SEARCH · bioRxiv Science

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 955 records · Page 53Linked to original sources

bioRxiv2024

Choices of regulatory logic class modulate the dynamical regime in random Boolean networks

Random Boolean networks (RBNs) have been widely explored as models for understanding complex systems, in particular gene regulatory networks (GRNs). Stability (order) and instability (chaos), critical characteristics of any dynamic system, have naturally been a central focus in RBN research. Several measures have previously been introduced to assess stability, e.g., via damage spreading or via statistical properties of attractors such as their number, lengths or basin sizes. Undoubtedly, network topology plays an important role in shaping the dynamics of RBN. Another key factor influencing the dynamics is the Boolean functions (BFs) assigned to each node. In this work, we conduct a systematic investigation to examine the influence of five different classes of BFs on the dynamics of RBNs. By employing various dynamical stability measures, we show that biologically meaningful BFs consistently drive the dynamics toward the stable regime compared to random BFs. This observation holds across networks with varying size, connectivity, and degree distributions. Additionally, we show that the change in the values of our stability measures with increasing connectivity or size of RBNs is different across the BF classes. For most observables, random BFs change the values significantly, pushing the dynamics toward a more chaotic regime with increasing connectivity or network size. In contrast, biologically meaningful BFs show very minimal fluctuations for most stability measures. These findings emphasize the advantage of restricting to biologically meaningful classes in the reconstruction and modeling of biological systems within Boolean framework.

systems biology↗

bioRxiv2026

GeNETop: Context-Specific Genome-Scale Constrained Models Using Network Topology, Flux Variability, and Transcriptomics

Context-specific genome-scale metabolic models are critical tools for studying cellular metabolism under dynamic conditions. However, most existing methods for deriving these models are designed for steady-state settings and may fail to preserve reactions required for transient metabolic shifts, thereby limiting their compatibility with dynamic FBA. Here, we present GeNETop, a methodology for deriving context-specific GEMs designed to preserve dynamic compatibility. GeNETop integrates flux variability analysis (FVA), network topology metrics based on the Integrated Value of Influence (IVI), and transcriptomic data to identify reactions that are both flux-flexible and structurally influential. Reactions are prioritized based on variability and maximality indices, while topology and gene expression guide further refinement, reducing dependence on fixed expression thresholds. Using batch fermentation of Saccharomyces cerevisiae as a case study, we evaluate GeNETop against established methods for context-specific metabolic reconstruction. The resulting networks remain dynamically feasible across growth phases, capture key metabolic transitions, reduce non-essential reactions, and maintain computational tractability. Overall, GeNETop enables context-specific metabolic reconstructions that are compatible with dynamic simulations while maintaining computational efficiency. By overcoming key limitations of existing approaches, the method supports a more accurate representation of time-dependent metabolic processes in biotechnology and systems biology. Author summaryCellular metabolism relies on complex networks of reactions to process nutrients, generate energy, and build essential compounds for biomass. Context-specific metabolic models aim to represent only the reactions active under a given condition, improving biological realism and reducing computational complexity in flux balance analysis simulations. However, metabolic activity adapts dynamically to changing environmental conditions, and reactions that are inactive at one stage may become essential at another. Many current reconstruction methods are designed for steady-state conditions and may exclude reactions that are required during metabolic transitions, thereby limiting their ability to describe dynamic behavior. Here, we introduce GeNETop, a novel approach that refines context-specific networks by integrating multiple layers of information. GeNETop identifies the most relevant reactions by considering their flexibility, importance within the network topology, and gene activity levels. In this way, the method generates biologically meaningful models that focus on metabolic pathways relevant under dynamic conditions. We tested GeNETop on yeast fermentation, a key process in food and biofuel production. The resulting models capture metabolic changes over time and enable stable dynamic simulations, supporting improved flux balance analysis of time-dependent metabolic processes.

systems biology↗

bioRxiv2015

Sensitivity analysis of Wnt β-catenin based transcription complex might bolster power-logarithmic psychophysical law and reveal preserved gene gene interactions

Recently, psychophysical laws have been observed to be functional in certain factors working downstream of the Wnt pathway. This work tests the veracity of the prevalence of such laws, albeit at a coarse level, using sensitivity analysis on biologically inspired epigenetically influenced computational causal models. In this work, the variation in the effect of the predictive behaviour of the transcription complex (TRCMPLX) conditional on the evidences of gene expressions in normal/tumor samples is observed by varying the initially assigned values of conditional probability tables (cpt) for TRCMPLX. Preliminary analysis shows that the variation in predictive behaviour of TRCMPLX follows power-logarithmic psychophysical law, crudely. More recently, wet lab experiments have proved the existence of sensors that behave in a logarithmic fashion thus supporting the earlier proposed postulates based on computational sensitivity analysis of this manuscript regarding the existence of logarithmic behaviour in the signaling pathways. It also signifies the importance of systems biology approach where in silico experiments combined with in vivo/in vitro experiments have the power to explore the deeper mechanisms of a signaling pathway. Additionally, it is hypothesized that these laws are prevalent at gene-gene interaction level also. The interactions were obtained by thresholding the inferred conditional probabilities of a gene activation given the status of another gene activation. The deviation in the interactions in normal/tumor samples was similarly observed by varying the initially assigned values of conditional probability tables (cpt) for TRCMPLX. Analysis of deviation in interactions show prevalence of psychophysical laws and is reported for interaction between elements of pairs (SFRP3, MYC), (SFRP2, CD44) and (DKK1, DACT2). Based on crude static models, it is assumed that dynamic models of Bayesian networks might reveal the phenomena in a better way.

Systems Biology↗

bioRxiv2021

Predicting the fitness costs of complex mutations

The fitness cost of complex pleiotropic mutations is generally difficult to assess. On the one hand, it is necessary to identify which molecular properties are directly altered by the mutation. On the other, this alteration modifies the activity of many genetic targets with uncertain consequences. Here, we examine the possibility of addressing these challenges by identifying unique predictors of these costs. To this aim, we consider mutations in the RNA polymerase (RNAP) in Escherichia coli as a model of complex mutations. Changes in RNAP modify the global program of transcriptional regulation, with many consequences. Among others is the difficulty to decouple the direct effect of the mutation from the response of the whole system to such mutation. A problem that we solve quantitatively with data of a set of constitutive genes, which better read the global program. We provide a statistical framework that incorporates the direct effects and other molecular variables linked to this program as predictors, which leads to the identification that some genes are more suitable predictors than others. Therefore, we not only identified which molecular properties best anticipate costs in fitness, but we also present the paradoxical result that, despite pleiotropy, specific genes serve as better predictors. These results have connotations for the understanding of the architecture of robustness in biological systems.

systems biology↗

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↗

bioRxiv2024

Systematic analysis of immune cell motility leveraging Immunemap, an open intravital microscopy atlas

Studying the spatiotemporal dynamics of cells in living organisms is a current frontier in bioimaging. Intravital Microscopy (IVM) provides direct, long-term observation of cell behavior in living animals, from tissue to sub-cellular resolution. Hence, IVM has become crucial for studying complex biological processes in motion and across scales, such as the immune response to pathogens and cancer. However, IVM data are typically kept in private repositories inaccessible to the scientific community, hampering large-scale analysis that aggregates data from multiple laboratories. To solve this issue, we introduce Immunemap, an atlas of immune cell motility based on an Open Data platform that provides access to over 58000 single-cell tracks and 1049000 cell-centroid annotations from 360 videos in murine models. Leveraging Immunemap and unsupervised learning, we systematically analyzed cell trajectories, identifying four main patterns of cell migration in immune cells. Two patterns correspond to behaviors previously characterized: directed movement and arresting. However, we identified two other patterns, characterized by low directionality and twisted paths, often considered random migration. We show that the newly defined patterns can be subdivided into two distinct types: within small areas, suggesting a focused patrolling around one or a few cells, and over larger areas, indicative of a more extended tissue patrolling. Furthermore, we show that the percentage of cells displaying these motility patterns changes in response to immune stimuli. Altogether, Immunemap embraces the FAIR principles, promoting data reuse to extract novel insights from immune cell dynamics through an image-based systems biology approach.

systems biology↗

bioRxiv2022

Lab evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress tolerance mechanisms: 1) modification of transport, 2) activation of ROS stress responses, 3) use of ROS-sensitive iron regulation, 4) motility, 5) broad transcriptional reallocation toward growth, and 6) metabolic rewiring to decrease NADH production. This work thus reveals the genome-scale systems biology of ROS tolerance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/521246v1_ufig1.gif" ALT="Figure 1"> View larger version (83K): org.highwire.dtl.DTLVardef@187f455org.highwire.dtl.DTLVardef@ba0798org.highwire.dtl.DTLVardef@148b175org.highwire.dtl.DTLVardef@17a8f8c_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Stress anisotropy in 3D active curved structures

Layers composed of lateral connections prevail in biological systems from subcellular membranes to epithelial sheets. This work presents a continuum framework to describe the effects of mechanical forces within a 3D curved layer with supporting lateral mesh. We provide detailed discussion on the emergence of stress anisotropy as a function of depth in different curvature settings, building on Lou et al. Phys. Rev. Lett. 130, 108401 (2023). We principally consider an epithelial monolayer to explain how the interplay between layer curvature and cell mechanics determines the stress anisotropy. We show that this can lead to irregular cellular shapes in 3D, including scutoid-like geometries. Our framework is general, and can be extended to a diverse set of biologically relevant systems.

biophysics↗

bioRxiv2018

Benchmark Problems for Dynamic Modeling of Intracellular Processes

MotivationDynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark problems is yet missing.\n\nResultsWe present a collection of 20 ODE models developed given experimental data as benchmark problems in order to evaluate new and existing methodologies, e.g. for parameter estimation or uncertainty analysis. In addition to the equations of the dynamical system, the benchmark collection provides experimental measurements as well as observation functions and assumptions about measurement noise distributions and parameters. The presented benchmark models comprise problems of different size, complexity and numerical demands. Important characteristics of the models and methodological requirements are summarized, estimated parameters are provided, and some example studies were performed for illustrating the capabilities of the presented benchmark collection.\n\nAvailabilityThe models are provided in several standardized formats, including an easy-to-use human readable form and machine-readable SBML files. The data is provided as Excel sheets. All files are available at https://github.com/Benchmarking-Initiative/Benchmark-Models, with MATLAB code to process and simulate the models.\n\nContactjan.hasenauer@helmholtz-muenchen.de, ckreutz@fdm.uni-freiburg.de

systems biology↗

bioRxiv2019

Finding MEMo: Minimum sets of elementary flux modes

Metabolic network reconstructions are widely used in computational systems biology for in silico studies of cellular metabolism. A common approach to analyse these models are elementary flux modes (EFMs), which correspond to minimal functional units in the network. Already for medium-sized networks, it is often impossible to compute the set of all EFMs, due to their huge number. From a practical point of view, this might also not be necessary because a subset of EFMs may already be sufficient to answer relevant biological questions. In this article, we study MEMos or minimum sets of EFMs that can generate all possible steady-state behaviours of a metabolic network. The number of EFMs in a MEMo may be by several orders of magnitude smaller than the total number of EFMs. Using MEMos, we can compute generating sets of EFMs in metabolic networks where the whole set of EFMs is too large to be enumerated.

systems biology↗

bioRxiv2021

The social architecture of an in-depth cellular protein interactome

Nearly all cellular functions are mediated by protein-protein interactions and mapping the interactome provides fundamental insights into the regulation and structure of biological systems. In principle, affinity purification coupled to mass spectrometry (AP-MS) is an ideal and scalable tool, however, it has been difficult to identify low copy number complexes, membrane complexes and those disturbed by protein-tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive, high-throughput, and highly reproducible AP-MS technology combined with a quantitative two-dimensional analysis strategy for comprehensive interactome mapping of Saccharomyces cerevisiae. We reduced required cell culture volumes thousand-fold and employed 96-well formats throughout, allowing replicate analysis of the endogenous green fluorescent protein (GFP) tagged library covering the entire expressed yeast proteome. The 4159 pull-downs generated a highly structured network of 3,909 proteins connected by 29,710 interactions. Compared to previous large-scale studies, we double the number of proteins (nodes in the network) and triple the number of reliable interactions (edges), including very low abundant epigenetic complexes, organellar membrane complexes and non-taggable complexes interfered by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 15 interactors, the majority of them unreported so far. Similar to social networks between humans, the average shortest distance is 4.2 interactions. A web portal (www.yeast-interactome.org) enables exploration of our dataset by the network and biological communities and variations of our AP-MS technology can be employed in any organism or dynamic conditions.

systems biology↗

bioRxiv2022

Deep Proteome Profiling Reveals Signatures of Age and Sex Differences in Paw Skin and Sciatic Nerve of Naïve Mice

The age and sex of studied animals profoundly impact experimental outcomes in animal-based preclinical biomedical research. However, most preclinical studies in mice use a wide-spanning age range from 4 to 14 weeks and do not assess study parameters in male and female mice in parallel. This raises concerns regarding reproducibility and neglects potentially relevant age and sex differences. Furthermore, the molecular setup of tissues in dependence of age and sex is unknown in naive mice. Here, we employed an optimized quantitative proteomics workflow in order to deeply profile mouse paw skin and sciatic nerve (SCN) - two tissues, which are crucially implicated in nociception and pain as well as diverse diseases induced by inflammation, trauma, and demyelination. Remarkably, we uncovered significant differences when comparing (i) male and female mice, and, in parallel, (ii) adolescent mice (4 weeks) with adult mice (14 weeks). Age was identified as a major discriminator of analyzed samples irrespective of tissue type. Moreover, our analysis enabled us to decipher protein subsets and networks that exhibit differential abundance in dependence on the age and/or sex of mice. Notably, among these were proteins and signaling pathways with known relevance for (patho)physiology, such as homeostasis and epidermal signaling in skin and, in SCN, multiple myelin proteins and regulators of neuronal development. In addition, extensive comparisons with available databases revealed that we quantified approx. 50% of gene products that were implicated in distinct skin diseases and pain, many of which exhibited significant abundance changes in dependence on age and/or sex. Taken together, our study emphasizes the need for accurate age matching and uncovers hitherto unknown sex and age differences at the level of proteins and protein networks. Overall, we provide a unique systems biology proteome resource, which facilitates mechanistic insights into somatosensory and skin biology in dependence on age and sex - a prerequisite for successful preclinical studies in mouse disease models. Graphic workflow O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/498721v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@fc5aaforg.highwire.dtl.DTLVardef@1a5b87dorg.highwire.dtl.DTLVardef@f353e9org.highwire.dtl.DTLVardef@109f6e5_HPS_FORMAT_FIGEXP M_FIG C_FIG The Figure was partly generated using Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.

systems biology↗

bioRxiv2024

Leveraging AI for improved reproducibility of mathematical disease models: insights from a retinitis pigmentosa case study

Mathematical modeling of disease and drug action is becoming an indispensable component of drug development, underscored by recent examples of models predicting trial results. To be able to rely on such approaches, decision-makers need to be able to verify those results independently with in silico confirmatory studies. Artificial Intelligence (AI) offers a valuable avenue for improving the reproducibility of complex models, enabling their swift and software-agnostic deployment. This paper highlights AIs impact through a case study on an Ordinary Differential Equation (ODE) model of Retinitis Pigmentosa (RP). We use a version of Chat GPT 4, a sophisticated large language model (LLM) developed by OpenAI, as customized by Mathpix company with additional capabilities. This setup facilitated the extraction of equations from the PDF and converted into a human-readable, text-based definition language called Antimony, which is part of the Python package tellurium. Subsequently, the model was converted into Systems Biology Markup Language (SBML) using tellurium and uploaded onto the jink[o] platform for simulation. The RP model was efficiently and accurately implemented using AI techniques. Furthermore, we were able to reproduce the model behavior presented in the literature. Our findings advocate for the broader application of AI in mathematical model re-implementations to ensure reliability and reproducibility of the results.

systems biology↗

bioRxiv2019

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

BackgroundMetabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous - i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity. ResultsOur workflow utilizes PROXIMAL - a tool that uses reactant-product transformation patterns from the KEGG database - to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in E. coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB). ConclusionsWe utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but have not been documented in iML1515 or other databases. Among these, we found that 17 metabolites have previously been documented in E. coli metabolomics studies. Further, 6 of these metabolites are not documented for any other E. coli metabolic model (e.g. KEGG, EcoCyc). The corresponding reactions should be added to iML1515 to create an Extended Metabolic Model (EMM). Other predicted metabolites and reactions can guide future experimental metabolomics studies. Further, our workflow can easily be applied to other organisms for which comprehensive genome-scale metabolic models are desirable.

systems biology↗

bioRxiv2022

Global analysis of the yeast knock-out phenome

Genome-wide phenotypic screens in the budding yeast Saccharomyces cerevisiae have produced the largest, richest and most systematic phenotypic description of any organism. Such an achievement was enabled by the development of highly scalable phenotypic assays and construction of the yeast knock-out (YKO) collection, comprising ~5,000 isogenic strains each deleted for exactly one open reading frame. Systematic screening of the YKO collection led to ~500 publications describing ~14,500 phenotypes capturing nearly every aspect of yeast biology. Yet, integrative analyses of this rich data source have been virtually impossible due to the lack of a central repository and consistent meta-data annotations. Here, we describe the aggregation, harmonization and analysis of all published phenotypic screens of the YKO collection, which we refer to as the Yeast Phenome (www.yeastphenome.org). To demonstrate the power of data integration and illustrate how much it facilitates the generation of testable hypotheses, we present three discoveries uniquely enabled by Yeast Phenome. First, we use the variation in the number of phenotypes per gene to identify tryptophan homeostasis as a central point of vulnerability to a wide range of chemical compounds, including FDA-approved drugs. Second, using phenotypic profiles as a tool for predicting gene function, we identify and validate the role of YHR045W as a novel regulator of ergosterol biosynthesis and DNA damage response, and YGL117W as a new member of the aromatic amino acid biosynthesis pathway. Finally, we describe a surprising exponential relationship between phenotypic similarity and intergenic distance in both yeast and human genomes. This relationship, which stretches as far as 380 kb in yeast and 100 Mb in humans, suggests that gene positions are optimized for function to a much greater extent than appreciated previously. Overall, we show that Yeast Phenome enables systematic enquiries into the nature of gene-gene and gene-phenotype relationships and is an important new resource for systems biology.

systems biology↗

bioRxiv2020

Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data

Cancer immunotherapy, specifically immune checkpoint blockade therapy, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Consequently, elucidating immune system-related pre-treatment biomarkers that are predictive with respect to sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders and non-responders. Our group has been studying immune signaling networks as an accurate reflection of the global immune state. Flow cytometry (FACS, Fluorescence-activated cell sorting) data characterizing immune signaling in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune signaling networks in this setting. Here, we describe a novel computational pipeline developed by us to perform secondary analyses of FACS data using systems biology / machine learning techniques and concepts. It is centered around comparative Bayesian network analyses of immune signaling networks and is capable of detecting true positive signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up immune markers (and combinations / interactions thereof) associated with clinical responses that were identified by this computational pipeline.

systems biology↗

bioRxiv2024

Regulated resource reallocation is transcriptionally hard wired into the yeast stress response

Many organisms maintain generalized stress responses activated by adverse conditions. Although details vary, a common theme is the redirection of transcriptional and translational capacity away from growth-promoting genes and toward defense genes. Yet the precise roles of these coupled programs are difficult to dissect. Here we investigated Saccharomyces cerevisiae responding to salt as a model stressor. We used molecular, genomic, and single-cell microfluidic methods to examine the interplay between transcription factors Msn2 and Msn4 that induce stress-defense genes and Dot6 and Tod6 that transiently repress growth-promoting genes during stress. Surprisingly, loss of Dot6/Tod6 led to slower acclimation to salt, whereas loss of Msn2/4 produced faster growth during stress. This supports a model where transient repression of growth-promoting genes accelerates the Msn2/4 response, which is essential for acquisition of subsequent peroxide tolerance. Remarkably, we find that Msn2/4 regulate DOT6 mRNA production, influence Dot6 activation dynamics, and are required for full repression of growth-promoting genes. Thus, Msn2/4 directly regulate resource reallocation needed to mount their own response. We discuss broader implications for common stress responses across organisms. SYNOPSISThis study investigates how genes induced and repressed in the yeast Environmental Stress Response contribute to stress tolerance, growth rate, and resource allocation. The work uses molecular, genomic, and systems biology approaches to present new insights into eukaryotic responses to acute stress. HIGHLIGHTSO_LICells lacking stress-activated transcription factors have a faster post-stress growth rate C_LIO_LICells lacking repressors of growth-promoting genes have a slower post-stress growth rate C_LIO_LIStress-defense factors control the induction of growth-promoting gene repressors, thereby coordinating the resource re-allocation needed for the response C_LI

systems biology↗

bioRxiv2023

Systems analysis of miR-199a/b-5p and multiple miR-199a/b-5p targets during chondrogenesis

Changes in chondrocyte gene expression can contribute to the development of osteoarthritis (OA), and so recognition of the regulative processes during chondrogenesis can lead to a better understanding of OA. microRNAs (miRNAs) are key regulators of gene expression in chondrocytes/OA and we have used a combined experimental, bioinformatic, and systems biology approach to explore the multiple miRNA-mRNA interactions that regulate chondrogenesis. A longitudinal chondrogenesis bioinformatic analysis identified paralogues miR-199a-5p and miR-199b-5p as pro-chondrogenic regulators. Experimental work demonstrated alteration of miR-199a-5p or miR-199b-5p expression led to significant inverse modulation of key chondrogenic genes and extracellular matrix production. miR-199a/b-5p targets FZD6, ITGA3 and CAV1 were identified by inhibition experiments and verified as direct targets by luciferase assay. The experimental work was used to generate and parameterize a multi-miRNA 14-day chondrogenesis kinetic model to be used as a repository for the experimental work and as a resource for further investigation of this system. This is the first multi-miRNA model of a chondrogenesis-based system, and highlights the complex relationships between regulatory miRNAs, and their target mRNAs.

systems biology↗