Search

Vaccines have proven useful in curbing contagion from new strains of the SARS-CoV-2 virus. However, equitable vaccine allocation continues to be a significant challenge worldwide, necessitating a comprehensive allocation strategy incorporating heterogeneity in epidemiological and behavioral considerations. In this paper, we present a hierarchical allocation strategy that assigns vaccines to zones and their constituent neighborhoods cost-effectively, based on their population density, susceptibility, infected count, and attitude towards vaccinations. Moreover, it includes a module that tackles vaccine shortages in certain zones by locally transferring vaccines from zones with surplus vaccines. We leverage the epidemiological, socio-demographic, and social media datasets from Chicago and Greece and their constituent community areas to show that the proposed allocation approach assigns vaccines based on the chosen criteria and captures the effects of disparate vaccine adoption rates. We conclude the paper with a lowdown on future efforts to extend this study to design models for effective public policies and vaccination strategies that curtail vaccine purchase costs.

Predicting pandemic evolution involves complex modeling challenges, typically involving detailed discrete mathematics executed on large volumes of epidemiological data. Making them physics based provides added intuition as well as predictive value. Differential equations have the advantage of offering smooth, well-behaved solutions that try to capture overall predictive trends and averages. In this paper, the canonical susceptible-infected-recovered model is simplified, in the process generating quasi-analytical solutions and fitting functions that agree well with the numerics, as well as infection data across multiple countries. The equations provide an elegant way to visualize the evolution of the pandemic spread, by drawing equivalents with the similar dynamics of a particle, whose location over time represents the growing fraction of the population that is infected. This particle slides down a potential whose shape is set by model epidemiological parameters such as reproduction rate. Potential sources of errors and their growth over time are identified, and the uncertainties are mapped into a diffusive jitter that tends to push the particle away from its minimum. The combined physical understanding and analytical expressions offered by such an intuitive drift-diffusion model sets the foundation for their eventual extension to a multi-patch model while offering practical error bounds and could thus be useful in making policy decisions going forward.

COVID-19 is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2. While swift vaccine development and distribution have arrested the infection spread rate, it is necessary to design public policies that inform human mobility to curb outbreaks from future strains of the virus. While existing non-pharmaceutical approaches employing network science and machine learning offer promising travel policy solutions, they are guided by epidemiological and economic considerations alone and not human itineraries. We introduce an evolutionary algorithm (EA) based mobility scheduler that incorporates the personalized itineraries of individuals to determine the ideal timing of their mobility. We mathematically analyze the computational efficiency versus the optimality trade-off of the mobility scheduler. Through extensive simulations, we demonstrate that the EA-based mobility scheduler can balance the trade-off between (1) optimality and computational cost and (2) fair and preferential human mobility while reducing contagion under lockdown and no-lockdown as well as even and uneven human mobility traffic scenarios. We show that for two human mobility models, the scheduler exhibits lower infection numbers than a baseline trip-planning approach that directs human traffic along the least congested route to minimize contagion. We discuss that the EA scheduler lends itself to intricate mobility schedules of multiple destination choices with varying priorities and socioeconomic and demographic considerations.

A major hallmark of Alzheimer’s disease (AD) is the accumulation of extracellular aggregates of amyloid-β (Aβ). Structural polymorphism observed among Aβ fibrils in AD brains seem to correlate with the clinical subtypes suggesting a link between fibril polymorphism and pathology. Since fibrils emerge from a templated growth of low-molecular-weight oligomers, understanding the factors affecting oligomer generation is important. Membrane lipids are key factors to influence early stages of Aβ aggregation and oligomer generation, which cause membrane disruption. We have previously demonstrated that conformationally discrete Aβ oligomers can be generated by modulating the charge, composition, and chain length of lipids and surfactants. Here, we extend our studies into liposomal models by investigating Aβ oligomerization on large unilamellar vesicles (LUVs) of total brain extracts (TBE), reconstituted lipid rafts (LRs), or 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC). Varying the vesicle composition by specifically increasing the amount of GM1 gangliosides as a constituent, we found that only GM1-enriched liposomes induce the formation of toxic, low-molecular-weight oligomers. Furthermore, we found that the aggregation on liposome surface and membrane disruption are highly cooperative and sensitive to membrane surface characteristics. Numerical simulations confirm such a cooperativity and reveal that GM1-enriched liposomes form twice as many pores as those formed in the absence GM1. Overall, this study uncovers mechanisms of cooperativity between oligomerization and membrane disruption under controlled lipid compositional bias, and refocuses the significance of the early stages of Aβ aggregation in polymorphism, propagation, and toxicity in AD.

In this era of multi-drug resistance (MDR), antimicrobial peptides (AMPs) are one of the most promising classes of potential drug candidates to combat communicable as well as noncommunicable diseases such as cancers and diabetes. AMPs show a wide spectrum of biological activities which include antiviral, antifungal, anti-mitogenic, anticancer, and anti-inflammatory properties. Apart from these prospective therapeutic potentials, the AMPs can act as food preservatives and immune modulators. Therefore, AMPs have the potential to replace conventional drugs and may gain a significant global drug market share. Although several AMPs have shown therapeutic potential in vitro or in vivo, in most cases they have failed the clinical trial owing to various issues. In this review, we discuss in brief (i) molecular mechanisms of AMPs in various diseases, (ii) importance of AMPs in pharmaceutical industries, (iii) the challenges in using AMPs as therapeutics and how to overcome, (iv) available AMP therapeutics in market, and (v) AMPs under clinical trials. Here, we specifically focus on the therapeutic AMPs in the areas of dermatology, surgery, oncology and metabolic diseases.

Background: Mycobacterium tuberculosis, Vibrio cholerae, and pathogenic Escherichia coli are global concerns for public health. The emergence of multi-drug resistant (MDR) strains of these pathogens is creating additional challenges in controlling infections caused by these deadly bacteria. Recently, we reported that Acetate kinase (AcK) could be a broad-spectrum novel target in several bacteria including these pathogens. Methods: Here, using in silico and in vitro approaches we show that (i) AcK is an essential protein in pathogenic bacteria; (ii) natural compounds Chlorogenic acid and Pinoresinol from Piper betel and Piperidine derivative compound 6-oxopiperidine-3-carboxylic acid inhibit the growth of pathogenic E. coli and M. tuberculosis by targeting AcK with equal or higher efficacy than the currently used antibiotics; (iii) molecular modeling and docking studies show interactions between inhibitors and AcK that correlate with the experimental results; (iv) these compounds are highly effective even on MDR strains of these pathogens; (v) further, the compounds may also target bacterial two-component system proteins that help bacteria in expressing the genes related to drug resistance and virulence; and (vi) finally, all the tested compounds are predicted to have drug-like properties. Results and Conclusion: Suggesting that, these Piper betel derived compounds may be further tested for developing a novel class of broad-spectrum drugs against various common and MDR pathogens.

Proteinaceous deposits composed of fibrillar amyloid-β(Aβ) are the primary neuropathological hallmarks in Alzheimer disease (AD) brains. The nucleation-dependent aggregation of Aβis a stochastic process with frequently observed heterogeneity in aggregate size, structure, and conformation that manifests in fibril polymorphism. Emerging evidence indicates that polymorphic variations in Aβfibrils contribute to phenotypic diversity and the rate of disease progression in AD. We recently demonstrated that a dodecamer strain derived from synthetic Aβ42 propagates to morphologically distinct fibrils and selectively induces cerebral amyloid angiopathy phenotype in transgenic mice. This report supports the growing contention that stable oligomer strains can influence phenotypic outcomes by faithful propagation of their structures. Although we determined the mechanism of dodecamer propagation on a mesoscopic scale, the molecular details of the microscopic reactions remained unknown. Here, we have dissected and evaluated individually the kinetics of macroscopic phases in aggregation to gain insight into the process of strain propagation. The bulk rates determined experimentally in each phase were used to build an ensemble kinetic simulation model, which confirmed our observation that dodecamer seeds initially grow by monomer addition toward the formation of a key intermediate. This is followed by conversion of the intermediate to fibrils by oligomer elongation and association mechanisms. Overall, this report reveals important insights into the molecular details of oligomer strain propagation involved in AD pathology.

Network motifs, such as the feed-forward loop (FFL), introduce a range of complex behaviors to transcriptional regulatory networks, yet such properties are typically determined from their isolated study. We characterize the effects of crosstalk on FFL dynamics by modeling the cross regulation between two different FFLs and evaluate the extent to which these patterns occur in vivo. Analytical modeling suggests that crosstalk should overwhelmingly affect individual protein-expression dynamics. Counter to this expectation we find that entire FFLs are more likely than expected to resist the effects of crosstalk (≈20% for one crosstalk interaction) and remain dynamically modular. The likelihood that cross-linked FFLs are dynamically correlated increases monotonically with additional crosstalk, but is independent of the specific regulation type or connectivity of the interactions. Just one additional regulatory interaction is sufficient to drive the FFL dynamics to a statistically different state. Despite the potential for modularity between sparsely connected network motifs, Escherichia coli(E. coli) appears to favor crosstalk wherein at least one of the cross-linked FFLs remains modular. A gene ontology analysis reveals that stress response processes are significantly overrepresented in the cross-linked motifs found within E. coli. Although the daunting complexity of biological networks affects the dynamical properties of individual network motifs, some resist and remain modular, seemingly insulated from extrinsic perturbations—an intriguing possibility for nature to consistently and reliably provide certain network functionalities wherever the need arise.

The study of protein-protein interactions (PPIs) can help researchers raise new hypotheses about an organism or disease and guide new experiments. Various methods for the identification and analysis of PPIs have been discussed in the literature. These methods are generally categorized as experimental or computational - each having its own advantages and disadvantages. Experimental methods provide insights into the real state of biological interactions but tend to be time-consuming and costly. Computational methods, on the other hand, can study thousands of PPIs at a very low cost and in much less time; however, the accuracy of such in silico prediction results heavily depends on the specific computational approach used. Furthermore, there is no gold standard for these computational methods; a method that works well for predicting one PPI may perform poorly (by generating false positives and false negatives) for a different PPI. Therefore, all such predictions must be carefully validated, preferably with experimental data. In this paper, we review the existing computational approaches and emphasize the use of biological data as inputs for accurate predictions of PPIs. We also discuss how such input datasets and approaches may influence the sensitivity and specificity of the predicted PPI networks.

Wireless sensor networks (WSNs) form a critical component in modern computing applications; given their size, ability to process and communicate information, and to sense stimuli, they are a promising part of the Internet of Things. However, they are also plagued by reliability and node failure problems. Here we address these problems by using the Gene Regulatory Networks (GRNs) of the organism Escherichia coli—believed to be robust against signaling disruptions, such as gene failures—to study the transmission properties of randomly-generated WSNs and transmission structures derived from these genetic networks. Selection of sink nodes is crucial to the performance of these networks; here we introduce four sink-node selection techniques: two motif-based, an attractor based and a highest degree-based approach and perform comprehensive simulations to assess their performance. Specifically, we use NS-2 simulations to evaluate the packet transmission robustness properties of such GRN-derived communication structures as against typical randomly deployed sensor network topologies under varying channel loss models. Packet receipt rates are compared among these networks, which are shown to be higher using GRNs for the communication structure, rather than randomly generated WSNs. We also evaluate the performance of communication structures derived from existing biological network generation models to assess their applicability in providing robust communication. This work paves the way for future development of fault-tolerant and robust WSN deployment and routing algorithms based on the inherent signal transmission robustness properties of the gene regulatory network topologies.