Quantitative systems biology of dynamic signal transduction and gene regulation in single cells.
Long noncoding RNA biology
Signaling in physiological environments in human cells
A rate threshold mechanism regulates MAPK stress signaling and survival
Kinetics of osmotic stress regulates a cell fate switch of cell survival
Predictive modeling and model-identification of signal transduction and gene regulation
Combining quantitative experiments with predictive modeling to understand cell signaling
Many human diseases are caused by cellular and molecular changes in signal transduction and gene regulatory pathways as a consequence of kinetic exposure of humans to harmful environmental conditions. However, when we try to understand molecular alternations that appear in disease cells, almost all biological studies today are performed in environments that are constant over time and may not represent physiologically relevant conditions. In addition, even within a group of individuals diagnosed with the same condition, the disease outcome and response to treatment at the whole body, organ, or cellular level can vastly differ. However, because most studies are based on population measurements, they assume that all cells of a specific type behave identically. To address these limitations, my lab aims to understand fundamental mechanisms of signal transduction and gene expression in normal and disease physiology in the context of kinetics and variability. We do so by applying a quantitative framework to broad biological questions in model organisms and in healthy and diseased tissue. We combine a series of single-cell, single-molecule, and genome-wide approaches and complement these quantitative assays with in-depth computational data analysis, genetics, molecular biology, chemical profiling, and single-cell predictive computational modeling. This combined quantitative experimental and computational framework is the foundation for and expertise of the Neuert lab.
Research areas are:
- Study 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 enables 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 to develop a better understanding of cellular pathways as well as better targeted therapies.
- Understand the function of the non-coding 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 control environmental conditions precisely and measure the downstream effects on lncRNA and mRNA expression patterns. Our goal is to understand the mechanisms by which lncRNAs regulate gene expression, particularly addressing whether it is the process of lncRNA transcription or the lncRNA transcript itself that is responsible for modulating the expression of the neighboring mRNA. 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 that have been implicated in disease in human cells.
 Single-cell analysis reveals that noncoding RNAs contribute to clonal heterogeneity by modulating transcription factor recruitment. SL Bumgarner, G Neuert, et al., Molecular cell 45 (4), 470-482, 2012.
- 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 in order to infer and model the relationships between genes within the 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.
 Distribution shapes govern the discovery of predictive models for gene regulation. B Munsky, G Li, ZR Fox, DP Shepherd, G Neuert, Proceedings of the National Academy of Sciences 115 (29), 7533-7538, 2018.
- Develop robust and data-driven analysis pipelines for dynamic single-cell and genomic data sets: Data generated in research directions 1 and 2 are data sets that change over time. In order to analyze these data sets correctly and to 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 and similar to our ability to generate reproducible data between biological replicas, our data analysis pipelines produce robust results even if done differently on the same data or if done by different individuals at 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.
 Building predictive signaling models by perturbing yeast cells with time-varying stimulations resulting in distinct signaling responses. H Jashnsaz, ZR Fox, B Munsky, G Neuert, STAR protocols 2 (3), 100660, 2021.