1
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Ruzicka L, Strobl B, Bergmann S, Nolden G, Michalsky T, Domscheit C, Priesnitz J, Blümel F, Kohn B, Heitzinger C. Toward Synthetic Physical Fingerprint Targets. Sensors (Basel) 2024; 24:2847. [PMID: 38732954 PMCID: PMC11086259 DOI: 10.3390/s24092847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/11/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024]
Abstract
Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.
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Affiliation(s)
- Laurenz Ruzicka
- Austrian Institute of Technology, 1210 Vienna, Austria; (B.S.); (B.K.)
| | - Bernhard Strobl
- Austrian Institute of Technology, 1210 Vienna, Austria; (B.S.); (B.K.)
| | - Stephan Bergmann
- Bundesamt für Sicherheit in der Informationstechnik, 53175 Bonn, Germany; (S.B.); (G.N.)
| | - Gerd Nolden
- Bundesamt für Sicherheit in der Informationstechnik, 53175 Bonn, Germany; (S.B.); (G.N.)
| | | | | | | | - Florian Blümel
- Biometrie-Evaluations-Zentrum (BEZ) Hochschule Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany;
| | - Bernhard Kohn
- Austrian Institute of Technology, 1210 Vienna, Austria; (B.S.); (B.K.)
| | - Clemens Heitzinger
- Institute of Information Systems Engineering/Research Unit of Machine Learning, Technische Universität Wien, 1040 Vienna, Austria;
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2
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Böck M, Malle J, Pasterk D, Kukina H, Hasani R, Heitzinger C. Superhuman performance on sepsis MIMIC-III data by distributional reinforcement learning. PLoS One 2022; 17:e0275358. [PMID: 36327195 PMCID: PMC9632869 DOI: 10.1371/journal.pone.0275358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/15/2022] [Indexed: 11/06/2022] Open
Abstract
We present a novel setup for treating sepsis using distributional reinforcement learning (RL). Sepsis is a life-threatening medical emergency. Its treatment is considered to be a challenging high-stakes decision-making problem, which has to procedurally account for risk. Treating sepsis by machine learning algorithms is difficult due to a couple of reasons: There is limited and error-afflicted initial data in a highly complex biological system combined with the need to make robust, transparent and safe decisions. We demonstrate a suitable method that combines data imputation by a kNN model using a custom distance with state representation by discretization using clustering, and that enables superhuman decision-making using speedy Q-learning in the framework of distributional RL. Compared to clinicians, the recovery rate is increased by more than 3% on the test data set. Our results illustrate how risk-aware RL agents can play a decisive role in critical situations such as the treatment of sepsis patients, a situation acerbated due to the COVID-19 pandemic (Martineau 2020). In addition, we emphasize the tractability of the methodology and the learning behavior while addressing some criticisms of the previous work (Komorowski et al. 2018) on this topic.
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Affiliation(s)
- Markus Böck
- Technische Universität Wien (TU Wien), Vienna, Austria
| | - Julien Malle
- Technische Universität Wien (TU Wien), Vienna, Austria
| | - Daniel Pasterk
- Technische Universität Wien (TU Wien), Vienna, Austria
- * E-mail:
| | - Hrvoje Kukina
- Technische Universität Wien (TU Wien), Vienna, Austria
| | - Ramin Hasani
- Massachusetts Institute of Technology (MIT), Cambridge, MA, United States of America
| | - Clemens Heitzinger
- Technische Universität Wien (TU Wien), Vienna, Austria
- CAIML (Center for Artificial Intelligence and Machine Learning), TU Wien, Vienna, Austria
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3
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Jalaeian Zaferani E, Teshnehlab M, Khodadadian A, Heitzinger C, Vali M, Noii N, Wick T. Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception. Sensors (Basel) 2022; 22:s22166206. [PMID: 36015967 PMCID: PMC9413006 DOI: 10.3390/s22166206] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 08/15/2022] [Accepted: 08/16/2022] [Indexed: 05/27/2023]
Abstract
In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage. To address this challenge, researchers have applied optimization methods. Although there were successes, the search space is very large due to the large number of deep learning hyper-parameters, which increases the probability of getting stuck in local optima. Researchers have also focused on improving global optimization methods. In this regard, we suggest a novel global optimization method based on the cultural algorithm, multi-island and the concept of parallelism to search this large space smartly. At first, we evaluated our method on three well-known optimization benchmarks and compared the results with recently published papers. Results indicate that the convergence of the proposed method speeds up due to the ability to escape from local optima, and the precision of the results improves dramatically. Afterward, we applied our method to optimize five hyper-parameters of an asymmetric auto-encoder for automatic personality perception. Since inappropriate hyper-parameters lead the network to over-fitting and under-fitting, we used a novel cost function to prevent over-fitting and under-fitting. As observed, the unweighted average recall (accuracy) was improved by 6.52% (9.54%) compared to our previous work and had remarkable outcomes compared to other published personality perception works.
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Affiliation(s)
- Effat Jalaeian Zaferani
- Electrical & Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
| | - Mohammad Teshnehlab
- Electrical & Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
| | - Amirreza Khodadadian
- Institute of Applied Mathematics, Leibniz University of Hannover, 30167 Hannover, Germany
| | - Clemens Heitzinger
- Institute of Analysis and Scientific Computing, TU Wien, 1040 Vienna, Austria
- Center for Artificial Intelligence and Machine Learning (CAIML), TU Wien, 1040 Vienna, Austria
| | - Mansour Vali
- Electrical & Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
| | - Nima Noii
- Institute of Continuum Mechanics, Leibniz University of Hannover, 30823 Garbsen, Germany
| | - Thomas Wick
- Institute of Applied Mathematics, Leibniz University of Hannover, 30167 Hannover, Germany
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4
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Khodadadian A, Parvizi M, Teshnehlab M, Heitzinger C. Rational Design of Field-Effect Sensors Using Partial Differential Equations, Bayesian Inversion, and Artificial Neural Networks. Sensors 2022; 22:s22134785. [PMID: 35808281 PMCID: PMC9269136 DOI: 10.3390/s22134785] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/17/2022] [Accepted: 06/21/2022] [Indexed: 02/06/2023]
Abstract
Silicon nanowire field-effect transistors are promising devices used to detect minute amounts of different biological species. We introduce the theoretical and computational aspects of forward and backward modeling of biosensitive sensors. Firstly, we introduce a forward system of partial differential equations to model the electrical behavior, and secondly, a backward Bayesian Markov-chain Monte-Carlo method is used to identify the unknown parameters such as the concentration of target molecules. Furthermore, we introduce a machine learning algorithm according to multilayer feed-forward neural networks. The trained model makes it possible to predict the sensor behavior based on the given parameters.
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Affiliation(s)
- Amirreza Khodadadian
- Institute of Applied Mathematics, Leibniz University Hannover, Welfengarten 1, 30167 Hannover, Germany;
- Correspondence:
| | - Maryam Parvizi
- Institute of Applied Mathematics, Leibniz University Hannover, Welfengarten 1, 30167 Hannover, Germany;
- Cluster of Excellence PhoenixD (Photonics, Optics, and Engineering-Innovation Across Disciplines), Leibniz University Hannover, 30167 Hannover, Germany
| | - Mohammad Teshnehlab
- Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 19697, Iran;
| | - Clemens Heitzinger
- Institute of Analysis and Scientific Computing, TU Wien, Wiedner Hauptstrasse 8–10, 1040 Vienna, Austria;
- Center for Artificial Intelligence and Machine Learning (CAIML), TU Wien, 1040 Vienna, Austria
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5
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Tomeva E, Switzeny OJ, Heitzinger C, Hippe B, Haslberger AG. Comprehensive Approach to Distinguish Patients with Solid Tumors from Healthy Controls by Combining Androgen Receptor Mutation p.H875Y with Cell-Free DNA Methylation and Circulating miRNAs. Cancers (Basel) 2022; 14:cancers14020462. [PMID: 35053623 PMCID: PMC8774173 DOI: 10.3390/cancers14020462] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/13/2022] [Accepted: 01/16/2022] [Indexed: 02/01/2023] Open
Abstract
Liquid biopsy-based tests emerge progressively as an important tool for cancer diagnostics and management. Currently, researchers focus on a single biomarker type and one tumor entity. This study aimed to create a multi-analyte liquid biopsy test for the simultaneous detection of several solid cancers. For this purpose, we analyzed cell-free DNA (cfDNA) mutations and methylation, as well as circulating miRNAs (miRNAs) in plasma samples from 97 patients with cancer (20 bladder, 9 brain, 30 breast, 28 colorectal, 29 lung, 19 ovarian, 12 pancreas, 27 prostate, 23 stomach) and 15 healthy controls via real-time qPCR. Androgen receptor p.H875Y mutation (AR) was detected for the first time in bladder, lung, stomach, ovarian, brain, and pancreas cancer, all together in 51.3% of all cancer samples and in none of the healthy controls. A discriminant function model, comprising cfDNA mutations (COSM10758, COSM18561), cfDNA methylation markers (MLH1, MDR1, GATA5, SFN) and miRNAs (miR-17-5p, miR-20a-5p, miR-21-5p, miR-26a-5p, miR-27a-3p, miR-29c-3p, miR-92a-3p, miR-101-3p, miR-133a-3p, miR-148b-3p, miR-155-5p, miR-195-5p) could further classify healthy and tumor samples with 95.4% accuracy, 97.9% sensitivity, 80% specificity. This multi-analyte liquid biopsy-based test may help improve the simultaneous detection of several cancer types and underlines the importance of combining genetic and epigenetic biomarkers.
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Affiliation(s)
- Elena Tomeva
- HealthBioCare GmbH, A-1090 Vienna, Austria; (E.T.); (O.J.S.); (B.H.)
| | | | - Clemens Heitzinger
- Center for Artificial Intelligence and Machine Learning (CAIML), TU Wien, A-1040 Vienna, Austria;
| | - Berit Hippe
- HealthBioCare GmbH, A-1090 Vienna, Austria; (E.T.); (O.J.S.); (B.H.)
- Department of Nutritional Sciences, University of Vienna, A-1090 Vienna, Austria
| | - Alexander G. Haslberger
- Department of Nutritional Sciences, University of Vienna, A-1090 Vienna, Austria
- Correspondence:
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6
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Mitscha-Baude G, Stadlbauer B, Howorka S, Heitzinger C. Protein Transport through Nanopores Illuminated by Long-Time-Scale Simulations. ACS Nano 2021; 15:9900-9912. [PMID: 34096722 PMCID: PMC8291773 DOI: 10.1021/acsnano.1c01078] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 05/28/2021] [Indexed: 06/12/2023]
Abstract
The transport of molecules through nanoscale confined space is relevant in biology, biosensing, and industrial filtration. Microscopically modeling transport through nanopores is required for a fundamental understanding and guiding engineering, but the short duration and low replica number of existing simulation approaches limit statistically relevant insight. Here we explore protein transport in nanopores with a high-throughput computational method that realistically simulates hundreds of up to seconds-long protein trajectories by combining Brownian dynamics and continuum simulation and integrating both driving forces of electroosmosis and electrophoresis. Ionic current traces are computed to enable experimental comparison. By examining three biological and synthetic nanopores, our study answers questions about the kinetics and mechanism of protein transport and additionally reveals insight that is inaccessible from experiments yet relevant for pore design. The discovery of extremely frequent unhindered passage can guide the improvement of biosensor pores to enhance desired biomolecular recognition by pore-tethered receptors. Similarly, experimentally invisible nontarget adsorption to pore walls highlights how to improve recently developed DNA nanopores. Our work can be expanded to pressure-driven flow to model industrial nanofiltration processes.
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Affiliation(s)
| | - Benjamin Stadlbauer
- Institute
of Analysis and Scientific Computing, TU
Wien, Vienna, 1040, Austria
| | - Stefan Howorka
- Department
of Chemistry, Institute of Structural Molecular Biology, University College London, London, WC1E 6BT, United Kingdom
- Institute
of Biophysics, Johannes Kepler University
Linz, Linz, 4020, Austria
| | - Clemens Heitzinger
- Institute
of Analysis and Scientific Computing, TU
Wien, Vienna, 1040, Austria
- School
of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona 85287, United States
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7
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Taghizadeh L, Karimi A, Heitzinger C. Uncertainty quantification in epidemiological models for the COVID-19 pandemic. Comput Biol Med 2020; 125:104011. [PMID: 33091766 PMCID: PMC7518858 DOI: 10.1016/j.compbiomed.2020.104011] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 09/11/2020] [Accepted: 09/17/2020] [Indexed: 12/16/2022]
Abstract
Mathematical modeling of epidemiological diseases using differential equations are of great importance in order to recognize the characteristics of the diseases and their outbreak. The procedure of modeling consists of two essential components: the first component is to solve the mathematical model numerically, the so-called forward modeling. The second component is to identify the unknown parameter values in the model, which is known as inverse modeling and leads to identifying the epidemiological model more precisely. The main goal of this paper is to develop the forward and inverse modeling of the coronavirus (COVID-19) pandemic using novel computational methodologies in order to accurately estimate and predict the pandemic. This leads to governmental decisions support in implementing effective protective measures and prevention of new outbreaks. To this end, we use the logistic equation and the SIR (susceptible-infected-removed) system of ordinary differential equations to model the spread of the COVID-19 pandemic. For the inverse modeling, we propose Bayesian inversion techniques, which are robust and reliable approaches, in order to estimate the unknown parameters of the epidemiological models. We deploy an adaptive Markov-chain Monte-Carlo (MCMC) algorithm for the estimation of a posteriori probability distribution and confidence intervals for the unknown model parameters as well as for the reproduction number. We perform our analyses on the publicly available data for Austria to estimate the main epidemiological model parameters and to study the effectiveness of the protective measures by the Austrian government. The estimated parameters and the analysis of fatalities provide useful information for decision-makers and makes it possible to perform more realistic forecasts of future outbreaks. According to our Bayesian analysis for the logistic model, the growth rate and the carrying capacity are estimated respectively as 0.28 and 14974. Moreover for the parameters of the SIR model, namely the transmission rate and recovery rate, we estimate 0.36 and 0.06, respectively. Additionally, we obtained an average infectious period of 17 days and a transmission period of 3 days for COVID-19 in Austria. We also estimate the reproduction number over time for Austria. This quantity is estimated around 3 on March 26, when the first recovery was reported. Then it decays to 1 at the beginning of April. Furthermore, we present a fatality analysis for COVID-19 in Austria, which is also of importance for governmental protective decision-making. According to our analysis, the case fatality rate (CFR) is estimated as 4% and a prediction of the number of fatalities for the coming 10 days is also presented. Additionally, the ICU bed usage in Austria indicates that around 2% of the active infected individuals are critical cases and require ICU beds. Therefore, if Austrian governmental protective measures would not have taken place and for instance if the number of active infected cases would have been around five times larger, the ICU bed capacity could have been exceeded.
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Affiliation(s)
- Leila Taghizadeh
- Institute of Analysis and Scientific Computing, TU Wien, Wiedner Hauptstraße 8–10, 1040, Vienna, Austria,Corresponding author
| | - Ahmad Karimi
- Institute of Analysis and Scientific Computing, TU Wien, Wiedner Hauptstraße 8–10, 1040, Vienna, Austria
| | - Clemens Heitzinger
- Institute of Analysis and Scientific Computing, TU Wien, Wiedner Hauptstraße 8–10, 1040, Vienna, Austria,School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, 85287, USA
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8
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Khodadadian A, Noii N, Parvizi M, Abbaszadeh M, Wick T, Heitzinger C. A Bayesian estimation method for variational phase-field fracture problems. Comput Mech 2020; 66:827-849. [PMID: 33029034 PMCID: PMC7510934 DOI: 10.1007/s00466-020-01876-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 06/17/2020] [Indexed: 06/11/2023]
Abstract
In this work, we propose a parameter estimation framework for fracture propagation problems. The fracture problem is described by a phase-field method. Parameter estimation is realized with a Bayesian approach. Here, the focus is on uncertainties arising in the solid material parameters and the critical energy release rate. A reference value (obtained on a sufficiently refined mesh) as the replacement of measurement data will be chosen, and their posterior distribution is obtained. Due to time- and mesh dependencies of the problem, the computational costs can be high. Using Bayesian inversion, we solve the problem on a relatively coarse mesh and fit the parameters. In several numerical examples our proposed framework is substantiated and the obtained load-displacement curves, that are usually the target functions, are matched with the reference values.
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Affiliation(s)
- Amirreza Khodadadian
- Institute of Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8–10, 1040 Vienna, Austria
- Institute of Applied Mathematics, Leibniz University Hannover, Welfengarten 1, 30167 Hanover, Germany
| | - Nima Noii
- Institute of Applied Mathematics, Leibniz University Hannover, Welfengarten 1, 30167 Hanover, Germany
| | - Maryam Parvizi
- Institute of Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8–10, 1040 Vienna, Austria
| | - Mostafa Abbaszadeh
- Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914 Iran
| | - Thomas Wick
- Institute of Applied Mathematics, Leibniz University Hannover, Welfengarten 1, 30167 Hanover, Germany
| | - Clemens Heitzinger
- Institute of Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8–10, 1040 Vienna, Austria
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287 USA
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9
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Stadlbauer B, Mitscha-Baude G, Heitzinger C. Modeling single-molecule stochastic transport for DNA exo-sequencing in nanopore sensors. Nanotechnology 2020; 31:075502. [PMID: 31652425 DOI: 10.1088/1361-6528/ab513e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We present a simulation framework for computing the probability that a single molecule reaches the recognition element in a nanopore sensor. The model consists of the Langevin equation for the diffusive motion of small particles driven by external forces and the Poisson-Nernst-Planck-Stokes equations to compute these forces. The model is applied to examine DNA exo-sequencing in α-hemolysin, whose practicability depends on whether isolated DNA monomers reliably migrate into the channel in their correct order. We find that, at moderate voltage, migration fails in the majority of trials if the exonuclease which releases monomers is located farther than 1 nm above the pore entry. However, by tuning the pore to have a higher surface charge, applying a high voltage of 1 V and ensuring the exonuclease stays close to the channel, success rates of over 95% can be achieved.
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Affiliation(s)
- Benjamin Stadlbauer
- Institute for Analysis and Scientific Computing, TU Vienna, A-1040 Vienna, Austria
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10
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Taghizadeh L, Karimi A, Presterl E, Heitzinger C. Bayesian inversion for a biofilm model including quorum sensing. Comput Biol Med 2019; 117:103582. [PMID: 31885354 DOI: 10.1016/j.compbiomed.2019.103582] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 12/06/2019] [Accepted: 12/10/2019] [Indexed: 10/25/2022]
Abstract
We propose a mathematical model based on a system of partial differential equations (PDEs) for biofilms. This model describes the time evolution of growth and degradation of biofilms which depend on environmental factors. The proposed model also includes quorum sensing (QS) and describes the cooperation among bacteria when they need to resist against external factors such as antibiotics. The applications include biofilms on teeth and medical implants, in drinking water, cooling water towers, food processing, oil recovery, paper manufacturing, and on ship hulls. We state existence and uniqueness of solutions of the proposed model and implement the mathematical model to discuss numerical simulations of biofilm growth and cooperation. We also determine the unknown parameters of the presented biofilm model by solving the corresponding inverse problem. To this end, we propose Bayesian inversion techniques and the delayed-rejection adaptive-Metropolis (DRAM) algorithm for the simultaneous extraction of multiple parameters from the measurements. These quantities cannot be determined directly from the experiments or from the computational model. Furthermore, we evaluate the presented model by comparing the simulations using the estimated parameter values with the measurement data. The results illustrate a very good agreement between the simulations and the measurements.
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Affiliation(s)
- Leila Taghizadeh
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8-10, 1040 Vienna, Austria.
| | - Ahmad Karimi
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8-10, 1040 Vienna, Austria.
| | - Elisabeth Presterl
- Department for Hospital Hygiene and Infection Control, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
| | - Clemens Heitzinger
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8-10, 1040 Vienna, Austria; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA.
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11
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Lenzi EK, Evangelista LR, Taghizadeh L, Pasterk D, Zola RS, Sandev T, Heitzinger C, Petreska I. Reliability of Poisson–Nernst–Planck Anomalous Models for Impedance Spectroscopy. J Phys Chem B 2019; 123:7885-7892. [DOI: 10.1021/acs.jpcb.9b06263] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- E. K. Lenzi
- Departamento de Física, Universidade Estadual de Ponta Grossa, Avenida Av. General Carlos Cavalcanti 4748, 84030-900 Ponta Grossa, Paraná, Brazil
| | - L. R. Evangelista
- Departamento de Física, Universidade Estadual de Maringá, Avenida Colombo 5790, 87020-900 Maringá, Paraná, Brazil
| | - L. Taghizadeh
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8−10, 1040 Vienna, Austria
| | - D. Pasterk
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8−10, 1040 Vienna, Austria
| | - R. S. Zola
- Departamento de Física, Universidade Tecnológica Federal do Paraná—Apucarana, PR 86812-460, Brazil
| | - T. Sandev
- Research Center for Computer Science and Information Technologies, Macedonian Academy of Sciences and Arts, Bul. Krste Misirkov 2, 1000 Skopje, Macedonia
- Institute of Physics, Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University, Arhimedova 3, 1000 Skopje, Macedonia
| | - C. Heitzinger
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8−10, 1040 Vienna, Austria
| | - I. Petreska
- Institute of Physics, Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University, Arhimedova 3, 1000 Skopje, Macedonia
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12
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Khodadadian A, Parvizi M, Abbaszadeh M, Dehghan M, Heitzinger C. A multilevel Monte Carlo finite element method for the stochastic Cahn-Hilliard-Cook equation. Comput Mech 2019; 64:937-949. [PMID: 31929667 PMCID: PMC6936653 DOI: 10.1007/s00466-019-01688-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 02/10/2019] [Indexed: 06/10/2023]
Abstract
In this paper, we employ the multilevel Monte Carlo finite element method to solve the stochastic Cahn-Hilliard-Cook equation. The Ciarlet-Raviart mixed finite element method is applied to solve the fourth-order equation. In order to estimate the mild solution, we use finite elements for space discretization and the semi-implicit Euler-Maruyama method in time. For the stochastic scheme, we use the multilevel method to decrease the computational cost (compared to the Monte Carlo method). We implement the method to solve three specific numerical examples (both two- and three dimensional) and study the effect of different noise measures.
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Affiliation(s)
- Amirreza Khodadadian
- Institute of Applied Mathematics, Leibniz University of Hannover, Welfengarten 1, 30167 Hanover, Germany
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8–10, 1040 Vienna, Austria
| | - Maryam Parvizi
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8–10, 1040 Vienna, Austria
| | - Mostafa Abbaszadeh
- Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914 Iran
| | - Mehdi Dehghan
- Department of Applied Mathematics, Faculty of Mathematics and Computer Sciences, Amirkabir University of Technology, No. 424, Hafez Ave., Tehran, 15914 Iran
| | - Clemens Heitzinger
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8–10, 1040 Vienna, Austria
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287 USA
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Khodadadian A, Hosseini K, Manzour-Ol-Ajdad A, Hedayati M, Kalantarinejad R, Heitzinger C. Optimal design of nanowire field-effect troponin sensors. Comput Biol Med 2017; 87:46-56. [PMID: 28550739 DOI: 10.1016/j.compbiomed.2017.05.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Revised: 05/09/2017] [Accepted: 05/09/2017] [Indexed: 10/19/2022]
Abstract
We propose a design strategy for affinity-based biosensors using nanowires for sensing and measuring biomarker concentration in biological samples. Such sensors have been shown to have superior properties compared to conventional biosensors in terms of LOD (limit of detection), response time, cost, and size. However, there are several parameters affecting the performance of such devices that must be determined. In order to solve the design problem, we have developed a comprehensive model based on stochastic transport equations that makes it possible to optimize the sensing behavior.
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Affiliation(s)
- Amirreza Khodadadian
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8-10, 1040 Vienna, Austria.
| | - Kiarash Hosseini
- Shezan Research and Innovation Centre, No. 25, Innovation 2 St., Pardis TechPark, Tehran, Iran
| | - Ali Manzour-Ol-Ajdad
- Shezan Research and Innovation Centre, No. 25, Innovation 2 St., Pardis TechPark, Tehran, Iran
| | - Marjan Hedayati
- Shezan Research and Innovation Centre, No. 25, Innovation 2 St., Pardis TechPark, Tehran, Iran
| | - Reza Kalantarinejad
- Shezan Research and Innovation Centre, No. 25, Innovation 2 St., Pardis TechPark, Tehran, Iran
| | - Clemens Heitzinger
- Institute for Analysis and Scientific Computing, Vienna University of Technology (TU Wien), Wiedner Hauptstraße 8-10, 1040 Vienna, Austria; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ 85287, USA
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14
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Tulzer G, Heitzinger C. Brownian-motion based simulation of stochastic reaction-diffusion systems for affinity based sensors. Nanotechnology 2016; 27:165501. [PMID: 26939610 DOI: 10.1088/0957-4484/27/16/165501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this work, we develop a 2D algorithm for stochastic reaction-diffusion systems describing the binding and unbinding of target molecules at the surfaces of affinity-based sensors. In particular, we simulate the detection of DNA oligomers using silicon-nanowire field-effect biosensors. Since these devices are uniform along the nanowire, two dimensions are sufficient to capture the kinetic effects features. The model combines a stochastic ordinary differential equation for the binding and unbinding of target molecules as well as a diffusion equation for their transport in the liquid. A Brownian-motion based algorithm simulates the diffusion process, which is linked to a stochastic-simulation algorithm for association at and dissociation from the surface. The simulation data show that the shape of the cross section of the sensor yields areas with significantly different target-molecule coverage. Different initial conditions are investigated as well in order to aid rational sensor design. A comparison of the association/hybridization behavior for different receptor densities allows optimization of the functionalization setup depending on the target-molecule density.
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15
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Bernardi MH, Schmidlin D, Ristl R, Heitzinger C, Schiferer A, Neugebauer T, Wrba T, Hiesmayr M, Druml W, Lassnigg A. Serum Creatinine Back-Estimation in Cardiac Surgery Patients: Misclassification of AKI Using Existing Formulae and a Data-Driven Model. Clin J Am Soc Nephrol 2016; 11:395-404. [PMID: 26801479 DOI: 10.2215/cjn.03560315] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2015] [Accepted: 12/01/2015] [Indexed: 12/21/2022]
Abstract
BACKGROUND AND OBJECTIVES A knowledge of baseline serum creatinine (bSCr) is mandatory for diagnosing and staging AKI. With often missing values, bSCr is estimated by back-calculation using several equations designed for the estimation of GFR, assuming a "true" GFR of 75 ml/min per 1.73 m(2). Using a data set from a large cardiac surgery cohort, we tested the appropriateness of such an approach and compared estimated and measured bSCr. Moreover, we designed a novel data-driven model (estimated serum creatinine [eSCr]) for estimating bSCr. Finally, we analyzed the extent of AKI and mortality rate misclassifications. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Data for 8024 patients (2833 women) in our cardiac surgery center were included from 1997 to 2008. Measured and estimated bSCr were plotted against age for men and women. Patients were classified to AKI stages defined by the Kidney Disease Improving Global Outcomes (KDIGO) group. Results were compared with data from another cardiac surgery center in Zurich, Switzerland. RESULTS The Modification of Diet in Renal Disease and the Chronic Kidney Disease Epidemiology Collaboration formulae describe higher estimated bSCr values in younger patients, but lower values in older patients compared with the measured bSCr values in both centers. The Pittsburgh Linear Three Variables formula correctly describes the increasing bSCr with age, however, it underestimates the overall bSCr level, being in the range of the 25% quantile of the measured values. Our eSCr model estimated measured bSCr best. AKI stage 1 classification using all formulae, including our eSCr model, was incorrect in 53%-80% of patients in Vienna and in 74%-91% in Zurich; AKI severity (according to KDIGO stages) and also mortality were overestimated. Mortality rate was higher among patients falsely classified into higher KDIGO stages by estimated bSCr. CONCLUSIONS bSCr values back-estimated using currently available eGFR formulae are inaccurate and cannot correctly classify AKI stages. Our model eSCr improves the prediction of AKI but to a still inadequate extent.
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Affiliation(s)
| | - Daniel Schmidlin
- Department of Anesthesiology and Intensive Care Medicine, Klinik Im Park, Zurich, Switzerland; and
| | - Robin Ristl
- Center for Medical Statistics, Informatics and Intelligent Systems, and
| | - Clemens Heitzinger
- Institute for Analysis and Scientific Computing, Technical University of Vienna, Vienna, Austria
| | - Arno Schiferer
- *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine
| | - Thomas Neugebauer
- *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine
| | - Thomas Wrba
- Center for Medical Statistics, Informatics and Intelligent Systems, and
| | - Michael Hiesmayr
- *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine
| | - Wilfred Druml
- *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine
| | - Andrea Lassnigg
- *Department of Cardiothoracic and Vascular Anesthesia and Intensive Care Medicine
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Tulzer G, Heitzinger C. Fluctuations due to association and dissociation processes at nanowire-biosensor surfaces and their optimal design. Nanotechnology 2015; 26:025502. [PMID: 25517111 DOI: 10.1088/0957-4484/26/2/025502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In this work, we calculate the effect of the binding and unbinding of molecules at the surface of a nanowire biosensor on the signal-to-noise ratio of the sensor. We model the fluctuations induced by association and dissociation of target molecules by a stochastic differential equation and extend this approach to a coupled diffusion-reaction system. Where possible, analytic solutions for the signal-to-noise ratio are given. Stochastic simulations are performed wherever closed forms of the solutions cannot be derived. Starting from parameters obtained from experimental data, we simulate DNA hybridization at the sensor surface for different target molecule concentrations in order to optimize the sensor design.
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Affiliation(s)
- Gerhard Tulzer
- Vienna University of Technology, Wiedner Hauptstrasse 8-10, A-1040 Vienna, Austria
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Tulzer G, Baumgartner S, Brunet E, Mutinati GC, Steinhauer S, Köck A, Barbano PE, Heitzinger C. Kinetic parameter estimation and fluctuation analysis of CO at SnO2 single nanowires. Nanotechnology 2013; 24:315501. [PMID: 23851634 DOI: 10.1088/0957-4484/24/31/315501] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
In this work, we present calculated numerical values for the kinetic parameters governing adsorption/desorption processes of carbon monoxide at tin dioxide single-nanowire gas sensors. The response of such sensors to pulses of 50 ppm carbon monoxide in nitrogen is investigated at different temperatures to extract the desired information. A rate-equation approach is used to model the reaction kinetics, which results in the problem of determining coefficients in a coupled system of nonlinear ordinary differential equations. The numerical values are computed by inverse-modeling techniques and are then used to simulate the sensor response. With our model, the dynamic response of the sensor due to the gas-surface interaction can be studied in order to find the optimal setup for detection, which is an important step towards selectivity of these devices. We additionally investigate the noise in the current through the nanowire and its changes due to the presence of carbon monoxide in the sensor environment. Here, we propose the use of a wavelet transform to decompose the signal and analyze the noise in the experimental data. This method indicates that some fluctuations are specific for the gas species investigated here.
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Affiliation(s)
- Gerhard Tulzer
- AIT Austrian Institute of Technology, Donau-City-Strasse 1, Vienna, Austria.
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Baumgartner S, Heitzinger C, Vacic A, Reed MA. Predictive simulations and optimization of nanowire field-effect PSA sensors including screening. Nanotechnology 2013; 24:225503. [PMID: 23644739 DOI: 10.1088/0957-4484/24/22/225503] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We apply our self-consistent PDE model for the electrical response of field-effect sensors to the 3D simulation of nanowire PSA (prostate-specific antigen) sensors. The charge concentration in the biofunctionalized boundary layer at the semiconductor-electrolyte interface is calculated using the propka algorithm, and the screening of the biomolecules by the free ions in the liquid is modeled by a sensitivity factor. This comprehensive approach yields excellent agreement with experimental current-voltage characteristics without any fitting parameters. Having verified the numerical model in this manner, we study the sensitivity of nanowire PSA sensors by changing device parameters, making it possible to optimize the devices and revealing the attributes of the optimal field-effect sensor.
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Affiliation(s)
- Stefan Baumgartner
- AIT Austrian Institute of Technology, Donau-City-Strasse 1, A-1220 Vienna, Austria.
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Punzet M, Baurecht D, Varga F, Karlic H, Heitzinger C. Determination of surface concentrations of individual molecule-layers used in nanoscale biosensors by in situ ATR-FTIR spectroscopy. Nanoscale 2012; 4:2431-8. [PMID: 22399200 DOI: 10.1039/c2nr12038k] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
For the development of nanowire sensors for chemical and medical detection purposes, the optimal functionalization of the surface is a mandatory component. Quantitative ATR-FTIR spectroscopy was used in situ to investigate the step-by-step layer formation of typical functionalization protocols and to determine the respective molecule surface concentrations. BSA, anti-TNF-α and anti-PSA antibodies were bound via 3-(trimethoxy)butylsilyl aldehyde linkers to silicon-oxide surfaces in order to investigate surface functionalization of nanowires. Maximum determined surface concentrations were 7.17 × 10(-13) mol cm(-2) for BSA, 1.7 × 10(-13) mol cm(-2) for anti-TNF-α antibody, 6.1 × 10(-13) mol cm(-2) for anti-PSA antibody, 3.88 × 10(-13) mol cm(-2) for TNF-α and 7.0 × 10(-13) mol cm(-2) for PSA. Furthermore we performed antibody-antigen binding experiments and determined the specific binding ratios. The maximum possible ratio of 2 was obtained at bulk concentrations of the antigen in the μg ml(-1) range for TNF-α and PSA.
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Affiliation(s)
- Manuel Punzet
- Institute of Biophysical Chemistry, University of Vienna, Althanstrasse 14, A-1090 Vienna, Austria
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Abstract
In order to facilitate the rational design and the characterization of nanowire field-effect sensors, we have developed a model based on self-consistent charge-transport equations combined with interface conditions for the description of the biofunctionalized surface layer at the semiconductor/electrolyte interface. Crucial processes at the interface, such as the screening of the partial charges of the DNA strands and the influence of the angle of the DNA strands with respect to the nanowire, are computed by a Metropolis Monte Carlo algorithm for charged molecules at interfaces. In order to investigate the sensing mechanism of the device, we have computed the current–voltage characteristics, the electrostatic potential and the concentrations of electrons and holes. Very good agreement with measurements has been found and optimal device parameters have been identified. Our approach provides the capability to study the device sensitivity, which is of fundamental importance for reliable sensing.
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Affiliation(s)
- S Baumgartner
- Wolfgang Pauli Institute c/o Department of Mathematics, University of Vienna, A-1090 Vienna, Austria.
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Bulyha A, Heitzinger C. An algorithm for three-dimensional Monte-Carlo simulation of charge distribution at biofunctionalized surfaces. Nanoscale 2011; 3:1608-1617. [PMID: 21301731 DOI: 10.1039/c0nr00791a] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
In this work, a Monte-Carlo algorithm in the constant-voltage ensemble for the calculation of 3d charge concentrations at charged surfaces functionalized with biomolecules is presented. The motivation for this work is the theoretical understanding of biofunctionalized surfaces in nanowire field-effect biosensors (BioFETs). This work provides the simulation capability for the boundary layer that is crucial in the detection mechanism of these sensors; slight changes in the charge concentration in the boundary layer upon binding of analyte molecules modulate the conductance of nanowire transducers. The simulation of biofunctionalized surfaces poses special requirements on the Monte-Carlo simulations and these are addressed by the algorithm. The constant-voltage ensemble enables us to include the right boundary conditions; the dna strands can be rotated with respect to the surface; and several molecules can be placed in a single simulation box to achieve good statistics in the case of low ionic concentrations relevant in experiments. Simulation results are presented for the leading example of surfaces functionalized with pna and with single- and double-stranded dna in a sodium-chloride electrolyte. These quantitative results make it possible to quantify the screening of the biomolecule charge due to the counter-ions around the biomolecules and the electrical double layer. The resulting concentration profiles show a three-layer structure and non-trivial interactions between the electric double layer and the counter-ions. The numerical results are also important as a reference for the development of simpler screening models.
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Affiliation(s)
- Alena Bulyha
- Department of Mathematics and Wolfgang Pauli Institute, University of Vienna, A-1090, Vienna, Austria.
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Heitzinger C, Liu Y, Mauser NJ, Ringhofer C, Dutton RW. Calculation of Fluctuations in Boundary Layers of Nanowire Field-Effect Biosensors. ACTA ACUST UNITED AC 2010. [DOI: 10.1166/jctn.2010.1644] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Heitzinger C, Kennell R, Klimeck G, Mauser N, McLennan M, Ringhofer C. Modeling and simulation of field-effect biosensors (BioFETs) and their deployment on the nanoHUB. ACTA ACUST UNITED AC 2008. [DOI: 10.1088/1742-6596/107/1/012004] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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