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Matthew Burgess

Publications and source records attributed to Matthew Burgess.

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Bioelectrical interfaces beyond excitable cells: cancer, aging, and gene expression modulation

The investigation of biological conductivity has evolved from its classical foundation based on ionic fluxes underpinning cardiac and neuronal excitability to a multifaceted regulator of cellular physiology. Traditional approaches for probing electrical events in living matter focused largely on action potentials recording. However, bioelectricity in non-excitable cells governs key phenomena, including developmental patterning, tissue homeostasis, and disease progression. Pioneering studies implicated endogenous bioelectrics in many aspects of morphogenesis, wound healing, regeneration, and cancer. Early findings laid the groundwork for viewing bioelectricity as a means to influence cell fate, cell cycle progression, differentiation, and senescence. More recently, spatial variations in membrane potential within tumor microenvironments were found to correlate with metastatic potential. In parallel, substantial breakthroughs have been achieved in designing advanced bioelectrical interfaces for the study of neuronal networks and cardiac function. This perspective bridges the engineering and biological domains by examining how such technologies might enable new insights into non-excitable cell electrical events at different scales of operation to ultimately manipulate cellular pathways in cancer reprogramming, anti-aging interventions, and gene expression modulation.

q-bio.QM

Link-Prediction Enhanced Consensus Clustering for Complex Networks

Many real networks that are inferred or collected from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a portion of the data). The consequence is that downstream analyses that consume the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based algorithm. The framework then merges their multiple outputs into a final consensus output. On average our method boosts performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook.

cs.SI