systems biology papers | Gist.Science

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.

📄 systems biology

A heterogeneous biomedical knowledge network framework for rare disease drug candidate prioritization: integrating Orphadata and DisGeNET via gene-bridge harmonization

This study presents a reproducible, network-based decision support framework that integrates Orphadata and DisGeNET via a rigorous gene-harmonization pipeline and employs a Graph Attention Network to learn biologically informed embeddings, achieving a 400-fold improvement over random baselines in prioritizing existing drug candidates for rare diseases.

Ramani, D.2026-09-08

📄 systems biology

Selectively Advantageous Instability and Information Theory in Sex-specific Aging

This paper proposes that "selectively advantageous instability" (SAI) is an evolutionarily favored mechanism where organisms actively destabilize specific biological information to access superior adaptive states, creating a "stabilization-destabilization complementarity" that explains the trade-offs underlying sex-specific aging, antagonistic pleiotropy, and the accumulation of irreversible damage despite the benefits of information maintenance.

Tower, J.2026-09-07

📄 systems biology

INFORME: coupling information-theoretic experimental design with nonlinear mixed-effects modeling for efficient observation scheduling

The paper introduces INFORME, a framework that integrates Bayesian information-theoretic experimental design with nonlinear mixed-effects modeling to adaptively schedule patient measurements, thereby significantly reducing the number of required scans and accelerating individualized treatment predictions compared to fixed protocols.

Cho, H., Tang, T., Lewis, A., Storey, K. M., Phan, T.2026-09-03

📄 systems biology

Predicting Cerebral Pericyte Contractility Across Experimental and Physiological Conditions: an in-silico framework

This study introduces and validates a multiscale in-silico framework that links pericyte electrophysiology and intracellular calcium dynamics to vascular wall mechanics, successfully predicting capillary contractility across diverse experimental and pharmacological conditions to support therapeutic strategies for cerebrovascular pathologies.

Coccarelli, A., Al-Areqi, A., Harraz, O. F.2026-09-03

📄 systems biology

The trade-off between parsimony and model complexity for understanding biomedical mechanisms from mathematical models

This paper demonstrates through ovarian cancer modeling that while statistical metrics like AIC and BIC help balance goodness-of-fit with parsimony, selecting the most biologically insightful model requires a deliberate trade-off between statistical simplicity and the inclusion of essential physiological mechanisms to avoid unidentifiability and overfitting.

Lamirande, P., Brunetti, M., Easlick, T., Beigmohammadi, F., Craig, M.2026-09-01

📄 systems biology

Regulatory stochasticity drives opposing phenotypic outcomes in cell-fate decision networks

This study demonstrates that temporal fluctuations in gene regulatory interaction strengths—specifically additive versus multiplicative noise—can drive opposing phenotypic outcomes in cell-fate decision networks by differentially reshaping the occupancy of co-expression and single-high states, thereby pushing developmental systems toward either progenitor-like or terminally differentiated fates.

Hari, K., Gupta, A., Shivakumar, L. M., Kulkarni, P., Salgia, R., Jolly, M. K., Levine, H.2026-08-26

📄 systems biology

Cross-Study Transcriptomic Meta-Analysis Reveals Conserved Adaptive Programs in Escherichia coli K-12

This study integrates transcriptomic data from multiple Escherichia coli K-12 adaptive laboratory evolution experiments to identify a conserved core of 109 genes that consistently remodel motility, chemotaxis, transport, and biofilm functions across diverse evolutionary trajectories.

Golmohammadi, M. J.2026-08-24

📄 systems biology

Evidence-constrained mechanistic synthesis for drug discovery

This paper introduces Evidence-Constrained Mechanistic Synthesis (ECMS), a framework that transforms heterogeneous biological evidence into constraints on a fixed ensemble of mechanistic hypotheses to optimize drug regimen selection while preserving distinct representations of evidence, uncertainty, and decision preferences.

Sengupta, D., Panda, S.2026-08-19

📄 systems biology

Quantitative Modeling of TLR Signaling Reveals Missing Negative Feedback Guiding Identification of TANK-IKKε Checkpoint

By developing a quantitative rule-based model of TLR4 signaling that revealed a consistent failure to predict pathway deactivation, researchers identified a previously unknown TANK-dependent IKKε checkpoint that negatively regulates the MyD88-IRAK1-TRAF6 module to restrain inflammation.

Manes, N. P., Zhang, F., Lin, B., Sun, J., Hassan, S. A., Armstrong, A. A., Shao, Y., Calzola, J. M., Kaplan-Stafford, P (…)2026-08-16

📄 systems biology

Growth costs of protein allocation in models of growing cells

This paper introduces a nonlinear growth balance analysis (GBA) framework to model the fitness costs of suboptimal protein allocation in growing cells, demonstrating that accounting for complex resource trade-offs and enzyme kinetics yields predictions consistent with experimental patterns and offers a practical tool for metabolic engineering.

Szeliova, D., Liebermeister, W., Lercher, M., Dourado, H.2026-08-13