Biology subjects

Helikar, T.

Publications and source records attributed to Helikar, T..

13 recordsLinked to original sources

bioRxiv2026

MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models

Identifying robust biomarkers from high-dimensional biomedical data is a central challenge in translational research, but candidate rankings produced by any single feature-selection or classification method depend on algorithmic choices and rarely reproduce across pipelines. We present a disease-agnostic machine-learning framework that addresses this dependence by systematically benchmarking 25 (feature-selection x classifier) pipelines under five-fold stratified cross-validation, aggregating per-feature evidence by two independent methods (a weighted-selection consensus score and Robust Rank Aggregation), and characterizing the direction of each candidate using Cohens d. We demonstrate the framework on immune-response measurements from two clinical phases: SARS-CoV-2 hospitalization and intensive-care admission; obtaining cross-validated mean F1 above 0.99 with balanced classification errors and producing tiered, direction-aware biomarker lists per phase. Interleukin-18 (IL-18) reached the strongest tier in both phases with consistent direction. The framework generalizes to any binary clinical classification problem and supports principled, reproducible biomarker prioritization.

bioinformatics↗

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

MechAInistic: An LLM-guided Multi-Agent System for Reasoning over Genome-Scale Constraint-Based Metabolic Models

LLM agents are increasingly used for scientific reasoning, but their fluent-sounding outputs can diverge from verifiable computational evidence, limiting their reliability for biomedical hypothesis generation. We developed MechAInistic, a multi-agent system in which an independently configured Reviewer agent supervises a planning Architect agent at each stage of the workflow, with all reasoning grounded in executable mechanistic-model analyses rather than language-model text alone. The Reviewer scores plans and intermediate results against pre-specified rubrics and triggers re-planning or re-execution when scores fall below threshold, producing an auditable chain from a natural-language question to model-derived evidence and cited literature. We instantiate the system over paired constraint-based metabolic models using COBRApy, supporting pathway comparison, perturbation analysis, drug-target exploration, and literature interpretation across healthy and disease states. We evaluated MechAInistic on two immune-cell therapeutic hypothesis-generation tasks. For rheumatoid arthritis versus healthy naive B-cell models, it identified mitochondrial metabolic rewiring and nominated Devimistat/CPI-613 as an investigational OGDH-centered hypothesis. For multiple sclerosis CD4+ Th17 versus healthy models, it identified NADP-dependent isocitrate dehydrogenase as a candidate target and proposed ivosidenib, with vorasidenib as a mechanistically complementary alternative. Comparator analyses against general-purpose LLM systems showed that plausible biological narratives can lack auditable model grounding, whereas MechAInistic preserves the computational reasoning path from prompt to result. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/723319v4_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@4e591eorg.highwire.dtl.DTLVardef@1bceee3org.highwire.dtl.DTLVardef@e7a0f4org.highwire.dtl.DTLVardef@f80b3a_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2026

AutoRNAseq: Automated Bulk RNA-seq Analysis Pipeline

SummaryImproved accessibility of high-throughput RNA sequencing has increased the amount of data generated each year. This increase in data creates a need for reproducible pipelines that can process RNA-seq data consistently across experiments. AutoRNAseq addresses this need by providing a Snakemake-based workflow for bulk RNA-seq analysis by automating data retrieval, quality control, and gene quantification. Unlike existing RNA-seq workflows that require users to coordinate multiple pipelines and pre-configure reference data, AutoRNAseq provides a single, end-to-end workflow that automates data acquisition, reference preparation, quality control, alignment, and quantification with minimal user intervention. AutoRNAseq is applicable to any domain requiring consistently processed RNA-seq datasets, including bioinformatics, computational biology, and drug-response studies. Availability and ImplementationAutoRNAseq is implemented in Snakemake and available at https://gitlab.com/unebraska/lagbh-public/autornaseq. Documentation and example configuration files are provided in the GitLab README file and this papers Supplementary Information. The code to reproduce the statistics presented here is in the GitLab repository under the "publication" folder.

bioinformatics↗

bioRxiv2024

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2022

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Community-driven and highly interdisciplinary, the project is collaborative and supports community standards, open access, and the FAIR data principles. The coordination of community work allowed for an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework links key molecules highlighted from broad omics data analysis and computational modeling to dysregulated pathways in a cell-, tissue- or patient-specific manner. We also employ text mining and AI-assisted analysis to identify potential drugs and drug targets and use topological analysis to reveal interesting structural features of the map. The proposed framework is versatile and expandable, offering a significant upgrade in the arsenal used to understand virus-host interactions and other complex pathologies.

systems biology↗

bioRxiv2022

DBERlibR: Automated Data Analysis for Discipline-Based Education Research

Discipline-Based Education Research (DBER) scientists repeatedly analyze assessment data to ensure question items reliability and examine the efficacy of a new educational intervention. Analyzing assessment data comprises multiple steps and statistical techniques that consume much of researchers time and are error-prone. While education research continues to grow across many disciplines of science, technology, engineering, and mathematics (STEM), the DBER community lacks tools to streamline education research data analysis. DBERlibR--an R package to streamline and automate DBER data processing and analysis--fills this gap. The package reads user-provided assessment data, cleans them, merges multiple datasets (as necessary), checks assumption(s) for specific statistical techniques (as necessary), applies various statistical tests (e.g., one-way analysis of covariance, one-way repeated-measures analysis of variance), and presents and interprets the results all at once. By providing the most frequently used analytic techniques, this package will contribute to DBER by facilitating the creation and widespread use of evidence-based knowledge and practices. The outputs contain a sample interpretation of the results for users convenience. User inputs are minimal; they only need to prepare the data files as instructed and type a function in RStudio to conduct a specific data analysis.

scientific communication and education↗

bioRxiv2020

A comprehensive logic-based model of the human immune system to study the dynamics responses to mono- and coinfections

The immune system is a complex and dynamic network, crucial for combating infections and maintaining health. Developing a comprehensive digital twin of the immune system requires incorporating essential cellular components and their interactions. This study presents the first blueprint for an immune system digital twin, consisting of a comprehensive and simulatable mechanistic model. It integrates 51 innate and adaptive immune cells, 37 secretory factors, and 11 disease conditions, providing the foundation for developing a multi-scale model. The cellular-level model demonstrates its potential in characterizing immune responses to various single and combinatorial disease conditions. By making the model available in easy-to-use formats directly in the Cell Collective platform, the community can easily and further expand it. This blueprint represents a significant step towards developing general-purpose immune digital twins, with far-reaching implications for the future of digital twin technology in life sciences and healthcare, advancing patient care, and accelerating precision medicine.

systems biology↗

bioRxiv2020

Multi-Approach and Multi-Scale Model of CD4+ T Cells Predicts Switch-Like and Oscillatory Emergent Behaviors in Inflammatory Response to Infection

Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells which uses four modeling approaches to integrate processes taking place at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility, and facilitates data integration. Our technology helps capture emergent behaviors that arise from nonlinear dynamics interwoven across three scales. Multi-scale insights added to single-scale studies allowed us to identify switch-like and oscillatory behaviors of CD4+ T cells at the population level, which are both novel and immunologically important. We envision our model and the generic framework encompassing it to become the foundation of a more comprehensive model of the human immune system.

systems biology↗

bioRxiv2020

Using computational modeling to teach metabolism as a dynamic system improves student performance

Understanding metabolic function requires knowledge of the dynamics, interdependence, and regulation of biochemical networks. However, current approaches are not optimal to develop the needed mechanistic understanding, and misconceptions about biological processes persist even after graduation. To address these issues, we developed a computational modeling and simulation approach that employs scaffolded learning to teach biochemistry students about the regulation of metabolism. The power of the approach lies in students abilities to alter any component or connection in a modeled system and instantly observe the effects of their changes. We find that students who use our approach perform better on biochemistry metabolism questions compared to students in a course that did not use this approach. We also investigated performance by gender and found that our modules may have the potential to increase equity in education. We noted that students are generally positive about the approach and appreciate its benefits. Our modules provide life science instructors with a dynamic and systems-driven approach to teach metabolic regulation and control that improves learning and also equips students with important technical skills.

scientific communication and education↗

bioRxiv2020

Integrative computational approach identifies new targets in CD4+ T cell-mediated immune disorders

CD4+ T cells provide adaptive immunity against pathogens and abnormal cells, and they are also associated with various immune related diseases. CD4+ T cells metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. Genome-scale metabolic modeling offers an opportunity to accelerate drug discovery by providing high-quality information about possible target space in the context of a modeled disease. Here, we develop genome-scale models of naive, Th1, Th2 and Th17 CD4+ T cell subtypes to map metabolic perturbations in rheumatoid arthritis, multiple sclerosis, and primary biliary cholangitis. We subjected these models to in silico simulations for drug response analysis of existing FDA-approved drugs, and compounds. Integration of disease-specific differentially expressed genes with altered reactions in response to metabolic perturbations identified 68 drug targets for the three autoimmune diseases. In vitro experimental validations together with literature-based evidence showed that modulation of fifty percent of identified drug targets has been observed to lead to suppression of CD4+ T cells, further increasing their potential impact as therapeutic interventions. The used approach can be generalized in the context of other diseases, and novel metabolic models can be further used to dissect CD4+ T cell metabolism.

systems biology↗

bioRxiv2018

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks

Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualisation of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to software tools including GINsim, BioLQM, Pint, MaBoSS, and Cell Collective for the modelling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.

bioinformatics↗

bioRxiv2017

CancerDiscover: A configurable pipeline for cancer prediction and biomarker identification using machine learning framework

MotivationUse of various high-throughput screening techniques has resulted in an abundance of data, whose complete utility is limited by the tools available for processing and analysis. Machine learning holds great potential for deciphering these data in the context of cancer classification and biomarker identification. However, current machine learning tools require manual processing of raw data from various sequencing platforms, which is both tedious and time-consuming. The current classification tools lack flexibility in choosing the best feature selection algorithms from a range of algorithms and most importantly inability to compare various learning algorithms.\n\nResultsWe developed CancerDiscover, an open-source software pipeline that allows users to efficiently and automatically integrate large high-throughput datasets, preprocess, normalize, and selects best performing features from multiple feature selection algorithms. The pipeline lets users apply various learning algorithms and generates multiple classification models and evaluation reports that distinguish cancer from normal samples, as well as different types and subtypes of cancer.\n\nAvailability and ImplementationThe open source pipeline is freely available for download at https://github.com/HelikarLab/CancerDiscover.\n\nContactelikar2@unl.edu\n\nSupplementary InformationPlease refer to the CancerDiscover README (Supplementary File 1) for detailed instructions on installation and operation of the pipeline. For a list of available feature selection methods, see Supplementary File 2.

bioinformatics↗