Faculty Appointments
Associate Professor of Molecular Physiology & Biophysics
Associate Professor of PharmacologyAssociate Professor of Biomedical Engineering
Education
Ph.D., Physics, Ludwig-Maximilians-Universität , Munich, GermanyM.Eng., Technical Physics, Ilmenau University of Technology, Ilmenau, Germany
Office Address
723B Light Hall
Research Description
Quantitative systems biology of dynamic signal transduction and gene regulation in single cells.
In the search for deeper insights into the molecular mechanisms of human diseases, the challenges of capturing the complex and dynamic interplay between cells and their environment remain a pressing concern. While current biological studies often focus on constant environmental conditions, the reality is that gradual exposure to harmful environmental conditions can profoundly affects to treatment among individuals with the same condition underscores the need for a more nuanced, signal transduction and gene regulatory pathways, contributing to disease pathogenesis. Moreover, the variability in disease outcomes and response to treatment among individuals with the same condition underscores the need for a more nuanced and single-cell approach to disease research.
To address these limitations, the Neuert lab has set out to shed light on the fundamental mechanisms of signal transduction and gene expression in normal and disease physiology in the context of gradual and physiologically relevant environmental changes and single-cell variability. Employing a quantitative framework to explore a range of biological questions in model organisms and healthy and diseased tissue, the lab integrates cutting-edge techniques, including single-cell, single-molecule, and genome-wide approaches, with computational data analysis, genetics, molecular biology, chemical profiling, and single-cell predictive computational modeling.
In the course of these endeavors, the lab has made significant strides in uncovering novel mechanisms, which are detailed in the lab's research section and publications. Key questions of interest to the lab include:
How do individual cells perceive physiologically relevant environments?
How do proteins generate dynamic behavior within a single cell?
How do cells regulate coding and non-coding genes in physiologically relevant dynamic and stochastic environments?
And how can single-cell behavior be analyzed, computationally modeled, and predicted to yield novel biological insights into both normal and mutated cells?
The Neuert lab provides a welcoming and supportive mentoring environment for researchers from all backgrounds. Through its innovative and multi-disciplinary approach to disease research, the lab is poised to make significant contributions to the field, providing new avenues for improving human health in the years to come.
Specific research areas are:
1. Probe signal transduction and gene regulation in physiologically relevant environments: In order to study signal transduction and gene regulatory pathways under physiologically relevant conditions, we have developed a series of methodologies that enable the precise manipulation of environments. To demonstrate the feasibility and biological importance of this approach, we interrogate and control stress response in cells, enabling the manipulation of cellular phenotype, signal transduction, and gene regulation. This approach is independent of the biological pathway or organism and presents a general methodology to interrogate and control signal transduction and gene expression pathways. Because many complex diseases are rooted in malfunctioning proteins within signaling and gene networks, improving our ability to define these networks has great potential to lay the foundation for a better understanding of cellular pathways and more targeted therapies.
2. Understand the function of the noncoding genome: Long non-coding RNAs (lncRNAs) represent a large fraction of the pervasively transcribed genome, although their function is largely unknown. The long-term goal of this area is to understand the effect of lncRNA expression on cellular function, particularly how lncRNAs contribute to gene regulation. In order to better understand the biological relevance of lncRNA, we use the model organisms Saccharomyces cerevisiae and mammalian cells to study the expression dynamics and the regulatory effects of lncRNAs on neighboring mRNAs. To accomplish this, we precisely control environmental conditions and measure downstream effects on lncRNA and mRNA expression patterns. Our goal is to understand the mechanisms by which lncRNAs regulate gene expression, particularly whether lncRNA transcription or the lncRNA transcript itself is responsible for modulating neighboring mRNA expression. Our approach is general and can be applied to any inducible lncRNA or gene regardless of the cell type or organism. We intend to apply the knowledge gained from studying these systems to similar systems implicated in disease in human cells.
3. Revolutionize predictive model identification to gain biological insight: Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes resulting from stochastic gene expression. We are developing optimal experimental design methods to quantify the unique stochastic variability patterns of different biological systems to infer and model the relationships between genes within underlying regulatory networks. Specifically, we are developing sophisticated image processing and experimental approaches to measure spatial-temporal expression patterns of multiple RNA species within the same cell. From these data, we derive multidimensional probability distributions that provide insight into the structure of gene regulatory networks. In essence, these cell-to-cell variability patterns are unique ‘fingerprints’ that reveal information about the relationships between genes within regulatory networks. The benefit of this approach is that the biological system being studied does not have to be genetically manipulated, making it particularly powerful to study mammalian gene regulatory networks.
4. Develop robust and data-driven analysis pipelines for kinetic single-cell and genomic data sets: Data generated in research directions 1 and 2 are data sets that change over time. To analyze these data sets correctly and extract the maximum amount of biological insight, we develop our own data analysis pipelines. Our focus is on data-driven, robust analysis approaches that make a minimum number of assumptions, are scalable, and lead to highly reproducible results. In addition, similar to our ability to generate reproducible data between biological replicates, our data analysis pipelines produce robust results even if done differently on the same data or if done by different individuals in different labs. Examples are our development of single-cell signal transduction analysis of time-lapse microscopy movies, the counting of long noncoding RNA in single cells, or the quantification of nascent transcription in single cells.
In the search for deeper insights into the molecular mechanisms of human diseases, the challenges of capturing the complex and dynamic interplay between cells and their environment remain a pressing concern. While current biological studies often focus on constant environmental conditions, the reality is that gradual exposure to harmful environmental conditions can profoundly affects to treatment among individuals with the same condition underscores the need for a more nuanced, signal transduction and gene regulatory pathways, contributing to disease pathogenesis. Moreover, the variability in disease outcomes and response to treatment among individuals with the same condition underscores the need for a more nuanced and single-cell approach to disease research.
To address these limitations, the Neuert lab has set out to shed light on the fundamental mechanisms of signal transduction and gene expression in normal and disease physiology in the context of gradual and physiologically relevant environmental changes and single-cell variability. Employing a quantitative framework to explore a range of biological questions in model organisms and healthy and diseased tissue, the lab integrates cutting-edge techniques, including single-cell, single-molecule, and genome-wide approaches, with computational data analysis, genetics, molecular biology, chemical profiling, and single-cell predictive computational modeling.
In the course of these endeavors, the lab has made significant strides in uncovering novel mechanisms, which are detailed in the lab's research section and publications. Key questions of interest to the lab include:
How do individual cells perceive physiologically relevant environments?
How do proteins generate dynamic behavior within a single cell?
How do cells regulate coding and non-coding genes in physiologically relevant dynamic and stochastic environments?
And how can single-cell behavior be analyzed, computationally modeled, and predicted to yield novel biological insights into both normal and mutated cells?
The Neuert lab provides a welcoming and supportive mentoring environment for researchers from all backgrounds. Through its innovative and multi-disciplinary approach to disease research, the lab is poised to make significant contributions to the field, providing new avenues for improving human health in the years to come.
Specific research areas are:
1. Probe signal transduction and gene regulation in physiologically relevant environments: In order to study signal transduction and gene regulatory pathways under physiologically relevant conditions, we have developed a series of methodologies that enable the precise manipulation of environments. To demonstrate the feasibility and biological importance of this approach, we interrogate and control stress response in cells, enabling the manipulation of cellular phenotype, signal transduction, and gene regulation. This approach is independent of the biological pathway or organism and presents a general methodology to interrogate and control signal transduction and gene expression pathways. Because many complex diseases are rooted in malfunctioning proteins within signaling and gene networks, improving our ability to define these networks has great potential to lay the foundation for a better understanding of cellular pathways and more targeted therapies.
2. Understand the function of the noncoding genome: Long non-coding RNAs (lncRNAs) represent a large fraction of the pervasively transcribed genome, although their function is largely unknown. The long-term goal of this area is to understand the effect of lncRNA expression on cellular function, particularly how lncRNAs contribute to gene regulation. In order to better understand the biological relevance of lncRNA, we use the model organisms Saccharomyces cerevisiae and mammalian cells to study the expression dynamics and the regulatory effects of lncRNAs on neighboring mRNAs. To accomplish this, we precisely control environmental conditions and measure downstream effects on lncRNA and mRNA expression patterns. Our goal is to understand the mechanisms by which lncRNAs regulate gene expression, particularly whether lncRNA transcription or the lncRNA transcript itself is responsible for modulating neighboring mRNA expression. Our approach is general and can be applied to any inducible lncRNA or gene regardless of the cell type or organism. We intend to apply the knowledge gained from studying these systems to similar systems implicated in disease in human cells.
3. Revolutionize predictive model identification to gain biological insight: Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes resulting from stochastic gene expression. We are developing optimal experimental design methods to quantify the unique stochastic variability patterns of different biological systems to infer and model the relationships between genes within underlying regulatory networks. Specifically, we are developing sophisticated image processing and experimental approaches to measure spatial-temporal expression patterns of multiple RNA species within the same cell. From these data, we derive multidimensional probability distributions that provide insight into the structure of gene regulatory networks. In essence, these cell-to-cell variability patterns are unique ‘fingerprints’ that reveal information about the relationships between genes within regulatory networks. The benefit of this approach is that the biological system being studied does not have to be genetically manipulated, making it particularly powerful to study mammalian gene regulatory networks.
4. Develop robust and data-driven analysis pipelines for kinetic single-cell and genomic data sets: Data generated in research directions 1 and 2 are data sets that change over time. To analyze these data sets correctly and extract the maximum amount of biological insight, we develop our own data analysis pipelines. Our focus is on data-driven, robust analysis approaches that make a minimum number of assumptions, are scalable, and lead to highly reproducible results. In addition, similar to our ability to generate reproducible data between biological replicates, our data analysis pipelines produce robust results even if done differently on the same data or if done by different individuals in different labs. Examples are our development of single-cell signal transduction analysis of time-lapse microscopy movies, the counting of long noncoding RNA in single cells, or the quantification of nascent transcription in single cells.
Research Keywords
Quantitative Systems Biology of signal transduction and gene regulation of coding and non-coding RNA, big data, bioinformatics, biophysics, cell death, cell biology, chromatin regulation, computational biology, computational modeling, developmental biology, drug development, drug design, epigenetic regulation, evolution, flow cytometry, gene regulation, genomics, human disease, imaging, image processing, immunology, long non-coding RNA biology, molecular biology, microbiology and immunology, machine learning, molecular pharmacology, molecular physiology, quantitative biology, microscopy, pharmacology, pharmacodynamics, pharmacokinetics, Quantitative Biology, RNA, single cells, single molecules, systems biology, signaling, signal transduction, transcription, x-chromosome inactivation, yeast genetics
Publications
Hospelhorn BG, Kesler BK, Jashnsaz H, Neuert G. TrueSpot: a robust automated tool for quantifying signal puncta in fluorescent imaging. Genome Biol. 2025 Sep 9/29/2025; 26(1): 317. PMID: 41024246, PMCID: PMC12477807, PII: 10.1186/s13059-025-03772-7, DOI: 10.1186/s13059-025-03772-7, ISSN: 1474-760X.
Hughes JJ, Kesler BK, Adams JE, Hospelhorn BG, Neuert G. TrueProbes: Quantitative Single-Molecule RNA-FISH Probe Design Improves RNA Detection. BioRxiv. 2025 Aug 8/19/2025; PMID: 40894740, PMCID: PMC12393252, PII: 2025.08.14.670355, DOI: 10.1101/2025.08.14.670355, ISSN: 2692-8205.
Kesler BK, Adams J, Neuert G. Transcriptional stochasticity reveals multiple mechanisms of long non-coding RNA regulation at the Xist-Tsix locus. Nat Commun. 2025 May 5/7/2025; 16(1): 4223. PMID: 40328749, PMCID: PMC12056010, PII: 10.1038/s41467-025-59496-6, DOI: 10.1038/s41467-025-59496-6, ISSN: 2041-1723.
Jashnsaz H, Neuert G. Phenotypic consequences of logarithmic signaling in MAPK stress response. IScience. 2025 Jan 1/17/2025; 28(1): 111625. PMID: 39886462, PMCID: PMC11780147, PII: S2589-0042(24)02852-9, DOI: 10.1016/j.isci.2024.111625, ISSN: 2589-0042.
Leasure CS, Neuert G. Modelling patient drug exposure profiles in vitro to narrow the valley of death. Nat Rev Bioeng [print-electronic]. 2024 Mar; 2(3): 196-7. PMID: 38873361, PMCID: PMC11175168, DOI: 10.1038/s44222-024-00160-x, ISSN: 2731-6092.
Thiemicke A, Neuert G. Rate thresholds in cell signaling have functional and phenotypic consequences in non-linear time-dependent environments. Front Cell Dev Biol. 2023; 11: 1124874. PMID: 37025183, PMCID: PMC10072286, PII: 1124874, DOI: 10.3389/fcell.2023.1124874, ISSN: 2296-634X.
Jashnsaz H, Fox ZR, Munsky B, Neuert G. Building predictive signaling models by perturbing yeast cells with time-varying stimulations resulting in distinct signaling responses. STAR Protoc. 2021 Sep 9/17/2021; 2(3): 100660. PMID: 34286292, PMCID: PMC8273411, PII: S2666-1667(21)00367-1, DOI: 10.1016/j.xpro.2021.100660, ISSN: 2666-1667.
Thiemicke A, Neuert G. Kinetics of osmotic stress regulate a cell fate switch of cell survival. Sci Adv [electronic-print]. 2021 Feb; 7(8): PMID: 33608274, PMCID: PMC7895434, PII: 7/8/eabe1122, DOI: 10.1126/sciadv.abe1122, ISSN: 2375-2548.
Johnson AN, Li G, Jashnsaz H, Thiemicke A, Kesler BK, Rogers DC, Neuert G. A rate threshold mechanism regulates MAPK stress signaling and survival. Proc Natl Acad Sci U S A. 2021 Jan 1/12/2021; 118(2): PMID: 33443180, PMCID: PMC7812835, PII: 2004998118, DOI: 10.1073/pnas.2004998118, ISSN: 1091-6490.
Jashnsaz H, Fox ZR, Hughes JJ, Li G, Munsky B, Neuert G. Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models. IScience. 2020 Oct 10/23/2020; 23(10): 101565. PMID: 33083733, PMCID: PMC7549069, PII: S2589-0042(20)30757-4, DOI: 10.1016/j.isci.2020.101565, ISSN: 2589-0042.
Fox ZR, Neuert G, Munsky B. Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments. Complexity [print-electronic]. 2020; 2020: PMID: 32982137, PMCID: PMC7515449, DOI: 10.1155/2020/8536365, ISSN: 1076-2787.
Kesler B, Li G, Thiemicke A, Venkat R, Neuert G. Automated cell boundary and 3D nuclear segmentation of cells in suspension. Sci Rep. 2019 Jul 7/15/2019; 9(1): 10237. PMID: 31308458, PMCID: PMC6629630, PII: 10.1038/s41598-019-46689-5, DOI: 10.1038/s41598-019-46689-5, ISSN: 2045-2322.
Thiemicke A, Jashnsaz H, Li G, Neuert G. Generating kinetic environments to study dynamic cellular processes in single cells. Sci Rep. 2019 Jul 7/12/2019; 9(1): 10129. PMID: 31300695, PMCID: PMC6625993, PII: 10.1038/s41598-019-46438-8, DOI: 10.1038/s41598-019-46438-8, ISSN: 2045-2322.
Li G, Neuert G. Multiplex RNA single molecule FISH of inducible mRNAs in single yeast cells. Sci Data. 2019 Jun 6/17/2019; 6(1): 94. PMID: 31209217, PMCID: PMC6572782, PII: 10.1038/s41597-019-0106-6, DOI: 10.1038/s41597-019-0106-6, ISSN: 2052-4463.
Munsky B, Li G, Fox ZR, Shepherd DP, Neuert G. Distribution shapes govern the discovery of predictive models for gene regulation. Proc. Natl. Acad. Sci. U.S.A [print-electronic]. 2018 Jul 7/17/2018; 115(29): 7533-8. PMID: 29959206, PMCID: PMC6055173, PII: 1804060115, DOI: 10.1073/pnas.1804060115, ISSN: 1091-6490.
Fox Z, Neuert G, Munsky B. Finite state projection based bounds to compare chemical master equation models using single-cell data. J Chem Phys. 2016 Aug 8/21/2016; 145(7): 74101. PMID: 27544081, DOI: 10.1063/1.4960505, ISSN: 1089-7690.
Munsky B, Fox Z, Neuert G. Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics. Methods [print-electronic]. 2015 Sep 9/1/2015; 85: 12-21. PMID: 26079925, PMCID: PMC4537808, PII: S1046-2023(15)00251-0, DOI: 10.1016/j.ymeth.2015.06.009, ISSN: 1095-9130.
Munsky B, Neuert G. From analog to digital models of gene regulation. Phys Biol. 2015 Jul; 12(4): 45004. PMID: 26086470, PMCID: PMC4591055, DOI: 10.1088/1478-3975/12/4/045004, ISSN: 1478-3975.
Neuert G, Munsky B, Tan RZ, Teytelman L, Khammash M, van Oudenaarden A. Systematic identification of signal-activated stochastic gene regulation. Science. 2013 Feb 2/1/2013; 339(6119): 584-7. PMID: 23372015, PMCID: PMC3751578, PII: 339/6119/584, DOI: 10.1126/science.1231456, ISSN: 1095-9203.
van Werven FJ, Neuert G, Hendrick N, Lardenois A, Buratowski S, van Oudenaarden A, Primig M, Amon A. Transcription of two long noncoding RNAs mediates mating-type control of gametogenesis in budding yeast. Cell [print-electronic]. 2012 Sep 9/14/2012; 150(6): 1170-81. PMID: 22959267, PMCID: PMC3472370, PII: S0092-8674(12)00939-7, DOI: 10.1016/j.cell.2012.06.049, ISSN: 1097-4172.
Munsky B, Neuert G, van Oudenaarden A. Using gene expression noise to understand gene regulation. Science. 2012 Apr 4/13/2012; 336(6078): 183-7. PMID: 22499939, PMCID: PMC3358231, PII: 336/6078/183, DOI: 10.1126/science.1216379, ISSN: 1095-9203.
Bumgarner SL, Neuert G, Voight BF, Symbor-Nagrabska A, Grisafi P, van Oudenaarden A, Fink GR. Single-cell analysis reveals that noncoding RNAs contribute to clonal heterogeneity by modulating transcription factor recruitment. Mol. Cell [print-electronic]. 2012 Feb 2/24/2012; 45(4): 470-82. PMID: 22264825, PMCID: PMC3288511, PII: S1097-2765(11)00996-8, DOI: 10.1016/j.molcel.2011.11.029, ISSN: 1097-4164.
Zimmermann JL, Nicolaus T, Neuert G, Blank K. Thiol-based, site-specific and covalent immobilization of biomolecules for single-molecule experiments. Nat Protoc. 2010 Jun; 5(6): 975-85. PMID: 20448543, PII: nprot.2010.49, DOI: 10.1038/nprot.2010.49, ISSN: 1750-2799.
Albrecht CH, Neuert G, Lugmaier RA, Gaub HE. Molecular force balance measurements reveal that double-stranded DNA unbinds under force in rate-dependent pathways. Biophys. J [print-electronic]. 2008 Jun; 94(12): 4766-74. PMID: 18339733, PMCID: PMC2397355, PII: S0006-3495(08)70343-6, DOI: 10.1529/biophysj.107.125427, ISSN: 1542-0086.
Neuert G, Albrecht CH, Gaub HE. Predicting the rupture probabilities of molecular bonds in series. Biophys. J [print-electronic]. 2007 Aug 8/15/2007; 93(4): 1215-23. PMID: 17468164, PMCID: PMC1929050, PII: S0006-3495(07)71379-6, DOI: 10.1529/biophysj.106.100511, ISSN: 0006-3495.
Neuert G, Albrecht C, Pamir E, Gaub HE. Dynamic force spectroscopy of the digoxigenin-antibody complex. FEBS Lett [print-electronic]. 2006 Jan 1/23/2006; 580(2): 505-9. PMID: 16388805, PII: S0014-5793(05)01535-8, DOI: 10.1016/j.febslet.2005.12.052, ISSN: 0014-5793.
Hughes JJ, Kesler BK, Adams JE, Hospelhorn BG, Neuert G. TrueProbes: Quantitative Single-Molecule RNA-FISH Probe Design Improves RNA Detection. BioRxiv. 2025 Aug 8/19/2025; PMID: 40894740, PMCID: PMC12393252, PII: 2025.08.14.670355, DOI: 10.1101/2025.08.14.670355, ISSN: 2692-8205.
Kesler BK, Adams J, Neuert G. Transcriptional stochasticity reveals multiple mechanisms of long non-coding RNA regulation at the Xist-Tsix locus. Nat Commun. 2025 May 5/7/2025; 16(1): 4223. PMID: 40328749, PMCID: PMC12056010, PII: 10.1038/s41467-025-59496-6, DOI: 10.1038/s41467-025-59496-6, ISSN: 2041-1723.
Jashnsaz H, Neuert G. Phenotypic consequences of logarithmic signaling in MAPK stress response. IScience. 2025 Jan 1/17/2025; 28(1): 111625. PMID: 39886462, PMCID: PMC11780147, PII: S2589-0042(24)02852-9, DOI: 10.1016/j.isci.2024.111625, ISSN: 2589-0042.
Leasure CS, Neuert G. Modelling patient drug exposure profiles in vitro to narrow the valley of death. Nat Rev Bioeng [print-electronic]. 2024 Mar; 2(3): 196-7. PMID: 38873361, PMCID: PMC11175168, DOI: 10.1038/s44222-024-00160-x, ISSN: 2731-6092.
Thiemicke A, Neuert G. Rate thresholds in cell signaling have functional and phenotypic consequences in non-linear time-dependent environments. Front Cell Dev Biol. 2023; 11: 1124874. PMID: 37025183, PMCID: PMC10072286, PII: 1124874, DOI: 10.3389/fcell.2023.1124874, ISSN: 2296-634X.
Jashnsaz H, Fox ZR, Munsky B, Neuert G. Building predictive signaling models by perturbing yeast cells with time-varying stimulations resulting in distinct signaling responses. STAR Protoc. 2021 Sep 9/17/2021; 2(3): 100660. PMID: 34286292, PMCID: PMC8273411, PII: S2666-1667(21)00367-1, DOI: 10.1016/j.xpro.2021.100660, ISSN: 2666-1667.
Thiemicke A, Neuert G. Kinetics of osmotic stress regulate a cell fate switch of cell survival. Sci Adv [electronic-print]. 2021 Feb; 7(8): PMID: 33608274, PMCID: PMC7895434, PII: 7/8/eabe1122, DOI: 10.1126/sciadv.abe1122, ISSN: 2375-2548.
Johnson AN, Li G, Jashnsaz H, Thiemicke A, Kesler BK, Rogers DC, Neuert G. A rate threshold mechanism regulates MAPK stress signaling and survival. Proc Natl Acad Sci U S A. 2021 Jan 1/12/2021; 118(2): PMID: 33443180, PMCID: PMC7812835, PII: 2004998118, DOI: 10.1073/pnas.2004998118, ISSN: 1091-6490.
Jashnsaz H, Fox ZR, Hughes JJ, Li G, Munsky B, Neuert G. Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models. IScience. 2020 Oct 10/23/2020; 23(10): 101565. PMID: 33083733, PMCID: PMC7549069, PII: S2589-0042(20)30757-4, DOI: 10.1016/j.isci.2020.101565, ISSN: 2589-0042.
Fox ZR, Neuert G, Munsky B. Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments. Complexity [print-electronic]. 2020; 2020: PMID: 32982137, PMCID: PMC7515449, DOI: 10.1155/2020/8536365, ISSN: 1076-2787.
Kesler B, Li G, Thiemicke A, Venkat R, Neuert G. Automated cell boundary and 3D nuclear segmentation of cells in suspension. Sci Rep. 2019 Jul 7/15/2019; 9(1): 10237. PMID: 31308458, PMCID: PMC6629630, PII: 10.1038/s41598-019-46689-5, DOI: 10.1038/s41598-019-46689-5, ISSN: 2045-2322.
Thiemicke A, Jashnsaz H, Li G, Neuert G. Generating kinetic environments to study dynamic cellular processes in single cells. Sci Rep. 2019 Jul 7/12/2019; 9(1): 10129. PMID: 31300695, PMCID: PMC6625993, PII: 10.1038/s41598-019-46438-8, DOI: 10.1038/s41598-019-46438-8, ISSN: 2045-2322.
Li G, Neuert G. Multiplex RNA single molecule FISH of inducible mRNAs in single yeast cells. Sci Data. 2019 Jun 6/17/2019; 6(1): 94. PMID: 31209217, PMCID: PMC6572782, PII: 10.1038/s41597-019-0106-6, DOI: 10.1038/s41597-019-0106-6, ISSN: 2052-4463.
Munsky B, Li G, Fox ZR, Shepherd DP, Neuert G. Distribution shapes govern the discovery of predictive models for gene regulation. Proc. Natl. Acad. Sci. U.S.A [print-electronic]. 2018 Jul 7/17/2018; 115(29): 7533-8. PMID: 29959206, PMCID: PMC6055173, PII: 1804060115, DOI: 10.1073/pnas.1804060115, ISSN: 1091-6490.
Fox Z, Neuert G, Munsky B. Finite state projection based bounds to compare chemical master equation models using single-cell data. J Chem Phys. 2016 Aug 8/21/2016; 145(7): 74101. PMID: 27544081, DOI: 10.1063/1.4960505, ISSN: 1089-7690.
Munsky B, Fox Z, Neuert G. Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics. Methods [print-electronic]. 2015 Sep 9/1/2015; 85: 12-21. PMID: 26079925, PMCID: PMC4537808, PII: S1046-2023(15)00251-0, DOI: 10.1016/j.ymeth.2015.06.009, ISSN: 1095-9130.
Munsky B, Neuert G. From analog to digital models of gene regulation. Phys Biol. 2015 Jul; 12(4): 45004. PMID: 26086470, PMCID: PMC4591055, DOI: 10.1088/1478-3975/12/4/045004, ISSN: 1478-3975.
Neuert G, Munsky B, Tan RZ, Teytelman L, Khammash M, van Oudenaarden A. Systematic identification of signal-activated stochastic gene regulation. Science. 2013 Feb 2/1/2013; 339(6119): 584-7. PMID: 23372015, PMCID: PMC3751578, PII: 339/6119/584, DOI: 10.1126/science.1231456, ISSN: 1095-9203.
van Werven FJ, Neuert G, Hendrick N, Lardenois A, Buratowski S, van Oudenaarden A, Primig M, Amon A. Transcription of two long noncoding RNAs mediates mating-type control of gametogenesis in budding yeast. Cell [print-electronic]. 2012 Sep 9/14/2012; 150(6): 1170-81. PMID: 22959267, PMCID: PMC3472370, PII: S0092-8674(12)00939-7, DOI: 10.1016/j.cell.2012.06.049, ISSN: 1097-4172.
Munsky B, Neuert G, van Oudenaarden A. Using gene expression noise to understand gene regulation. Science. 2012 Apr 4/13/2012; 336(6078): 183-7. PMID: 22499939, PMCID: PMC3358231, PII: 336/6078/183, DOI: 10.1126/science.1216379, ISSN: 1095-9203.
Bumgarner SL, Neuert G, Voight BF, Symbor-Nagrabska A, Grisafi P, van Oudenaarden A, Fink GR. Single-cell analysis reveals that noncoding RNAs contribute to clonal heterogeneity by modulating transcription factor recruitment. Mol. Cell [print-electronic]. 2012 Feb 2/24/2012; 45(4): 470-82. PMID: 22264825, PMCID: PMC3288511, PII: S1097-2765(11)00996-8, DOI: 10.1016/j.molcel.2011.11.029, ISSN: 1097-4164.
Zimmermann JL, Nicolaus T, Neuert G, Blank K. Thiol-based, site-specific and covalent immobilization of biomolecules for single-molecule experiments. Nat Protoc. 2010 Jun; 5(6): 975-85. PMID: 20448543, PII: nprot.2010.49, DOI: 10.1038/nprot.2010.49, ISSN: 1750-2799.
Albrecht CH, Neuert G, Lugmaier RA, Gaub HE. Molecular force balance measurements reveal that double-stranded DNA unbinds under force in rate-dependent pathways. Biophys. J [print-electronic]. 2008 Jun; 94(12): 4766-74. PMID: 18339733, PMCID: PMC2397355, PII: S0006-3495(08)70343-6, DOI: 10.1529/biophysj.107.125427, ISSN: 1542-0086.
Neuert G, Albrecht CH, Gaub HE. Predicting the rupture probabilities of molecular bonds in series. Biophys. J [print-electronic]. 2007 Aug 8/15/2007; 93(4): 1215-23. PMID: 17468164, PMCID: PMC1929050, PII: S0006-3495(07)71379-6, DOI: 10.1529/biophysj.106.100511, ISSN: 0006-3495.
Neuert G, Albrecht C, Pamir E, Gaub HE. Dynamic force spectroscopy of the digoxigenin-antibody complex. FEBS Lett [print-electronic]. 2006 Jan 1/23/2006; 580(2): 505-9. PMID: 16388805, PII: S0014-5793(05)01535-8, DOI: 10.1016/j.febslet.2005.12.052, ISSN: 0014-5793.