weekly papers
A tailored weekly research-paper digest from the BMAC lab.
A weekly, relevance-ranked reading list for biomolecular networks, synthetic and microbial systems, and biology-enabling ideas.
AI-assisted summaries use available public abstracts and metadata and should be checked against the linked paper.
Latest issue
2026-07-20–2026-07-26 · 7 papers
Identifying Non-Ideal Reaction-Diffusion Systems Unable to Maintain Diffusion Out-of-Equilibrium
Francesco Avanzini, Timur Aslyamov, Massimiliano Esposito
2026-07-07 · arXiv (Cornell University)
TL;DR. Develops a method using a kinetic potential as a Lyapunov function to identify when diffusion equilibrates in non-ideal reaction-diffusion systems under chemostat driving.
Key signal. Diffusion equilibrates in systems with pseudo-detailed balanced or complex balanced reaction networks, with constraints on reaction fluxes deriving from network stoichiometry.
Why it matters here. This directly supports your program on biomolecular networks by providing a general condition for equilibrium in non-ideal reaction-diffusion systems, relevant to binding and catalysis networks.
Kinetic proofreading decouples signal strength and range in paracrine gradient formation
Purushottam D. Dixit, Aviral Jain
2026-07-22 · bioRxiv (Cold Spring Harbor Laboratory)
TL;DR. Presents a model showing that kinetic proofreading in multi-step receptor processing decouples signal strength and spatial range in paracrine gradients, explaining recent live-imaging data on EGFR.
Key signal. When receptors process ligands via a multi-step phosphorylation cascade with resetting, the states generating activity decouple from those consuming ligand, breaking the tradeoff between signal strength and range.
Why it matters here. This directly relates to your interest in sensing and proofreading by demonstrating how kinetic proofreading enables independent control of signaling range and strength in a biological receptor system.
Metabolic adaptations in antibiotic-resistant Staphylococcus aureus : understanding resistance mechanisms and enhancing antibiotic efficacy
Zhiyu Pan, Wei Zhang, Zhuo Ying Cao, Liting Cai, Yubin Su, Jiao Fei
2026-06-29 · Microbiology Spectrum
TL;DR. Uses LC-MS/MS metabolomics to identify metabolic adaptations in antibiotic-resistant Staphylococcus aureus and shows that supplementing with pyruvate, citrate, or fumarate enhances antibiotic efficacy.
Key signal. Suppressed central carbon and energy metabolism is a shared resistance mechanism in ciprofloxacin- and cefazolin-resistant S. aureus, and exogenous metabolites can potentiate antibiotic killing.
Why it matters here. This aligns with your focus on bacterial physiology and antibiotic resistance by revealing specific metabolic vulnerabilities that can be targeted to combat resistant S. aureus infections.
Learning proteomic disease trajectories with flow matching
Erik Hartman, Christofer Karlsson, Johan Malmström
2026-07-13 · bioRxiv (Cold Spring Harbor Laboratory)
TL;DR. Introduces 'proteome velocity', a flow-matching framework to infer continuous proteome trajectories from cross-sectional or sparsely sampled proteomics data.
Key signal. Covariate-conditioned flow matching resolves tissue- and pathogen-specific proteome trajectories in mouse sepsis and distinct velocity programs associated with COVID-19 severity.
Why it matters here. As a biology-enabling generative method, proteome velocity can be applied to model dynamic biological networks, such as cell-fate circuits or metabolic responses, from proteomic data.
The Pharmacology of Bacterial Persistence: From Antibiotic Tolerance to Antimicrobial Resistance
Maria Cristina Caroleo, Maria Pisano, Erika Cione, Domenica Scumaci, Tommaso Cai, Luca Gallelli, Antonio Leo
2026-07-16 · Antibiotics
TL;DR. Proposes a pharmacological framework linking antibiotic exposure, bacterial persistence, tolerance, relapse, and antimicrobial resistance, operationalizing persistence prevention exposure metrics.
Key signal. Persistence is a distinct phenotype from resistance, characterized by no MIC elevation, and shaped by antibiotic exposure, biofilm barriers, and host stress, requiring MDK99/MDK99.99 measures.
Why it matters here. This framework is directly relevant to your research on bacterial physiology and antibiotic resistance, offering a pharmacological perspective on how persistence contributes to treatment failure.
Asymptotic Thermodynamics for Chemical Reaction Networks with Fast-Slow Kinetics
L Peng, L Hong
2026-01-01 · Entropy
TL;DR. Presents a systematic derivation of asymptotic expansions of nonequilibrium thermodynamics for chemical reaction networks with fast-slow kinetics.
Key signal. Asymptotic expansions provide a tractable thermodynamic description for reaction networks with separated timescales.
Why it matters here. This contributes to your biomolecular control interests by offering a thermodynamic framework for analyzing complex reaction networks with fast and slow dynamics.
SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks
Authors unavailable
TL;DR. Introduces SMGen, a generator for creating synthetic models of biochemical reaction networks.
Key signal. SMGen enables systematic generation of synthetic biochemical network models for testing and benchmarking.
Why it matters here. This tool supports your work on biomolecular control by providing a method to generate synthetic models for testing network analysis and design approaches.
Archive
- BMAC Weekly — 2026-W30 — 2026-07-26 · 7 papers