[Paper Review] Virus Detection in Multiplexed Nanowire Arrays using Hidden Semi-Markov models

Shalini Ghosh, Patrick Lincoln+2 more|arXiv (Cornell University)|Jul 16, 2014

Nanowire Synthesis and Applications3 references3 citations

TL;DR

This paper proposes a Hidden Semi-Markov Model (HSMM) to detect and identify multiple viruses binding to multiplexed nanowire arrays, leveraging conductance changes across antibody-coated nanowires. By modeling temporal patterns of conductance changes and accounting for noise and overlapping binding events, the HSMM improves detection accuracy in complex, real-time viral sensing scenarios.

ABSTRACT

In this paper, we address the problem of real-time detection of viruses docking to nanowires, especially when multiple viruses dock to the same nano-wire. The task becomes more complicated when there is an array of nanowires coated with different antibodies, where different viruses can dock to each coated nanowire at different binding strengths. We model the array response to a viral agent as a pattern of conductance change over nanowires with known modifier --- this representation permits analysis of the output of such an array via belief network (Bayes) methods, as well as novel generative models like the Hidden Semi-Markov Model (HSMM).

Motivation & Objective

• Address the challenge of detecting multiple viruses binding to the same nanowire in real time, especially when binding events overlap or have varying strengths.
• Overcome limitations of existing methods—such as the Patolsky method—in detecting rapidly mutating or engineered viruses due to lack of multiplexing and robust signal modeling.
• Enable accurate identification of viral agents in complex samples using arrays of nanowires coated with diverse antibodies and modifiers.
• Develop a generative probabilistic model that accounts for noisy, correlated, and non-stationary signals from nanowire arrays.
• Optimize modifier selection for multiplexed arrays to ensure redundancy, fault tolerance, and broad viral coverage.

Proposed method

• Model the conductance response of nanowire arrays as a pattern of changes over time, with each nanowire modified by a known antibody or modifier.
• Apply belief networks and Hidden Semi-Markov Models (HSMMs) to infer the most likely sequence of viral binding events from noisy conductance traces.
• Use histogram detrending and matched filtering to preprocess data and isolate binding signatures from transient noise and fluidic spikes.
• Estimate and remove common noise components by cross-correlating noise-only signals from similarly treated nanowires (e.g., same antibody or control wires).
• Leverage cross-correlation between nanowire signals to identify and suppress correlated noise sources, such as fluidic injections or electrical interference.
• Implement a noise-removal strategy by averaging noise estimates from similarly treated nanowires with time-lagged alignment to improve signal-to-noise ratio.

Experimental results

Research questions

• RQ1 How can multiple virus binding events on the same nanowire be distinguished when their conductance changes overlap in time?
• RQ2 What is the most effective way to model temporal conductance patterns in multiplexed nanowire arrays to improve virus detection accuracy?
• RQ3 How can correlated noise across nanowires be identified and removed to enhance detection sensitivity and reduce false positives?
• RQ4 What selection strategy for modifiers (antibodies) ensures robust detection of target viruses while tolerating nanowire failures and contamination?
• RQ5 Can generative models like HSMMs outperform traditional thresholding or filtering methods in detecting weak or transient binding events?

Key findings

• The HSMM model successfully captures the temporal dynamics of viral binding events, including overlapping boxcar conductance changes from multiple virus particles.
• Cross-correlation analysis revealed strong noise correlation between nanowires with identical modifiers (e.g., Cholera Toxin or control wires), indicating shared environmental noise sources.
• Noise removal via ensemble averaging of similarly treated nanowires reduced signal variance from 237 to 230, demonstrating a measurable noise suppression effect.
• The noise-removal technique improved signal clarity by isolating binding signatures from correlated background noise, especially from fluidic injections and electrical interference.
• Weak correlations between differently treated nanowires were observed at lags matching spike locations, suggesting residual noise coupling from external events.
• The approach enables detection of low-amplitude or transient binding signals that are obscured by noise in conventional thresholding or filtering methods.