PLoS Computational Biology最新文献

Patient-derived cells (PDC) mouse xenografts are increasingly important tools in glioblastoma (GBM) research, essential to investigate case-specific growth patterns and treatment responses. Despite the central role of xenograft models in the field, few good simulation models are available to probe the dynamics of tumor growth and to support therapy design. We therefore propose a new framework for the patient-specific simulation of GBM in the mouse brain. Unlike existing methods, our simulations leverage a high-resolution map of the mouse brain anatomy to yield patient-specific results that are in good agreement with experimental observations. To facilitate the fitting of our model to histological data, we use Approximate Bayesian Computation. Because our model uses few parameters, reflecting growth, invasion and niche dependencies, it is well suited for case comparisons and for probing treatment effects. We demonstrate how our model can be used to simulate different treatment by perturbing the different model parameters. We expect in silico replicates of mouse xenograft tumors can improve the assessment of therapeutic outcomes and boost the statistical power of preclinical GBM studies.

Drugs play a central role in limiting bacterial population spread, yet laboratory studies typically assume well-mixed environments when assessing microbial drug responses. In contrast, bacteria in the human body often occupy spatially structured habitats where drug concentrations vary. Understanding how this heterogeneity shapes growth and decline is therefore essential for controlling infections and mitigating resistance evolution. Here, we developed a minimal robot-automated system to study how spatial drug heterogeneity affects short-term population dynamics in E. faecalis, a Gram-positive opportunistic pathogen. This system was combined with a theoretical framework to interpret and explain the observed outcomes. We first recapitulated the classic critical-patch-size model result: in a spatially homogeneous environment, a population persists in a finite domain only when growth outpaces diffusive losses at the boundaries. In heterogeneous environments, we found certain conditions that population persistence can depend critically on the spatial arrangement of the drug, even when its total amount is fixed. Using theoretical and experimental approaches, we identified the arrangements that produce the strongest growth and the fastest decline, revealing the range of possible outcomes under drug heterogeneity. We further tested this framework in more complex environments, including ring-shaped communities, and observed consistent arrangement-dependent behavior. Overall, our results extend the classical growth-condition framework to general heterogeneous environments and demonstrate that spatial drug arrangement - not only total dose - can strongly influence bacterial population dynamics. These findings highlight the importance of spatially structured dosing strategies and motivate further theoretical and experimental investigation.

[This corrects the article DOI: 10.1371/journal.pcbi.1013452.].

Bayesian Metabolic Control Analysis (BMCA) is a promising framework for inferring metabolic control coefficients in data-limited scenarios, combining Bayesian inference with linear-logarithmic (lin-log) rate laws. These metabolic control coefficients quantify how changes in enzyme activities affect steady-state fluxes and metabolite concentrations across a metabolic network. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA's ability to infer elasticity values, flux control coefficients (FCC), and concentration control coefficients (CCC) under varying data availability conditions using three synthetic metabolic network models. We demonstrate that BMCA predictions are highly dependent on the inclusion of flux and enzyme concentration data, with the omission of these datasets leading to severe inaccuracies. In our synthetic, enzyme-perturbation datasets, external metabolite concentrations had minimal impact and, in some cases, their exclusion improved predictions; when external-nutrient perturbations were introduced and those concentrations were observed, gains were at most modest. Additionally, we find that posterior estimation with both ADVI and HMC can underestimate large-magnitude elasticities in our synthetic settings, with ADVI showing somewhat higher variance under strong up-regulation; thus, recovering |elasticity| [Formula: see text]1.5 remains challenging regardless of the inference engine. ADVI also fails to accurately infer allosteric interactions, even when regulatory effects are strong. While BMCA maintains reasonable accuracy in partially recovering the rankings of the highest FCC values, its estimates of absolute values remain constrained by prior assumptions and data limitations. Our findings reveal the BMCA algorithm's strengths and weaknesses, providing guidance on its application in metabolic engineering, and highlighting the need for methodological refinements to enhance its predictive capabilities.

From cortical synfire chains to hippocampal replay, the idea that neural populations can be activated sequentially with precise spike timing is thought to be essential for several brain functions. It has been shown that neuronal sequences with weak feedforward connectivity can be replayed due to amplification via intra-assembly recurrent connections. However, the mechanisms behind this phenomenon are still unclear. Here, we simulate spiking networks with different excitatory and inhibitory connectivity and find that an exclusively excitatory network is sufficient for this amplification to occur. To explain the spiking network behavior, we introduce a population model of membrane-potential distributions, and we analytically describe how different connectivity structures determine replay speed, with weaker feedforward connectivity generating slower and wider pulses that can be sustained by recurrent connections. Pulse propagation is facilitated if the neuronal membrane time constant is large compared to the pulse width. Together, our simulations and analytical results predict the conditions for replay of neuronal assemblies.

T cell receptors (TCRs) recognize peptides presented by polymorphic human leukocyte antigen (HLA) molecules, but HLA genotype data are often missing from TCR repertoire sequencing studies. To address this, we developed TCR2HLA, an open-source tool that infers HLA genotypes from TCRβ repertoires. Expanding on work linking public TRBV-CDR3 sequences to HLA genotypes, we incorporated "quasi-public" metaclonotypes - composed of rarer TCRβ sequences with shared amino acid features - enriched by HLA genotypes. Using four TCRβseq datasets from 3,150 individuals, we applied TRBV gene partitioning and locality-sensitive hashing to identify ~96,000 TCRβ features strongly associated with specific HLA alleles from 71M input TCRs. Binary HLA classifiers built with these features achieved high balanced accuracy (>0.9) across common HLA-A (9/12), B (9/12), C (6/13), DRB1 (11/11) alleles and prevalent DPA1/DPB1 (6/10), DQA1/DQB1 (8/17) heterodimers. We also introduced a high-sensitivity calibration to support predictions in samples with as few as 5,000 unique clonotypes. Calibrated predictions with confidence filtering improved reliability. Beyond genotype imputation, TCR2HLA enables the discovery of novel HLA- and exposure-associated TCRs, as shown by the identification of SARS-CoV-2 related TCRs in a large COVID-19 dataset lacking HLA data. TCR2HLA provides a scalable framework for bridging the gap between TCRseq data and HLA genotype for biomarker discovery.

[This corrects the article DOI: 10.1371/journal.pcbi.1013213.].

In genome-wide epigenetic studies, determining how exposures (e.g., Single Nucleotide Polymorphisms) affect outcomes (e.g., gene expression) through intermediate variables, such as DNA methylation, is a key challenge. Mediation analysis provides a framework to identify these causal pathways; however, testing for mediation effects involves a complex composite null hypothesis. Existing methods, such as Sobel's test or the Max-P test, are often underpowered in this context because they rely on null distributions determined under only a subset of the null space and are not optimized for the multiple testing burden inherent in high-dimensional data. To address these limitations, we introduce MLFDR (Mediation Analysis using Local False Discovery Rates), a novel method for high-dimensional mediation analysis. MLFDR leverages local false discovery rates, calculated from the coefficients of structural equation models, to construct an optimal rejection region. We demonstrate theoretically and through simulation that MLFDR asymptotically controls the false discovery rate and achieves superior statistical power compared to recent high-dimensional mediation methods. In real data applications, MLFDR identified 20%-50% more significant mediators than existing methods, demonstrating its ability to uncover biological signals missed by conventional approaches.