2019

Inferring generation-interval distributions from contact-tracing data

Generation intervals, defined as the time between when an individual is infected and when that individual infects another person, link two key quantities that describe an epidemic: the reproductive number, [Formula], and the rate of exponential growth, r. Generation intervals are often measured through contact tracing by identifying who infected whom. We study how observed intervals differ from \"intrinsic\" intervals that could be estimated by tracing individual-level infectiousness, and identify both spatial and temporal effects, including censoring (due to observation time), and the effects of susceptible depletion at various spatial scales. Early in an epidemic, we expect the variation in the observed generation intervals to be mainly driven by the censoring and the population structure near the source of disease spread; therefore, we predict that correcting observed intervals for the effect of temporal censoring but not for spatial effects will provide a spatially informed \"effective\" generation-interval distribution, which will correctly link r and [Formula]. We develop and test statistical methods for temporal corrections of generation intervals, and confirm our prediction using individual-based simulations on an empirical network.

ecology↗

bioRxiv2019

Bridging the gap between theory and data: the Red Queen Hypothesis for sex

Sexual reproduction persists in nature despite its large cost. The Red Queen Hypothesis postulates that parasite pressure maintains sexual reproduction in the host population by selecting for the ability to produce rare genotypes that are resistant to infection. Mathematical models have been used to lay theoretical foundations for the hypothesis; empirical studies have confirmed these predictions. For example, Lively used a simple host-parasite model to predict that the frequency of sexual hosts should be positively correlated with the prevalence of infection. Lively et al. later confirmed the prediction through numerous field studies of snail-trematode systems in New Zealand. In this study, we fit a simple metapopulation host-parasite coevolution model to three data sets, each representing a different snail-trematode system, by matching the observed prevalence of sexual reproduction and trematode infection among hosts. Using the estimated parameters, we perform a power analysis to test the feasibility of observing the positive correlation predicted by Lively. We discuss anomalies in the data that are poorly explained by the model and provide practical guidance to both modelers and empiricists. Overall, our study suggests that a simple Red Queen model can only partially explain the observed relationships between parasite infection and the maintenance of sexual reproduction.

ecology↗

bioRxiv2018

Exploring how generation intervals link strength and speed of epidemics

Infectious-disease outbreaks are often characterized by the reproductive number and exponential rate of growth r. provides information about out-break control and predicted final size. Directly estimating is difficult, while r can often be estimated from incidence data. These quantities are linked by the generation interval - the time between when an individual is infected by an infector, and when that infector was infected. It is often infeasible to ob-tain the exact shape of a generation-interval distribution, and to understand how this shape affects estimates of . We show that estimating generation interval mean and variance provides insight into the relationship between and r. We use examples based on Ebola, rabies and measles to explore approximations based on gamma-distributed generation intervals, and find that use of these simple approximations are often sufficient to capture the r- relationship and provide robust estimates of .

epidemiology↗