Quantitative approach of Min protein researches and applications: Experiments, mathematical modeling and computer simulations

Cell division in prokaryotes is a process (known as binary fission) where the parent cell divides into daughter cells. In this process, the dynamics of Min proteins is an important factor in the accurate positioning at the midcell in Escherichia coli. This site specificity is regulated by the oscillatorybehavior of Min proteins. Numerous studies of Min protein dynamics have focused on dynamic spatialtemporal pattern formation, the movement mechanism and the biochemical basis function mostly using wet lab experiments, but the quantitative data remains limited. Thus in this research review, focus is on quantitative methodologies. Up-to-date information and findings regarding Min proteins, particularly MinD proteins obtained by using quantitative approaches such as experiments, modeling and simulations were provided. This review of quantitative techniques is expected to benefit not only those who want to conduct research in this area using more quantitative approaches, but also those who are interested in using qualitative data to support their findings

Autocatalytic Loop, Amplification and Diffusion: A Mathematical and Computational Model of Cell Polarization in Neural Chemotaxis

The chemotactic response of cells to graded fields of chemical cues is a complex process that requires the coordination of several intracellular activities. Fundamental steps to obtain a front vs. back differentiation in the cell are the localized distribution of internal molecules and the amplification of the external signal. The goal of this work is to develop a mathematical and computational model for the quantitative study of such phenomena in the context of axon chemotactic pathfinding in neural development. In order to perform turning decisions, axons develop front-back polarization in their distal structure, the growth cone. Starting from the recent experimental findings of the biased redistribution of receptors on the growth cone membrane, driven by the interaction with the cytoskeleton, we propose a model to investigate the significance of this process. Our main contribution is to quantitatively demonstrate that the autocatalytic loop involving receptors, cytoplasmic species and cytoskeleton is adequate to give rise to the chemotactic behavior of neural cells. We assess the fact that spatial bias in receptors is a precursory key event for chemotactic response, establishing the necessity of a tight link between upstream gradient sensing and downstream cytoskeleton dynamics. We analyze further crosslinked effects and, among others, the contribution to polarization of internal enzymatic reactions, which entail the production of molecules with a one-to-more factor. The model shows that the enzymatic efficiency of such reactions must overcome a threshold in order to give rise to a sufficient amplification, another fundamental precursory step for obtaining polarization. Eventually, we address the characteristic behavior of the attraction/repulsion of axons subjected to the same cue, providing a quantitative indicator of the parameters which more critically determine this nontrivial chemotactic response

Modelling Vesicular Release at Hippocampal Synapses

We study local calcium dynamics leading to a vesicle fusion in a stochastic, and spatially explicit, biophysical model of the CA3-CA1 presynaptic bouton. The kinetic model for vesicle release has two calcium sensors, a sensor for fast synchronous release that lasts a few tens of milliseconds and a separate sensor for slow asynchronous release that lasts a few hundred milliseconds. A wide range of data can be accounted for consistently only when a refractory period lasting a few milliseconds between releases is included. The inclusion of a second sensor for asynchronous release with a slow unbinding site, and thereby a long memory, affects short-term plasticity by facilitating release. Our simulations also reveal a third time scale of vesicle release that is correlated with the stimulus and is distinct from the fast and the slow releases. In these detailed Monte Carlo simulations all three time scales of vesicle release are insensitive to the spatial details of the synaptic ultrastructure. Furthermore, our simulations allow us to identify features of synaptic transmission that are universal and those that are modulated by structure

Multiple traces of monkeypox detected in non-sewered wastewater with sparse sampling from a densely populated metropolitan area in Asia

The monkeypox virus is excreted in the feces of infected individuals. Therefore, there is an interest in using viral load detection in wastewater for sentinel early surveillance at a community level and as a complementary approach to syndromic surveillance. We collected wastewater from 63 sewered and non-sewered locations in Bangkok city center between May and August 2022. Monkeypox viral DNA copy numbers were quantified using real-time polymerase chain reaction (PCR) and confirmed positive by Sanger sequencing. Monkeypox viral DNA was first detected in wastewater from the second week of June 2022, with a mean copy number of 16.4 copies/ml (n = 3). From the first week of July, the number of viral DNA copies increased to a mean copy number of 45.92 copies/ml. Positive samples were Sanger sequenced and confirmed the presence of the monkeypox virus. Our study is the first to detect monkeypox viral DNA in wastewater from various locations within Thailand. Results suggest that this could be a complementary source for detecting viral DNA and predicting upcoming outbreaks

Surveillance of avian influenza through bird guano in remote regions of the global south to uncover transmission dynamics

Avian influenza viruses (AIVs) pose a growing global health threat, particularly in low- and middle-income countries (LMICs), where limited surveillance capacity and under-resourced healthcare systems hinder timely detection and response. Migratory birds play a significant role in the transboundary spread of AIVs, yet data from key regions along migratory flyways remain sparse. To address these surveillance gaps, we conducted a study between December 2021 and February 2023 using fresh bird guano collected across 10 countries in the Global South. Here, we show that remote, uninhabited regions in previously unsampled areas harbor a high diversity of AIV strains, with H5N1 emerging as the most prevalent. Some of these H5N1 samples also carry mutations that may make them less responsive to the antiviral drug oseltamivir. Our findings documented the presence of AIVs in several underrepresented regions and highlighted critical transmission hotspots where viral evolution may be accelerating. These results underscore the urgent need for geographically targeted surveillance to detect emerging variants, inform public health interventions, and reduce the risk of zoonotic spillover

Exploring indoor and outdoor dust as a potential tool for detection and monitoring of COVID-19 transmission

This study investigated the potential of using SARS-CoV-2 viral concentrations in dust as an additional surveillance tool for early detection and monitoring of COVID-19 transmission. Dust samples were collected from 8 public locations in 16 districts of Bangkok, Thailand, from June to August 2021. SARS-CoV-2 RNA concentrations in dust were quantified, and their correlation with community case incidence was assessed. Our findings revealed a positive correlation between viral concentrations detected in dust and the relative risk of COVID-19. The highest risk was observed with no delay (0-day lag), and this risk gradually decreased as the lag time increased. We observed an overall decline in viral concentrations in public places during lockdown, closely associated with reduced human mobility. The effective reproduction number for COVID-19 transmission remained above one throughout the study period, suggesting that transmission may persist in locations beyond public areas even after the lockdown measures were in place

Impact of rainfall on the transmission of leptospirosis in Si Sa Ket, Thailand

Abstract Leptospirosis is a zoonotic disease found worldwide, but especially in tropical and sub-tropical countries. In Thailand during the rainy season, agricultural and livestock workers are the main occupational risk groups, who are likely to be in contact with contaminated environments. In this study, we aim to examine the impact of rainfall on the transmission of leptospirosis using a stochastic cellular automata model in Si Sa Ket, Thailand, which had the highest number of reported cases from 2014 to 2018. Two bi-dimensional square lattices are created to represent human and contaminated environmental lattices. The reported cases are used to fit in the simulation results by varying transmission probability. The transmission probability depends on sinusoidal function and the rainfall index, the results of which are compared. This study highlighted the way that seasonal rainfall contributed to the transmission dynamics of leptospirosis. The total epidemic size, which is the sum of the overtime cases, was investigated to find the critical transmission probability from the endemic to the epidemic state. Further study of other factors such as flooding and temperature, should be investigated for a better understanding of how the transmission of leptospirosis affects the environment.</jats:p

Reconstruction of the transmission dynamics of the first COVID-19 epidemic wave in Thailand

AbstractThailand was the first country reporting the first Coronavirus disease 2019 (COVID-19) infected individual outside mainland China. Here we delineated the course of the COVID-19 outbreak together with the timeline of the control measures and public health policies employed by the Thai government during the first wave of the COVID-19 outbreak in Thailand. Based on the comprehensive epidemiological data, we reconstructed the dynamics of COVID-19 transmission in Thailand using a stochastic modeling approach. Our stochastic model incorporated the effects of individual heterogeneity in infectiousness on disease transmission, which allows us to capture relevant features of superspreading events. We found that our model could accurately capture the transmission dynamics of the first COVID-19 epidemic wave in Thailand. The model predicted that at the end of the first wave, the number of cumulative confirmed cases was 3091 (95%CI: 2782–3400). We also estimated the time-varying reproduction number (Rt) during the first epidemic wave. We found that after implementing the nationwide interventions, the Rt in Thailand decreased from the peak value of 5.67 to a value below one in less than one month, indicating that the control measures employed by the Thai government during the first COVID-19 epidemic wave were effective. Finally, the effects of transmission heterogeneity and control measures on the likelihood of outbreak extinction were also investigated.</jats:p