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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2023

Machine Learning-Driven Phenotype Predictions based on Genome Annotations

Over the past two decades, there has been a remarkable and exponential expansion in the availability of genome sequences, encompassing a vast number of isolate genomes, amounting to hundreds of thousands, and now extending to millions of metagenome-assembled genomes. The rapid and accurate interpretation of this data, along with the profiling of diverse phenotypes such as respiration type, antimicrobial resistance, or carbon utilization, is essential for a wide range of medical and research applications. Here, we leverage sequenced-based functional annotations obtained from the RAST annotation algorithm as predictors and employ six machine learning algorithms (K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Neural Networks, Logistic Regression, and Decision Trees) to generate classifiers that can accurately predict phenotypes of unclassified bacterial organisms. We apply this approach in two case studies focused on respiration types (aerobic, anaerobic, and facultative anaerobic) and Gram-stain types (Gram negative and Gram positive). We demonstrate that all six classifiers accurately classify the phenotypes of Gram stain and respiration type, and discuss the biological significance of the predicted outcomes. We also present four new applications that have been deployed in The Department of Energy Systems Biology Knowledgebase (KBase) that enable users to: (i) Upload high-quality data to train classifiers; (ii) Annotate genomes in the training set with the RAST annotation algorithm; (iii) Build six different genome classifiers; and (iv) Predict the phenotype of unclassified genomes. (https://narrative.kbase.us/#catalog/modules/kb_genomeclassification)

systems biology↗

bioRxiv2025

Building dynamical models of multi-step state transitions from single cell gene expression trajectories

Multi-step cell state transitions occur across biological processes, such as development and disease progression, yet the underlying gene regulation remains unclear. We introduce NetDes, a computational systems-biology method that infers core transcription factor (TF) regulatory networks and builds ODE-based dynamical models from single-cell gene expression trajectories. In benchmarks on synthetic trajectories with decoys and BEELINE scRNA-seq datasets, NetDes identifies regulatory interactions competitively with existing methods, and reconstructs a simulated cell-fate circuit. We applied it to time-series scRNA-seq data of iPSC-to-definitive-endoderm differentiation, epithelial-mesenchymal transition, erythropoiesis, and dendritic cell differentiation. NetDes has advantages over existing approaches in reconstructing a minimal network with a single model that reproduces observed expression dynamics and captures sequential state transitions. Network simulations predict TFs and their combinations driving each transition, recovering known master regulators, while network coarse-graining reveals the circuit logic of iPSC-to-DE differentiation. NetDes provides a general framework for mechanistic modeling of complex cell state transitions.

systems biology↗

bioRxiv2017

Inferring propensity amongst lung and breast carcinomas via overlapped gene expression profiles

Reconstruction of biological networks for topological analyses helps in correlation identification between various types of biomarkers. These networks have been vital components of System Biology in present era. Genes are the basic physical and structural unit of heredity. Genes act as instructions to make molecules called proteins. Alterations in the normal sequence of these genes are the root cause of various diseases and cancer is the prominent example disease caused by gene alteration or mutation. These slight alterations can be detected by microarray analysis. The high throughput data obtained by microarray experiments aid scientists in reconstructing cancer specific gene regulatory networks. The purpose of experiment performed is to find out the overlapping of the gene expression profiles of breast and lung cancer data, so that the common hub genes can be sifted and utilized as drug targets which could be used for the treatment of diseased conditions. In this study, first the differentially expressed genes have been identified (lung cancer and breast cancer), followed by a filtration approach and most significant genes are chosen using paired t-test and gene regulatory network construction. The obtained result has been checked and validated with the available databases and literature.

systems biology↗

bioRxiv2017

Probabilistic Graphical Models Relate Immune Status With Response To Neoadjuvant Chemo-Therapy In Breast Cancer

Breast cancer is the most frequent tumor in women and its incidence is increasing. Neoadjuvant chemotherapy has become standard of care as a complement to surgery in locally advanced or poor-prognosis early stage disease. The achievement of a complete response to neoadjuvant chemotherapy correlates with prognosis but it is not possible to predict who will obtain an excellent response. The molecular analysis of the tumor offers a unique opportunity to unveil predictive factors. In this work, gene expression profiling in 279 tumor samples from patients receiving neoadjuvant chemotherapy was performed and probabilistic graphical models were used. This approach enables addressing biological and clinical questions from a Systems Biology perspective, allowing to deal with large gene expression data and their interactions. Tumors presenting complete response to neoadjuvant chemotherapy had a higher activity of immune related functions compared to resistant tumors. Similarly, samples from complete responders presented higher expression of lymphocyte cell lineage markers, immune-activating and immune-suppressive markers, which may correlate with tumor infiltration by lymphocytes (TILs). These results suggest that the patients immune system plays a key role in tumor response to neoadjuvant treatment. However, future studies with larger cohorts are necessary to validate these hypotheses.

cancer biology↗

bioRxiv2016

Network Identification Methods

Recently, network inference algorithms have grown tremendously in the field of systems biology because network identification is essential for understanding relationships between regulation mechanisms for genes, elucidating functional mechanisms underlying cellular processes, as well as identifying molecular targets for discoveries in medicines. This article provides a brief overview of different approaches used to identify biological networks and reviews recent advances in network identification.

Systems Biology↗

bioRxiv2023

Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems

Individuals, organs, tissues, and cells age in diverse ways throughout the lifespan. Epigenetic clocks attempt to quantify differential aging between individuals, but they typically summarize aging as a single measure, ignoring within-person heterogeneity. Our aim was to develop novel systems-based methylation clocks that, when assessed in blood, capture aging in distinct physiological systems. We combined supervised and unsupervised machine learning methods to link DNA methylation, system-specific clinical chemistry and functional measures, and mortality risk. This yielded a panel of 11 system-specific scores- Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone, and Metabolic. Each system score predicted a wide variety of outcomes, aging phenotypes, and conditions specific to the respective system. We also combined the system scores into a composite Systems Age clock that is predictive of aging across physiological systems in an unbiased manner. Finally, we showed that the system scores clustered individuals into unique aging subtypes that had different patterns of age-related disease and decline. Overall, our biological systems based epigenetic framework captures aging in multiple physiological systems using a single blood draw and assay and may inform the development of more personalized clinical approaches for improving age-related quality of life.

systems biology↗

bioRxiv2018

Using maximum entropy production to describe microbial biogeochemistry over time and space in a meromictic pond

The maximum entropy production (MEP) conjecture posits that systems with many degrees of freedom will likely organize to maximize the rate of free energy dissipation. Previous work indicates that biological systems can outcompete abiotic systems by maximizing free energy dissipation over time by utilizing temporal strategies acquired and refined by evolution, and over space via cooperation. In this study, we develop an MEP model to describe biogeochemistry observed in Siders Pond, a phosphate limited meromictic system located in Falmouth, MA that exhibits steep chemical gradients due to density-driven stratification that supports anaerobic photosynthesis as well as microbial communities that catalyze redox cycles involving O, N, S, Fe and Mn. The MEP model uses a metabolic network to represent microbial redox reactions, where biomass allocation and reaction rates are determined by solving an optimization problem that maximizes entropy production over time and a 1D vertical profile constrained by an advection-dispersion-reaction model. We introduce a new approach for modeling phototrophy and explicitly represent aerobic photoautotrophs, anoxygenic photoheterotrophs and anaerobic photoautotrophs. The metabolic network also includes reactions for heterotrophic bacteria, sulfate reducing bacteria, sulfide oxidizing bacteria and aerobic and anaerobic grazers. Model results were compared to observations of biogeochemical constituents collected over a 24 hour period at 8 depths at a single 15 m deep station in Siders Pond. Maximizing entropy production over long (3 d) intervals produced results more similar to field observations than short (0.25 d) interval optimizations, which support the importance of temporal strategies for maximizing entropy production over time. Furthermore, we found that entropy production must be maximized locally instead of globally where energy potentials are degraded quickly by abiotic processes, such as light absorption by water. This combination of field observations with modeling results show that microbial systems in nature can be accurately described by the maximum entropy production conjecture applied over time and space.

systems biology↗

bioRxiv2019

Data-Driven Analysis of a Mechanistic Model of CAR T Cell Signaling Predicts Effects of Cell-to-cell Heterogeneity

Due to the variability of protein expression, cells of the same population can exhibit different responses to stimuli. It is important to understand this heterogeneity at the individual level, as population averages mask these underlying differences. Using computational modeling, we can interrogate a system much more precisely than by using experiments alone, in order to learn how the expression of each protein affects a biological system. Here, we examine a mechanistic model of CAR T cell signaling, which connects receptor-antigen binding to MAPK activation, to determine intracellular modulations that can increase cellular response. CAR T cell cancer therapy involves removing a patients T cells, modifying them to express engineered receptors that can bind to tumor-associated antigens to promote cell killing, and then injecting the cells back into the patient. This population of cells, like all cell populations, would have heterogeneous protein expression, which could affect the efficacy of treatment. Thus, it is important to examine the effects of cell-to-cell heterogeneity. We first generated a dataset of simulated cell responses via Monte Carlo simulations of the mechanistic model, where the initial protein concentrations were randomly sampled. We analyzed the dataset using partial least-squares modeling to determine the relationships between protein expression and ERK phosphorylation, the output of the mechanistic model. Using this data-driven analysis, we found that only the expressions of proteins relating directly to the receptor and the MAPK cascade, the beginning and end of the network, respectively, are relevant to the cells response. We also found, surprisingly, that increasing the amount of receptor present can actually inhibit the cells ability to respond due to increasing the strength of negative feedback from phosphatases. Overall, we have combined data-driven and mechanistic modeling to generate detailed insight into CAR T cell signaling.

systems biology↗

bioRxiv2026

Quantitative machine learning of protein interactions reveals the multiscale organization and molecular syntax of signaling networks

Cells employ dense networks of transient protein-protein interactions mediated by modular peptide-binding domains and unstructured peptidic motifs for high-fidelity information processing. How these networks physically execute computations through protein interactions governed by complex intra- and intermolecular mechanisms remains indiscernible from current, sparse and non-quantitative, maps of the human interactome. Here, we introduce a quantitative statistical mechanical modeling (QSM) approach for machine learning domain-peptide affinities with experimental-level accuracy. Leveraging a new, principled algorithm for data harmonization and a biophysically informed neural network architecture, QSM learns to predict dissociation constants directly from amino acid sequences with calibrated confidence. We use QSM to construct the first quantitative drafts of human signaling networks and study these networks across three physical scales--recognition mechanisms of modular binding domains, combinatorial logic of multi-dentate proteins, and pathways inferred from de novo inference of protein interaction networks. We find that (i) modular domains, based on their binding preferences, selectivities, and strengths, fall into a limited number of biophysical equivalence groups, (ii) those domains, along with peptidic motifs, are "syntactically" combined within proteins to yield multivalent recognition mechanisms, and (iii) the organization of cellular function can be traced back to algorithmically detectable modules induced by domain-mediated interactions. In aggregate, these analyses instantiate a tractable roadmap towards a comprehensive, mechanistic, and simulatable articulation of the systems biology of signaling.

systems biology↗

bioRxiv2024

Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study

Interactions between biological systems and engineered nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patients overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

systems biology↗

bioRxiv2017

The latent geometry of the human protein interaction network

To mine valuable information from the complex architecture of the human protein interaction network (hPIN), we require models able to describe its growth and dynamics accurately. Here, we present evidence that uncovering the latent geometry of the hPIN can ease challenging problems in systems biology. We embedded the hPIN to hyperbolic space, whose geometric properties reflect the characteristic scale invariance and strong clustering of the network. Interestingly, the inferred hyperbolic coordinates of nodes capture biologically relevant features, like protein age, function and cellular localisation. We also realised that the shorter the distance between two proteins in the embedding space, the higher their connection probability, which resulted in the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routeing process, guided by the latent geometry of the hPIN. When analysed from the appropriate biological context, these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency.

systems biology↗

bioRxiv2025

Quantum Algorithm for Metabolic Network Analysis

Biological systems, such as cellular metabolism, involve thousands of reactions that together determine how cells grow, respond to their environment, and produce energy. Modeling and analyzing these systems require solving very large mathematical problems that can quickly become computationally prohibitive. To address this challenge, we present a quantum algorithm to analyze metabolic networks, focusing on flux balance analysis as a representative case. We use a quantum interior point method consisting of a quantum subroutine for matrix inversion. Specifically, we reformulate the metabolic optimization problem for efficient execution on a quantum computer using quantum singular value transformation, enabling a rapid solution of complex systems that arise in flux balance analysis. This quantum approach offers a potential computational advantage over classical interior point methods for large and well-conditioned networks. We demonstrate the practical applicability of our method with numerical simulations on the glycolysis and tricarboxylic acid (TCA) cycle network and show that the quantum solution converges to the correct biological objective. This work represents the first application of quantum algorithms to metabolic pathway analysis, establishing a new direction for quantum computational biology and paving the way for quantum approaches to large-scale biological optimization.

systems biology↗

bioRxiv2020

Network Modeling and Inference of Peroxisome Proliferator-Activated Receptor Pathway in High fat diet-linked Obesity

Systems biology aims to understand how holistic systems theory can be used to explain the observable living system characteristics, and mathematical modeling tools have been successful in understanding the intricate relationships underlying cellular functions. Lately, researchers have been interested in understanding molecular mechanisms underlying obesity, which is a major health concern worldwide and has been linked to several diseases. Various mechanisms such as peroxisome proliferator-activated receptors (PPARs) are known to modulate obesity-induced inflammation and its consequences. In this study, we have modeled the PPAR pathway using a Bayesian model and inferred the sub-pathways that are potentially responsible for the activation of the output processes that are associated with high fat diet (HFD)-induced obesity. We examined a previously published dataset from a study that compared gene expression profiles of 40 mice maintained on HFD against 40 mice fed with chow diet (CD). Our simulations have highlighted that GPCR and FATCD36 sub-pathways were aberrantly active in HFD mice and are therefore favorable targets for anti-obesity strategies. We further cross-validated our observations with experimental results from the literature. We believe that mathematical models such as those presented in the present study can help in inferring other pathways and deducing significant biological relationships.

bioinformatics↗

bioRxiv2019

Microeconomics of metabolism: Overflow metabolism as Giffen behavior

Biological systems such as intracellular metabolism are rationally regulated to maximize the cellular growth rate through evolution (1-4), whereas microeconomics investigates the behaviour of consumers assumed to rationally maximize their utility (5, 6). Despite this analogy as optimization problems, the link between biology and economics has not been fully established (7, 8). Here, we developed an exact mapping between the regulation of metabolism and the theory of consumer choice, thereby revealing the correspondence between long-standing mysteries in both fields: overflow metabolism and Giffen behaviour. Overflow metabolism, particularly known as the Warburg effect in cancer (9, 10), is a seemingly wasteful but ubiquitous strategy where cells utilize aerobic glycolysis instead of the more energetically-efficient oxidative phosphorylation (9-16), whereas Giffen behaviour is the unexpected consumer behaviour where a good is demanded more as its price rises (17, 18). We revealed that the general conditions for these phenomena are trade-off and complementarity, i.e., impossibility of substitution for different goods. This correspondence implies that oxidative phosphorylation is counterintuitively stimulated when its efficiency is decreased by metabolic perturbations like drug administration. Therefore, Giffen behaviour bridges the Warburg effect and the reverse and inverse Warburg effect (19-22). This highlights that application of microeconomics to metabolism can offer new predictions and paradigms for both biology and economics.

systems biology↗

bioRxiv2015

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed Virtual Patients. In order to reproduce the statistics of a clinical population, Virtual Patients are often weighted to form a Virtual Population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate Virtual Patients and, from this ensemble, demonstrate how to select a Virtual Population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious Virtual Patients become over-represented in Virtual Populations.

Pharmacology and Toxicology↗

bioRxiv2015

Role of autoregulation and relative synthesis of operon partners in alternative sigma factor networks

Despite the central role of alternative sigma factors in bacterial stress response and virulence their regulation remains incompletely understood. Here we investigate one of the best-studied examples of alternative sigma factors: the {sigma}B network that controls the general stress response of Bacillus subtilis to uncover widely relevant general design principles that describe the structure-function relationship of alternative sigma factor regulatory networks. We show that the relative stoichiometry of the synthesis rates of {sigma}B, its anti-sigma factor RsbW and the anti-anti-sigma factor RsbV plays a critical role in shaping the network behavior by forcing the {sigma}B network to function as an ultrasensitive negative feedback loop. We further demonstrate how this negative feedback regulation insulates alternative sigma factor activity from competition with the housekeeping sigma factor for RNA polymerase and allows multiple stress sigma factors to function simultaneously with little competitive interference.\n\nMajor Subject Areas: Computational and systems biology, Microbiology & Infectious disease\n\nResearch Organism: Bacillus subtilis

Systems Biology↗

bioRxiv2016

GeNNet: An Integrated Platform for Unifying Scientific Workflow Management and Graph Databases for Transcriptome Data Analysis

BackgroundThere are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced may additionally be integrated with other biological databases, such as Protein-Protein Interactions and annotations. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managingthe resulting data and its respective metadata are challenging tasks. Running in-silico experiments to structure and compose the information as needed for analysis is a daunting task. Different programsmay need to be applied and different files are produced during the experiment cycle. In this context,the availability of a platform supporting experiment execution is paramount.\n\nResultsWe present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. GeNNet includes pre-loaded biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and geneset enrichment analysis. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships.The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene regulatory networks.\n\nConclusionsGeNNet is the first platform to integrate the analytical process of transcriptome data with graph database. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers as well can add new functionality to each component of GeNNet. The resulting data allows for testing previous hypotheses about an experiment as well as exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms.

bioinformatics↗

bioRxiv2020

Separating noise and function in systems of animal communication: a comparative study of aggressive signaling in crayfish

A primary issue in the study of dishonest signaling is the researchers ability to detect and describe a signal as being dishonest. However, by understanding the relative honesty of a signal as a statistical property of an individual or population, researchers have recently quantitively describe dishonest communication. Thus, dishonesty signals can be understood as when there is a breakdown in the correlation between a signal and its underlying meaning; creating variation within a signaling system. However, such variation in signaling systems may not be attributed to dishonesty, because of inherent noise within biological systems driven by evolutionary or physiological noise. Here, we try to separate out functional variation within honest or dishonesty signaling systems from inherent biological noise by leveraging homologous structures that have evolved for separate functions - the enlarged claws of freshwater crayfish. Because burrowing species of freshwater crayfish claws have not evolved as signals, the variability in the size and strength of their claws should be minimal when compared to claws of non-burrowing species that evolved as signals during aggression. We found that despite the claws of burrowing and nonburrowing crayfish claws having evolved to serve difference functions, the claws of all species in our study were inherently noisy. Furthermore, although claws that unreliably correlate to the strengthen the wielder may function as dishonest signals in other crustaceans, we did not find support for this hypothesis; because crayfish escalated aggression based on relative body size.

animal behavior and cognition↗