Biology subjects

Li, L. X.

Publications and source records attributed to Li, L. X..

5 recordsLinked to original sources

bioRxiv2026

TabularQual: A spreadsheet-based format for annotating and curating logical models in SBML-qual

Logical models are widely used to study regulatory and signaling systems, yet their reuse, annotation, and exchange across tools remain challenging. Although SBML Level 3 Qualitative Models (SBML-qual) provides a standard representation, its XML-based syntax is difficult to inspect and edit directly. Here we introduce TabularQual, a spreadsheet-based, community-driven representation for Boolean and multi-valued logical models, together with a bidirectional converter between spreadsheets and SBML-qual. The converter is accessible programmatically and via a web interface to support diverse user workflows. We further describe two integration strategies that enable existing modeling tools to operate with TabularQual, either through SBML-qual exchange or via direct support. Case studies using the Stress Knowledge Map and CaSQ demonstrate how this integration supports model construction, curation, and reuse. TabularQual provides a practical bridge between human-readable model representations and standardized executable formats, supporting reproducibility, interoperability, and community-driven model development.

systems biology↗

bioRxiv2026

KGBN: Augmenting and optimizing logical gene regulatory networks using knowledge graphs

Logical gene regulatory network (GRN) models provide interpretable, mechanistic representations of cellular regulation and are widely used in systems biology. However, most existing models remain incomplete, context-specific, and difficult to extend to comprehensive GRNs, limiting their broader applicability to tasks such as drug-response prediction and precision medicine. Here, add some ideas about virtual twins: With AML patients, we can use comprehensive GRNs to predict drug response. We present KGBN (Knowledge Graph-augmented Boolean Network modeling), a computational workflow for systematically augmenting logical GRN models. KGBN incorporates regulatory interactions derived from curated knowledge graphs as alternative logical rules while preserving the validated structure of existing models. Rule probabilities are optimized against experimental data to represent regulatory uncertainty and achieve data-driven calibration. Applying KGBN to acute myeloid leukemia, we show that extending an existing GRN with drug-target pathways and training against ex vivo drug-response data yields mutation-specific models that recapitulate known therapeutic sensitivities and signaling dependencies, demonstrating the utility of KGBN for interpretable, context-aware GRN modeling. (Should be predict)

systems biology↗

bioRxiv2024

Merging logical models: An application in Acute Myeloid Leukemia modeling

MotivationGene regulatory network (GRN) models provide mechanistic understanding of genetic interactions that regulate gene expression and, consequently, influence cellular behavior. Dysregulated gene expression plays a critical role in disease progression and treatment response, making GRN models a promising tool for precision medicine. While researchers have built many models to describe specific subsets of gene interactions, more comprehensive models that cover a broader range of genes are challenging to build. This necessitates the development of automated approaches for merging existing models. ResultsWe present LM-Merger, a workflow for semi-automatically merging logical GRN models. The workflow consists of five main steps: (a) model identification, (b) model standardization and annotation, (c) model verification, (d) model merging, and (d) model evaluation. We demonstrate the feasibility and benefit of this workflow with two pairs of published models pertaining to acute myeloid leukemia (AML). The integrated models were able to retain the predictive accuracy of the original models, while expanding coverage of the biological system. Notably, when applied to a new dataset, the integrated models outperformed the individual models in predicting patient response. This study highlights the potential of logical model merging to advance systems biology research and our understanding of complex diseases. Availability and implementationThe workflow and accompanying tools, including modules for model standardization, automated logical model merging, and evaluation, are available at https://github.com/IlyaLab/LogicModelMerger/.

systems biology↗

bioRxiv2019

Cryptococcus neoformans evades pulmonary immunity by modulating xylose transport

Cryptococcus neoformans is a fungal pathogen that kills almost 200,000 people each year and is distinguished by abundant and unique surface glycan structures that are rich in xylose. A mutant strain of C. neoformans that cannot transport xylose precursors into the secretory compartment is severely attenuated in virulence in mice, yet surprisingly is not cleared. We found that this strain failed to induce the non-protective T helper cell type 2 (Th2) responses characteristic of wild-type infection, instead promoting sustained Interleukin (IL)-12p40 induction and increased IL-17A (IL-17) production. It also stimulated dendritic cells to release high levels of pro-inflammatory cytokines, a behavior we linked to xylose expression. We further discovered that inducible bronchus associated lymphoid tissue (iBALT) forms in response to infection with either wild-type cryptococci or the mutant strain with reduced surface xylose; although iBALT formation is slowed in the latter case, the tissue is better organized. Finally, our temporal studies suggest that lymphoid structures in the lung restrict the spread of mutant fungi for at least 18 weeks after infection, in contrast to ineffective control of the pathogen after infection with wild-type cells. These studies demonstrate the role of xylose in modulation of host response to a fungal pathogen and show that cryptococcal infection triggers iBALT formation.

microbiology↗

bioRxiv2019

Hypermutation in Cryptococcus reveals anovel pathway to 5-fluorocytosine (5FC) resistance

Drug resistance is a critical challenge in treating infectious disease. For fungal infections, this issue is exacerbated by the limited number of available and effective antifungal agents. Patients infected with the fungal pathogen Cryptococcus are most effectively treated with a combination of amphotericin B and 5-fluorocytosine (5FC). Isolates causing infections frequently develop resistance to 5FC although the mechanism of this resistance is poorly understood. Here we show that resistance is acquired more frequently in isolates with defects in DNA mismatch repair that confer an elevated mutation rate. Natural isolates of Cryptococcus with mismatch repair defects have recently been described and defective mismatch repair has been reported in other pathogenic fungi. In addition, whole genome sequencing was utilized to identify mutations associated with 5FC resistance in vitro. Using a combination of candidate-based Sanger and whole genome Illumina sequencing, the presumptive genetic basis of resistance in 16 independent isolates was identified, including mutations in the known resistance genes FUR1 and FCY2, as well as a novel gene, UXS1. Mutations in UXS1 lead to accumulation of a metabolic intermediate that appears to suppress toxicity of both 5FC and its toxic derivative 5FU. Interestingly, while a UXS1 ortholog has not been identified in other fungi like Saccharomyces cerevisiae, where the mechanisms underlying 5FC and 5FU resistance were elucidated, a UXS1 ortholog is found in humans, suggesting that mutations in UXS1 in cancer cells may also play a role in resistance to 5FU when used during cancer chemotherapy in humans.

microbiology↗