121 papers
Systems biology moves beyond studying individual genes or proteins to understand how they work together as a complex, living network. Instead of looking at isolated parts, this field examines the intricate conversations between molecules that drive life, revealing how cellular systems respond to changes and maintain balance. It is a holistic approach that turns vast amounts of data into a coherent story of how organisms function as a whole.
At Gist.Science, we ensure these breakthroughs remain accessible to everyone by processing every new preprint in this category directly from bioRxiv. Our team generates both plain-language explanations for the curious mind and detailed technical summaries for researchers, bridging the gap between rapid scientific discovery and clear understanding.
Below are the latest preprints in systems biology, freshly curated and summarized to help you navigate the cutting edge of network science.
This study introduces the TransComp-R computational framework to bridge transcriptomic gaps between mouse models and human late-onset Alzheimer's disease, successfully identifying and validating the orexin receptor antagonist suvorexant as a repurposable drug that reduces phosphorylated tau levels in human patients.
By integrating metagenome-scale metabolic modeling with metabolomics, this study characterizes the functional dynamics of Microcystis phycospheres, revealing that while bacterial communities expand metabolic potential and maintain redundancy independent of cyanobacterial phylogeny, the overall metabolomic profile is primarily driven by the cyanobacteria themselves, thereby highlighting the ecological significance of interspecies metabolic interactions in harmful blooms.
FASTERCC is a new, significantly faster algorithm that accelerates flux consistency testing and context-specific metabolic network reconstruction by leveraging structural information to pre-process and clean large-scale models, thereby reducing computation times by up to 20-fold compared to its predecessor FASTCC.
This study integrates a mechanistic computational model with in vivo data to demonstrate that tumor adaptation to the bone microenvironment shifts dependence from bone-derived growth factors to autonomous proliferation, rendering osteoclast-targeted therapies effective only against non-adapted tumors while failing to control bone-adapted variants.
This paper introduces BIRDkiss, a new open-source R-based modeling framework that integrates simplified energy budget and toxicokinetic-toxicodynamic principles to predict how pesticides and food availability interact to affect bird growth and reproduction, thereby enhancing the ecological relevance of environmental risk assessments.
The paper introduces mCanonicalTockySeq, a systems-level framework that integrates fluorescent timer-based signalling history with single-cell RNA sequencing to construct an experimentally anchored temporal-developmental state space, enabling the resolution of T-cell maturation dynamics and the cross-species projection of human thymic transcriptomes onto a mouse reference.
The paper introduces MICA, a model-informed change-point detection algorithm that combines binary segmentation with a genetic algorithm to identify structural transitions in simulatable dynamical systems by minimizing discrepancies between model simulations and observed data.
GeNETop is a novel methodology that integrates flux variability analysis, network topology metrics, and transcriptomic data to generate context-specific genome-scale metabolic models that remain dynamically feasible and computationally efficient for studying time-dependent metabolic processes.
By integrating interpretable machine learning with systems biology, this study overcomes linkage disequilibrium limitations to decode complex genotype-phenotype maps in yeast, successfully identifying causal genes, validating pleiotropic effects, and uncovering novel biological functions like PDR8's role in cell wall integrity.
This paper presents a flexible framework that utilizes convolutional autoencoders to map experimental imaging data and agent-based model outputs into a shared latent space, enabling the quantitative optimization of model parameters to accurately reproduce complex spatial tumor features across diverse modalities.