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Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Systems biology reveals NR2F6 and TGFB1 as key regulators of feed efficiency in beef cattle

Systems biology approaches are used as strategy to uncover tissue-specific perturbations and regulatory genes related to complex phenotypes. We applied this approach to study feed efficiency (FE) in beef cattle, an important trait both economically and environmentally. Poly-A selected RNA of five tissues (adrenal gland, hypothalamus, liver, skeletal muscle and pituitary) of eighteen young bulls, selected for high and low FE, were sequenced (100bp, pared-end). From the 17,354 expressed genes, 1,317 were prioritized by five selection categories (differentially expressed, harbouring SNPs associated with FE, tissue-specific, secreted in plasma and key regulators) and used for network construction. NR2F6 and TGFB were identified and validated by motif discovery as key regulators of hepatic inflammatory response and muscle tissue development, respectively, two biological processes demonstrated to be associated to FE. Moreover, we indicated potential biomarkers of FE which are related to hormonal control of metabolism and sexual maturity. By using robust methodologies and validation strategies, we confirmed main biological processes related to FE in Bos indicus and indicated candidate genes as regulators or biomarkers of superior animals.

systems biology↗
bioRxiv2023

Integrative systems biology framework discovers common gene regulatory signatures in multiple mechanistically distinct inflammatory skin diseases

AimMore than 20% of the population across the world is affected by non-communicable inflammatory skin diseases including psoriasis, atopic dermatitis, hidradenitis suppurativa, rosacea, etc. Many of these chronic diseases are painful and debilitating with limited effective therapeutic interventions. However, recent advances in psoriasis treatment have improved the effectiveness and provide better management of the disease. Similar effective treatments are also needed for other chronic skin disorders regulated molecular pathogenesis. This study aims to identify common regulatory pathways and master regulators that regulate molecular pathogenesis. MethodsWe designed an integrative systems biology framework to identify the significant regulators across several inflammatory skin diseases. We exploited the gene co-expression-, and protein-protein interaction-based networks to identify shared genes and pro components in different diseases with relevant functional implications. Further, we utilized network analytics to unravel the high-priority proteins, that play a crucial role in disease pathogenesis. ResultsWith conventional transcriptome analysis we identified 55 shared genes, which are enriched in several immune-associated pathways in eight inflammatory skin diseases. Our correlation-based co-expression networks identified the core co-expression networks and the integrative multi-omics interactomes providing core interactions across four of the eight diseases. Next, by implementing network analytics, we unraveled 55 high-value proteins as significant regulators in molecular pathogenesis. We believe that these significant regulators should be explored with critical experimental approaches to identify the putative drug targets for more effective treatments. As an example, we identified IKZF1 as a shared significant master regulator in three inflammatory skin diseases, which can serve as a putative drug target with known disease-derived molecules for developing efficacious combinatorial treatments for hidradenitis suppurativa, atopic dermatitis, and rosacea. ConclusionThe conventional transcriptome results identify huge complexity among inflammatory skin diseases, which demands a systems biology perspective for comprehensive understanding. The proposed framework is very modular, which can indicate a significant path of molecular mechanism-based drug development from complex transcriptomics data and other multi-omics data.

systems biology↗
bioRxiv2019

Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

MotivationBreast cancer is a heterogeneous disease. In order to guide proper treatment decisions for each individual patient, there is an urgent need for robust prognostic biomarkers that allow reliable prognosis prediction. Gene feature selection on microarray data is an approach to systematically discover potential biomarkers. However, common pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and thus tend to select genes that lack biological insights. In addition, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We therefore combined systems biology feature selection with ensemble learning in this study, aiming to address the above challenges and select genes with biological insights, as well as robust prognostic predictive power. Moreover, in order to capture the complex molecular processes of breast cancer, where multiple disease-contributing genes may exist and interact, we adopted a multi-gene approach to predict the prognosis status using machine learning classifiers.\n\nResultsWe systematically evaluated three different ensemble approaches that all improved the original systems biology feature selector. We found that compared to the most popular data-perturbation approach, function perturbation can produce significant improvement with just a few ensembles. Among all, the hybrid ensemble approach led to the most robust feature selection result, and the identified genes were shown to be highly involved in pathways, such as ubiquitination and cell cycle. Final prognosis prediction models were constructed using the identified genes and clinical information as input features. Among all models, bimodal deep neural network (DNN) achieved the highest AUC (area under receiver operating characteristic curve) in test performance evaluation, where subsequent survival analysis also verified its ability to differentiate patients with different prognosis statuses. In summary, the study demonstrated the potential of ensemble learning to improve gene feature selection robustness, as well as the potential of bimodal DNN in providing reliable prognosis prediction and guiding precision medicine.

bioinformatics↗
bioRxiv2018

Identification of Key Proteins Involved in Axon Guidance Related Disorders: A Systems Biology Approach

Axon guidance is a crucial process for growth of the central and peripheral nervous systems. In this study, 3 axon guidance related disorders, namely-Duane Retraction Syndrome (DRS), Horizontal Gaze Palsy with Progressive Scoliosis (HGPPS) and Congenital fibrosis of the extraocular muscles type 3 (CFEOM3) were studied using various Systems Biology tools to identify the genes and proteins involved with them to get a better idea about the underlying molecular mechanisms including the regulatory mechanisms. Based on the analyses carried out, 7 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with DRS, HGPPS and CFEOM3 associated genes. From the PPI network, 3 have been identified as hub proteins-DRD2, UBC and CUL3.

systems biology↗
bioRxiv2018

An integrative systems biology and experimental approach identifies convergence of epithelial plasticity, metabolism, and autophagy to promote chemoresistance

The evolution of therapeutic resistance is a major cause of death for patients with solid tumors. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure induced by the host immune system. These ecological and selective forces often lead to evolutionary convergence on one or more pathways or hallmarks that drive progression. These hallmarks are, in turn, intimately linked to each other through gene expression networks. Thus, a deeper understanding of the evolutionary convergences that occur at the gene expression level could reveal vulnerabilities that could be targeted to treat therapy-resistant cancer. To this end, we used a combination of phylogenetic clustering, systems biology analyses, and wet-bench molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during TGF-{beta}-mediated epithelial-mesenchymal transition in a lung cancer model system. Phylogenetics analyses of gene expression data from TGF-{beta} treated cells revealed evolutionary convergence of cells toward amine-metabolic pathways and autophagy during TGF-{beta} treatment. Using high-throughput drug screens, we found that knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-{beta}-induced resistance, implicating autophagy as a TGF-{beta}-mediated chemoresistance mechanism. Analysis of publicly-available clinical data sets validated the adverse prognostic importance of ATG16L expression in multiple cancer types including kidney, lung, and colon cancer patients. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities.

cancer biology↗
bioRxiv2021

Application of Modular Response Analysis to Medium- to Large-Size Biological Systems

The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactions a posteriori. We also exploited a block structure in MRA linear algebra to parallelize large system resolutions. Author SummaryThe knowledge of gene and protein regulatory networks in specific cell types, including pathologic cells, is an important endeavor in the post-genomic era. A particular type of data obtained through the systematic perturbation of the actors of such networks enables the reconstruction of the latter and is becoming available at a large scale (networks comprised of almost 1,000 genes). In this work, we benchmark the performance of a classical methodology for such data called modular response analysis, which has been so far applied to networks of modest sizes. We also propose improvements to increase performance and to accelerate computations on large problems.

systems biology↗
bioRxiv2024

Discovering Governing Equations of Biological Systems through Representation Learning and Sparse Model Discovery

Understanding the governing rules of complex biological systems remains a significant challenge due to the nonlinear, high-dimensional nature of biological data. In this study, we present CLERA, a novel end-to-end computational framework designed to uncover parsimonious dynamical models and identify active gene programs from single-cell RNA sequencing data. By integrating a supervised autoencoder architecture with Sparse Identification of Nonlinear Dynamics, CLERA leverages prior knowledge to simultaneously extract related low-dimensional embeddings and uncovers the underlying dynamical systems that drive the processes. Through the analysis of both synthetic and biological datasets, CLERA demonstrates robust performance in reconstructing gene expression dynamics, identifying key regulatory genes, and capturing temporal patterns across distinct cell types. CLERAs ability to generate dynamic interaction networks, combined with network rewiring using Personalized PageRank to highlight central genes and active gene programs, offers new insights into the complex regulatory mechanisms underlying cellular processes.

bioinformatics↗
bioRxiv2022

An integrated systems-biology platform for power-to-gas technology

Methanogenesis allows methanogenic archaea (methanogens) to generate cellular energy for their growth while producing methane. Hydrogenotrophic methanogens thrive on carbon dioxide and molecular hydrogen as sole carbon and energy sources. Thermophilic and hydrogenotrophic Methanothermobacter spp. have been recognized as robust biocatalysts for a circular carbon economy and are now applied in power-to-gas technology. Here, we generated the first manually curated genome-scale metabolic reconstruction for three Methanothermobacter spp.. We investigated differences in the growth performance of three wild-type strains and one genetically engineered strain in two independent chemostat bioreactor experiments. In the first experiment, with molecular hydrogen and carbon dioxide, we found the highest methane production rate for Methanothermobacter thermautotrophicus {Delta}H, while Methanothermobacter marburgensis Marburg reached the highest biomass growth rate. Systems biology investigations, including implementing a pan-model that contains combined reactions from all three microbes, allowed us to perform an interspecies comparison. This comparison enabled us to identify crucial differences in formate anabolism. In the second experiment, with sodium formate, we found stable growth with an M. thermautotrophicus {Delta}H plasmid-carrying strain with similar performance parameters compared to wild-type Methanothermobacter thermautotrophicus Z-245. Our findings reveal that formate anabolism influences the diversion of carbon to biomass and methane with implications for biotechnological applications of Methanothermobacter spp. in power-to-gas technology and for chemical production. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/522236v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1d468aeorg.highwire.dtl.DTLVardef@24a595org.highwire.dtl.DTLVardef@1f2dd60org.highwire.dtl.DTLVardef@1a3d8fc_HPS_FORMAT_FIGEXP M_FIG C_FIG Broader contextRenewable energy sources (e.g., wind and solar) provide carbon-free electric power. However, their intermittency and offset between peak production and demand generate the need to store this electric power. Furthermore, these technologies alone do not satisfy the demand for carbon-based commodities. Power-to-gas technology provides a means to store intermittent renewable electric power with concomitant carbon dioxide recycling into a chemical energy carrier, such as methane, on a centralized and decentralized scale. This is particularly important to establish equitable energy strategies for all countries, as is highlighted by the United Nations Sustainable Development Goals. With this work, we provide an integrated systems-biology platform for Methanothermobacter spp. to optimize biological power-to-gas technology and formulate strategies to produce other value-added products besides methane.

microbiology↗
bioRxiv2020

DIA-based systems biology approach unveils novel E3-dependent responses to a metabolic shift

Yeast Saccharomyces cerevisiae is a powerful model system for systems-wide biology screens and large-scale proteomics methods. Nearly complete proteomics coverage has been achieved owing to advances in mass spectrometry. However, it remains challenging to scale this technology for rapid and high-throughput analysis of the yeast proteome to investigate biological pathways on a global scale. Here we describe a systems biology workflow employing plate-based sample preparation and rapid, single-run data independent mass spectrometry analysis (DIA). Our approach is straightforward, easy to implement and enables quantitative profiling and comparisons of hundreds of nearly complete yeast proteomes in only a few days. We evaluate its capability by characterizing changes in the yeast proteome in response to environmental perturbations, identifying distinct responses to each of them, and providing a comprehensive resource of these responses. Apart from rapidly recapitulating previously observed responses, we characterized carbon source dependent regulation of the GID E3 ligase, an important regulator of cellular metabolism during the switch between gluconeogenic and glycolytic growth conditions. This unveiled new regulatory targets of the GID ligase during a metabolic switch. Our comprehensive yeast system read-out pinpointed effects of a single deletion or point mutation in the GID complex on the global proteome, allowing the identification and validation novel targets of the GID E3 ligase. Moreover, our approach allowed the identification of targets from multiple cellular pathways that display distinct patterns of regulation. Although developed in yeast, rapid whole proteome-based readouts can serve as comprehensive systems-level assay in all cellular systems.

systems biology↗
bioRxiv2019

Statistical analysis and optimality of biological systems

Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy "optimization priors." This family defines a smooth interpolation between a data-rich inference regime (characteristic of "bottom-up" statistical models), and a data-limited ab inito prediction regime (characteristic of "top-down" normative theory). We demonstrate the applicability of our framework using data from the visual cortex, the retina, and C. elegans, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems.

neuroscience↗
bioRxiv2019

A new Bayesian methodology for nonlinear model calibration in Computational Systems Biology

Computational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they cannot be directly measured. Therefore, a key issue in Systems Biology is model calibration, i.e. estimate parameters from experimental data. Existing methodologies for parameter estimation are divided in two classes: frequentist and Bayesian methods. The first ones optimize a cost function while the second ones estimate the parameter posterior distribution through different sampling techniques. Here, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for nonlinear model calibration and robustness analysis using omics data. CRC is an iterative algorithm based on the sampling of a proposal distribution and on the definition of multiple objective functions, one for each observable. CRC estimates the probability density function (pdf) of parameters conditioned to the experimental measures and it performs a robustness analysis, quantifying how much each parameter influences the observables behavior. We apply CRC to three Ordinary Differential Equations (ODE) models to test its performances compared to the other state of the art approaches, namely Profile Likelihood (PL), Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) and Delayed Rejection Adaptive Metropolis (DRAM). Compared with these methods, CRC finds a robust solution with a reduced computational cost. CRC is developed as a set of Matlab functions (version R2018), whose fundamental source code is freely available at https://github.com/fortunatobianconi/CRC.

systems biology↗
bioRxiv2024

RBC-GEM: a Knowledge Base for Systems Biology of Human Red Blood Cell Metabolism

Advancements with cost-effective, high-throughput omics technologies have had a transformative effect on both fundamental and translational research in the medical sciences. These advancements have facilitated a departure from the traditional view of human red blood cells (RBCs) as mere carriers of hemoglobin, devoid of significant biological complexity. Over the past decade, proteomic analyses have identified a growing number of different proteins present within RBCs, enabling systems biology analysis of their physiological functions. Here, we introduce RBC-GEM, the most extensive and meticulously curated metabolic reconstruction of a specific human cell type to-date. It was developed through meta-analysis of proteomic data from 28 studies published over the past two decades resulting in a RBC proteome composed of more than 4,600 distinct proteins. Through workflow-guided manual curation, we have compiled the metabolic reactions carried out by this proteome. RBC-GEM is hosted on a version-controlled GitHub repository, ensuring adherence to the standardized protocols for metabolic reconstruction quality control and data stewardship principles. This reconstruction of the RBC metabolic network is a knowledge base consisting of 718 genes encoding proteins acting on 1,590 unique metabolites through 2,554 biochemical reactions: a 700% size expansion over its predecessor. This reconstruction as an up-to-date curated knowledge base can be used for contextualization of data and for the construction of a computational whole-cell model of a human RBC. Author SummaryHuman red blood cells (RBCs) have been studied for decades because of their unique physiology, essential oxygen delivery functions, and general accessibility. RBCs are the simplest yet most numerous of human cell types due to the loss of cellular organelles during their development process. This process has evolved to maximize hemoglobin content per cell to facilitate RBCs main function in gas transport. RBCs are integral to a variety of medical applications, such as blood storage for transfusion. Recent advancements in high-throughput data collection have greatly expanded our understanding of RBC metabolism, highlighting important roles and functions for RBCs in maintaining homeostasis in the organism in addition to oxygen transport. Here we provide a knowledge base for the human RBC as a genome-scale metabolic reconstruction. Our results highlight the complexity of RBC metabolism, supported by recent advancements in high-throughput data collection methods for detecting low-abundance proteins in RBCs. We make knowledge about the RBC findable, accessible, interoperable, and reusable (FAIR). As RBC research is likely to see many translational medical advancements, a knowledge base for the contextualization of RBC data will serve as an essential resource for further research and medical application development.

systems biology↗
bioRxiv2019

The potential role of collagens in congenital Zika syndrome: A systems biology approach

Zika virus (ZIKV) infection during pregnancy could cause a set of severe abnormalities in the fetus known as congenital Zika syndrome (CZS). Experiments using animal models and in vitro systems significantly contributed to our understanding of the physiopathology of ZIKV infection. However, the molecular basis of CZS is not yet studied in humans. Here, we used a systems biology approach to integrate transcriptomic, proteomic and genomic data from post-mortem brains of neonates with CZS. We observed that collagen genes were greatly reduced in CZS brains at both the RNA and protein levels and that neonates with CZS have several polymorphisms in collagen genes associated with osteogenesis imperfect and arthrogryposis. These findings were validated using immunohistochemistry and collagen staining of ZIKV infected and non-infected samples. Additionally, it was found that cell adhesion genes that are essential for neurite outgrowth and axon guidance were up-regulated and thereby confirmed the neuronal migration defects observed. This work provided new insights into the underlying mechanisms of CZS and revealed host genes associated with CZS susceptibility.

systems biology↗
bioRxiv2022

PanomiR: A systems biology framework for analysis of multi-pathway targeting by miRNAs

Charting microRNA (miRNA) regulation across pathways is central to characterizing their role in disease. Yet, current methods reveal only individual miRNA-pathway interactions. We have developed a systems biology approach, Pathway networks of miRNA Regulation (PanomiR), that overcomes these limitations to identify miRNA targeting of groups of interacting pathways using gene expression. The approach does not depend on statistically significant enrichment of miRNA target genes in individual pathways or significant differentially expressed genes. Rather, it directly captures differential activity of pathways between states, determining their up-or-down regulation while sensitively detecting biologically-meaningful signals. PanomiR analyzes the co-activity of differentially regulated pathways to determine coordinate functional groups and uses these co-activated grouped pathways to uncover miRNAs that target them. Incorporating both experimentally-supported or predicted miRNA-mRNA interactions, PanomiR robustly identifies miRNAs central to the regulation of disease functions. We applied PanomiR to a liver cancer dataset and showed that it can organize liver cancer pathways and their regulating miRNAs into coordinated transcriptional programs, reflecting the pathogenic mechanisms of hepatocellular carcinoma. PanomiR recapitulated known central miRNAs in liver cancer with a biologically meaningful assignment of pathways under their regulation, unbiased by the number of genes targeted by each miRNA. PanomiR is a granular framework for detecting broad-scale multi-pathway programs under miRNA regulation. It is accessible as an open-source R/Bioconductor package: .

systems biology↗
bioRxiv2019

Experimental evolution reveals the genetic basis and systems biology of superoxide stress tolerance

Bacterial response to oxidative stress is of fundamental importance. Oxidative stresses are endogenous, such as reactive oxidative species (ROS) production during respiration, or exogenous in industrial biotechnology, due to culture conditions or product toxicity. The immune system inflicts strong ROS stress on invading pathogens. In this study we make use of Adaptive Laboratory Evolution (ALE) to generate two independent lineages of Escherichia coli with increased tolerance to superoxide stress by up to 500% compared to wild type. We found: 1) that the use of ALE reveals the genetic basis for and systems biology of ROS tolerance, 2) that there are only 6 and 7 mutations, respectively, in each lineage, five of which reproducibly occurred in the same genes (iron-sulfur cluster regulator iscR, putative iron-sulfur repair protein ygfZ, pyruvate dehydrogenase subunit E aceE, succinate dehydrogenase sucA, and glutamine tRNA glnX), and 3) that the transcriptome of the strain lineages exhibits two different routes of tolerance: the direct mitigation and repair of ROS damage and the up-regulation of cell motility and swarming genes mediated through phosphate starvation, which has been linked to biofilm formation and aggregation. These two transcriptomic responses can be interpreted as flight and fight phenotypes.\n\nImportanceBacteria encounter oxidative stress from multiple sources. During pathogenic infections, our bodys immune system releases ROS as a form of antimicrobial defense whilst bacteria used in industrial biotechnology are frequently exposed to genetic modifications and culture conditions which induce oxidative stress. In order to get around the bodys defences, pathogens have developed various adaptations to tolerate high levels of ROS, and these adaptive mechanisms are not always well understood. At the same time, there is a need to improve oxidative stress tolerance for industrially relevant strains in order to increase robustness and productivity. In this study we generate two strains of superoxide tolerant Escherichia coli and identify several adaptive mechanisms. These findings can be directly applied to improve production strain fitness in an industrial setting. They also provide insight into potential virulence factors in other pathogens, highlighting potential targets for antimicrobial compounds.

systems biology↗
bioRxiv2025

Integrating Interpretable Machine Learning and Multi-omics Systems Biology for Personalized Biomarker Discovery and Drug Repurposing in Alzheimer's Disease

Alzheimers disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health record (EHR) data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; nine tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations (SHAP) identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct "subtissues" (clusters of samples); and gene-gene co-expression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six FDA-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large U.S. de-identified insurance-claims database (n = 364733), exposure to promethazine, one of the candidate drugs, was associated with a 57-62 % lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both p < 0.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multi-omics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

systems biology↗
bioRxiv2026

BioPhasor: Decoding Cellular State Tensors from Multi-Omics Phasor Dynamics for Quantum Ready Systems Biology

Integrating multi-omics data--transcriptomics, proteomics, metabolomics, single-cell--remains a fundamental challenge in systems biology. We present BioPhasor, a framework that encodes each measurement as a complex phasor z = ei{phi} on the compact N -torus TN, modelling the cell as phase-coupled oscillatory programs whose dissipative dynamics generate limit cycles and an attractor landscape. From this geometry we derive the Cell State Tensor (CST), a rank-3 tensor whose axes we root in measured multi-omics quantities: a pathway/module atlas on the regulatory axis and a directional central-dogma modality axis. Across nine scenarios on open public data (GEO, CPTAC), loaded through one unmodified data layer, we report verdicts honestly: four reproduce, three are partial, two do not. A data-driven cell-cycle axis lifts agreement with a reference method from 0.34 to 0.69; an explicit circadian origin cuts peak-time error from 10.6 to 1.4 h; and central-dogma coupling--mRNA phase organising protein amplitude--clears a surrogate null and is tumour-specific. Grounding the quantum-ready claim, the CST maps to a density-matrix formalism whose coherence and entropy match quantum-information counterparts, and the phasor circuit transpiles gate-for-gate to a variational quantum circuit, though no empirical advantage emerges. A single loader regenerates every reported number, and the code is released.

bioinformatics↗
bioRxiv2020

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience

Neuroscience incorporates knowledge from a range of scales, from molecular dynamics to neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB(R) scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model. Information Sharing StatementBoth the source code and documentation of the Subcellular Workflow are available at https://github.com/jpgsantos/Subcellular_Workflow and licensed under GNU General Public License v3.0. The model is stored in the SBtab format (Lubitz et al. 2016). Model reduction, parameter estimation and global sensitivity analysis tools are written in MATLAB(R) (RRID:SCR_001622) and require the SimBiology(R) toolbox. Conversion script to VFGEN (Weckesser 2008), MOD and SBML (RRID:SCR_007422) is written in R (RRID:SCR_001905). Conversion to SBML requires the use of libSBML (RRID:SCR_014134). Validations are run in COPASI (RRID:SCR_014260; Hoops et al. 2006), NEURON (RRID:SCR_005393; Hines and Carnevale 1997) and with the subcellular simulation setup application (RRID:SCR_018790; available at https://subcellular.humanbrainproject.eu/model/simulations) that uses a spatial solver provided by STEPS (RRID:SCR_008742; Hepburn et al. 2012) and network-free solver NFsim (available at http://michaelsneddon.net/nfsim/). The medium spiny neuron model (Lindroos et al. 2018) used in NEURON simulations is available in ModelDB database (RRID:SCR_007271) with access code 237653. The FindSim use case model is available in https://github.com/BhallaLab/FindSim (Viswan et al. 2018).

systems biology↗