Biology subjects

Miskov-Zivanov, N.

Publications and source records attributed to Miskov-Zivanov, N..

12 recordsLinked to original sources

bioRxiv2026

BELL: Biomodel Evidence and LLM-based Logic

Building accurate and predictive mechanistic models requires careful biological interaction curation and verification against existing knowledge. When done manually, these tasks become impractical, especially with massive extraction of interactions facilitated by advanced natural language processing methods and large language models (LLMs). We present BELL (Biomodel Evidence and LLM-based Logic), a biocuration support framework that automates evidence retrieval, scoring, and explanation for interaction-level verification. BELL processes each interaction through a five-step pipeline: entity grounding, database ranking, evidence retrieval from seven biological databases, a heuristic four-dimension programmatic scoring, and chain-of-thought explanation with a recommended curator action generated by LLMs. We applied BELL on 210 protein-protein interactions from a curated Glioblastoma Multiforme (GBM) model. Results show that 49.5% of interactions achieved HIGH confidence and 72.4% received a positive curator recommendation, while qualitative flags precisely directed curator attention to evidence gaps. BELL is integrated into the KALIMBA curation platform and available at www.boheme.pitt.edu/Kalimba.

bioinformatics↗

bioRxiv2026

Automated Knowledge Graph Construction for CAR T Cell Receptor Design via Hybrid Text Mining

Designing next-generation Chimeric Antigen Receptors (CARs) requires a systematic understanding of intracellular signaling domains and their downstream biological effects, yet no comprehensive knowledge resource currently exists for this purpose. Here, we present an automated workflow that integrates multiple natural language processing and large language model tools to extract biomolecular interactions from PubMed literature and assemble them into a CAR T cell signaling knowledge graph. Our pipeline combines REACH, INDRA, and Llama 3 across 15 targeted search queries, yielding a directed multi-relational graph of [~]7,500 unique interactions among [~]1,800 entities, including proteins, biological processes, and chemicals. We further demonstrate that queries incorporating biological process ontology terms retrieve more interaction-rich papers than protein-name-only searches, offering practical guidance for future literature mining efforts. The resulting knowledge base provides a structured foundation for predicting T cell phenotypes and prioritizing intracellular domain candidates for CAR design, with broader applicability to knowledge-driven inference in immunotherapy research.

synthetic biology↗

bioRxiv2025

Generating Bounded Linear Temporal Logic in Systems Biology with Large Language Models

Motivation: Translating natural language biological discoveries into formal temporal logic specifications for model verification demands expertise most experimentalists lack. Bounded Linear Temporal Logic (BLTL), which attaches explicit time bounds to temporal operators, is well suited for capturing biological dynamics. However, no automated solution exists for this translation, limiting the broader adoption of statistical model checking in systems biology. Results: We present NL2BLTL, the first framework to automate natural language to BLTL translation for systems biology. NL2BLTL combines a synthetic dataset of 5,000 NL-BLTL pairs built via grammar-guided generation, Chain-of-Thought preprocessing to resolve linguistic ambiguity in biological hypotheses, and grammar-constrained decoding to enforce syntactic validity and prevent hallucinated variables or time bounds. Evaluated on a newly curated biomedical NL-BLTL dataset drawn from published T cell and pancreatic cancer models, NL2BLTL framework achieves 84.62% exact match and 100% syntactic validity, outperforming GPT-4 by over 16 points and improving 14 points over the base fine-tuned model.

systems biology↗

bioRxiv2025

GBM model refinement with literature curation, rule-based NLP, and LLMs

In this work, our goal was twofold: (1) improve an existing glioblastoma multiforme (GBM) executable mechanistic model and (2) evaluate the effectiveness traditional natural language processing (NLP) pipeline and the generative AI approach in the process of model improvement. We used a suite of graph metrics and tools for interaction filtering and classification to collect data and conduct the analysis. Our results suggest that a more comprehensive literature search is necessary to collect enough information through automated paper retrieval and interaction extraction. Additionally, we found that graph metrics present a promising approach for model refinement, as they can provide useful insights and guidance when selecting new information to be added to a mechanistic model.

systems biology↗

bioRxiv2024

Context-driven interaction retrieval and classification for modeling, curation, and reuse

Automated extraction of molecular interactions from scientific literature has outpaced the development of systematic methods for integrating this information with curated models and knowledge graphs. Here we present VIOLIN (Versatile Interaction Organizing to Leverage Information in Networks), a configurable, attribute-aware reconciliation framework that formally compares newly extracted interaction lists against structured baseline graphs. VIOLIN classifies each interaction as a corroboration, contradiction, flagged case, or extension, and supports configurable attribute inclusion strategies and mismatch semantics to adjust reconciliation strictness. We evaluate VIOLIN using interaction lists generated by two traditional NLP systems (REACH, INDRA) and two large language models (GPT-4.1, Llama 3) across multiple literature corpora and structurally distinct baseline graphs. Across all conditions, reconciliation outcomes were stable and interpretable, with extensions dominating and corroboration-contradiction balance reflecting intrinsic structural relationships between baseline graphs and extracted evidence. Sensitivity analyses demonstrate that attribute inclusion and classification scheme selection shift category boundaries predictably. Benchmark evaluations confirm high algorithmic correctness and alignment with expert curation. VIOLIN is publicly available as a Python package and through web-based interface (https://nmzlab.github.io/Tools-UI).

systems biology↗

bioRxiv2024

An Open-Set Semi-Supervised Multi-Task Learning Framework for Context Classification in Biomedical Texts

ObjectiveIn biomedical research, knowledge about the relationships between entities, including genes, proteins, and drugs, is vital for unraveling the complexities of biological processes and intracellular pathway mechanisms. Natural language processing (NLP) and text mining methods have shown great success in biomedical relation extraction (RE). However, extracted relations often lack contextual information like cell type, cell line, and intracellular location, which are crucial components of biological knowledge. Previous studies have treated this problem as a post hoc context-relation association task, which is limited by the absence of a golden standard corpus, leading to error propagation and decreased model performance. To address these challenges, we created CELESTA (Context Extraction through LEarning with Semi-supervised multi-Task Architecture), a framework for biomedical context classification, applicable to both open-set and close-set scenarios. MethodsTo capture the inherent relationships between biomedical relations and their associated contexts, we designed a multi-task learning (MTL) architecture that seamlessly integrates with the semi-supervised learning (SSL) strategies during training. Our framework addresses the challenges caused by the lack of labeled data by assuming that the unlabeled data contain both in-distribution (ID) and out-of-distribution (OOD) data points. Further, we created a large-scale dataset consisting of five context classification tasks by curating two large Biological Expression Language (BEL) corpora and annotating them with our new entity span annotation method. We developed an OOD detector to distinguish between ID and OOD instances within the unlabeled data. Additionally, we utilized the data augmentation method combined with an external database to enrich our dataset, providing exclusive features for models during training process. ResultsWe conducted extensive experiments on the dataset, demonstrating the effectiveness of the proposed framework in significantly improving context classification and extracting contextual information with high accuracy. The newly created dataset and code used for this work are publicly available on GitHub (https://github.com/pitt-miskov-zivanov-lab/CELESTA).

bioinformatics↗

bioRxiv2024

The BioRECIPE Knowledge Representation Format

AO_SCPLOWBSTRACTC_SCPLOWThe BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to facilitate seamless human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems biology community. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/579694v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@76a130org.highwire.dtl.DTLVardef@505cb4org.highwire.dtl.DTLVardef@1f64e68org.highwire.dtl.DTLVardef@195bd03_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2022

Context-aware information selection and model recommendation with ACCORDION

New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: comprehensive, retrieving relevant knowledge from a range of literature sources; very effective, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; selective, recommending only the most relevant, context-specific, and useful subset (15-20%) of candidate knowledge in literature; diverse, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions.

systems biology↗

bioRxiv2021

Classifying Literature Extracted Events for Automated Model Extension

1In this study, we investigate the integration of three previously developed tools: FLUTE, VIOLIN, and CLARINET. We show how using these tools together adds additional capabilities in extending models from relevant research literature. We illustrate how we plan to address current modeling pitfalls with these tools (such as machine reading errors and literature volume), and how we plan to use these tools as the foundation for an automated model extension framework. Documentation and links to our tools can be found at: violin-tool.readthedocs.io clarinet-docs.readthedocs.io flute.readthedocs.io

systems biology↗

bioRxiv2021

Automated verification, assembly, and extension of GBM stem cell network model with knowledge from literature and data

Signaling network models are usually assembled from information in literature and expert knowledge or inferred from data. The goal of modeling is to gain mechanistic understanding of key signaling pathways and provide predictions on how perturbations affect large-scale processes such as disease progression. For glioblastoma multiforme (GBM), this task is critical, given the lack of effective treatments and pace of disease progression. Both manual and automated assembly of signaling networks from data or literature have drawbacks. Existing GBM networks, as well as networks assembled using state-of-the-art machine reading, fall short when judged by the quality and quantity of information, as well as certain attributes of the overall network structure. The contributions of this work are two-fold. First, we propose an automated methodology for verification of signaling networks. Next, we discuss automation of network assembly and extension that relies on methods and resources used for network verification, thus, implicitly including verification in these processes. In addition to these methods, we also present, and verify a comprehensive GBM network assembled with a hybrid of manual and automated methods. Finally, we demonstrate that, while an automated network assembly is fast, such networks still lack precision and realistic network topology.

systems biology↗

bioRxiv2021

Computational modeling of cell signaling and mutations in pancreatic cancer

Published research articles are rich sources of data when the knowledge is incorporated into models. Complex biological systems benefit from computational modelings ability to elucidate dynamics, explain data and address hypotheses. Modeling of pancreatic cancer could guide treatment of this devastating disease that has a known mutational profile disrupting signaling pathways but no reliable therapies. The approach described here is to utilize discrete modeling of the major signaling pathways, metabolism and the tumor microenvironment including macrophages. This modeling approach allows for abstraction in order to assemble large networks to capture numerous facets of the biological system under investigation. The Hallmarks of Cancer are represented as the processes of apoptosis, autophagy, cell cycle progression, inflammation, immune response, oxidative phosphorylation and proliferation. The model is initialized with pancreatic cancer receptors and mutations and simulated in time. The model portrays the hallmarks of cancer and suggests combinations of inhibitors as therapies.

systems biology↗

bioRxiv2020

Post-translational covalent assembly of CAR and synNotch receptors for programmable antigen targeting

Chimeric antigen receptors (CARs) and synthetic Notch (synNotch) receptors are engineered cell-surface receptors that sense a target antigen and respond by activating T cell receptor signaling or a customized gene program, respectively. To expand the targeting capabilities of these receptors, we have developed switchable adaptor receptor systems for which receptor specificity can be directed post-translationally via covalent attachment of a co-administered antibody. Instead of directly targeting an antigen, our receptors contain the SNAPtag self-labeling enzyme, which reacts with benzylguanine (BG)-conjugated antibodies to assemble covalently-associated antigen receptors. We demonstrate that activation of SNAP-CAR and SNAP-synNotch receptors and their downstream effector functions can be successfully targeted by several clinically-relevant BG-conjugated antibodies in an antigen-specific and antibody dose-dependent manner. To better define parameters affecting receptor signaling, we developed a mathematical model of switchable receptor systems. SNAP receptors provide a powerful new strategy to post-translationally reprogram the targeting specificity of engineered cells.

synthetic biology↗