Integrated modeling and experimental approach for determining transcription factor profiles from fluorescent reporter data

<p>Abstract</p> <p>Background</p> <p>The development of quantitative models of signal transduction, as well as parameter estimation to improve existing models, depends on the ability to obtain quantitative information about various proteins that are part of the signaling pathway. However, commonly-used measurement techniques such as Western blots and mobility shift assays provide only qualitative or semi-quantitative data which cannot be used for estimating parameters. Thus there is a clear need for techniques that enable quantitative determination of signal transduction intermediates.</p> <p>Results</p> <p>This paper presents an integrated modeling and experimental approach for quantitatively determining transcription factor profiles from green fluorescent protein (GFP) reporter data. The technique consists of three steps: (1) creating data sets for green fluorescent reporter systems upon stimulation, (2) analyzing the fluorescence images to determine fluorescence intensity profiles using principal component analysis (PCA) and K-means clustering, and (3) computing the transcription factor concentration from the fluorescence intensity profiles by inverting a model describing transcription, translation, and activation of green fluorescent proteins.</p> <p>We have used this technique to quantitatively characterize activation of the transcription factor NF-κB by the cytokine TNF-α. In addition, we have applied the quantitative NF-κB profiles obtained from our technique to develop a model for TNF-α signal transduction where the parameters were estimated from the obtained data.</p> <p>Conclusion</p> <p>The technique presented here for computing transcription factor profiles from fluorescence microscopy images of reporter cells generated quantitative data on the magnitude and dynamics of NF-κB activation by TNF-α. The obtained results are in good agreement with qualitative descriptions of NF-κB activation as well as semi-quantitative experimental data from the literature. The profiles computed from the experimental data have been used to re-estimate parameters for a NF-κB model and the results of additional experiments are predicted very well by the model with the new parameter values. While the presented approach has been applied to NF-κB and TNF-α signaling, it can be used to determine the profile of any transcription factor as long as GFP reporter fluorescent profiles are available.</p

Dynamical Consequences of Bandpass Feedback Loops in a Bacterial Phosphorelay

Under conditions of nutrient limitation, Bacillus subtilis cells terminally differentiate into a dormant spore state. Progression to sporulation is controlled by a genetic circuit consisting of a phosphorelay embedded in multiple transcriptional feedback loops, which is used to activate the master regulator Spo0A by phosphorylation. These transcriptional regulatory interactions are “bandpass”-like, in the sense that activation occurs within a limited band of Spo0A~P concentrations. Additionally, recent results show that the phosphorelay activation occurs in pulses, in a cell-cycle dependent fashion. However, the impact of these pulsed bandpass interactions on the circuit dynamics preceding sporulation remains unclear. In order to address this question, we measured key features of the bandpass interactions at the single-cell level and analyzed them in the context of a simple mathematical model. The model predicted the emergence of a delayed phase shift between the pulsing activity of the different sporulation genes, as well as the existence of a stable state, with elevated Spo0A activity but no sporulation, embedded within the dynamical structure of the system. To test the model, we used time-lapse fluorescence microscopy to measure dynamics of single cells initiating sporulation. We observed the delayed phase shift emerging during the progression to sporulation, while a re-engineering of the sporulation circuit revealed behavior resembling the predicted additional state. These results show that periodically-driven bandpass feedback loops can give rise to complex dynamics in the progression towards sporulation

Tuning transcriptional regulation through signaling: A predictive theory of allosteric induction

Allosteric regulation is found across all domains of life, yet we still lack simple, predictive theories that directly link the experimentally tunable parameters of a system to its input-output response. To that end, we present a general theory of allosteric transcriptional regulation using the Monod-Wyman-Changeux model. We rigorously test this model using the ubiquitous simple repression motif in bacteria by first predicting the behavior of strains that span a large range of repressor copy numbers and DNA binding strengths and then constructing and measuring their response. Our model not only accurately captures the induction profiles of these strains but also enables us to derive analytic expressions for key properties such as the dynamic range and $[EC_{50}]$. Finally, we derive an expression for the free energy of allosteric repressors which enables us to collapse our experimental data onto a single master curve that captures the diverse phenomenology of the induction profiles.Comment: Substantial revisions for resubmission (3 new figures, significantly elaborated discussion); added Professor Mitchell Lewis as another author for his continuing contributions to the projec

Stochastic Gene Expression in a Lentiviral Positive Feedback Loop: HIV-1 Tat Fluctuations Drive Phenotypic Diversity

Stochastic gene expression has been implicated in a variety of cellular processes, including cell differentiation and disease. In this issue of Cell, Weinberger et al. (2005) take an integrated computational-experimental approach to study the Tat transactivation feedback loop in HIV-1 and show that fluctuations in a key regulator, Tat, can result in a phenotypic bifurcation. This phenomenon is observed in an isogenic population where individual cells display two distinct expression states corresponding to latent and productive infection by HIV-1. These findings demonstrate the importance of stochastic gene expression in molecular "decision-making."Comment: Supplemental data available as q-bio.MN/060800

Experimental and computational validation of models of fluorescent and luminescent reporter genes in bacteria

<p>Abstract</p> <p>Background</p> <p>Fluorescent and luminescent reporter genes have become popular tools for the real-time monitoring of gene expression in living cells. However, mathematical models are necessary for extracting biologically meaningful quantities from the primary data.</p> <p>Results</p> <p>We present a rigorous method for deriving relative protein synthesis rates (mRNA concentrations) and protein concentrations by means of kinetic models of gene expression. We experimentally and computationally validate this approach in the case of the protein Fis, a global regulator of transcription in <it>Escherichia coli</it>. We show that the mRNA and protein concentration profiles predicted from the models agree quite well with direct measurements obtained by Northern and Western blots, respectively. Moreover, we present computational procedures for taking into account systematic biases like the folding time of the fluorescent reporter protein and differences in the half-lives of reporter and host gene products. The results show that large differences in protein half-lives, more than mRNA half-lives, may be critical for the interpretation of reporter gene data in the analysis of the dynamics of regulatory systems.</p> <p>Conclusions</p> <p>The paper contributes to the development of sound methods for the interpretation of reporter gene data, notably in the context of the reconstruction and validation of models of regulatory networks. The results have wide applicability for the analysis of gene expression in bacteria and may be extended to higher organisms.</p

A Systems Biology Approach to Develop Models of Signal Transduction Pathways

Mathematical models of signal transduction pathways are characterized by a large number of proteins and uncertain parameters, yet only a limited amount of quantitative data is available. The dissertation addresses this problem using two different approaches: the first approach deals with a model simplification procedure for signaling pathways that reduces the model size but retains the physical interpretation of the remaining states, while the second approach deals with creating rich data sets by computing transcription factor profiles from fluorescent images of green-fluorescent-protein (GFP) reporter cells. For the first approach a model simplification procedure for signaling pathway models is presented. The technique makes use of sensitivity and observability analysis to select the retained proteins for the simplified model. The presented technique is applied to an IL-6 signaling pathway model. It is found that the model size can be significantly reduced and the simplified model is able to adequately predict the dynamics of key proteins of the signaling pathway. An approach for quantitatively determining transcription factor profiles from GFP reporter data is developed as the second major contribution of this work. The procedure analyzes fluorescent images to determine fluorescence intensity profiles using principal component analysis and K-means clustering, and then computes the transcription factor concentration from the fluorescence intensity profiles by solving an inverse problem involving a model describing transcription, translation, and activation of green fluorescent proteins. Activation profiles of the transcription factors NF-κB, nuclear STAT3, and C/EBPβ are obtained using the presented approach. The data for NF-κB is used to develop a model for TNF-α signal transduction while the data for nuclear STAT3 and C/EBPβ is used to verify the simplified IL-6 model. Finally, an approach is developed to compute the distribution of transcription factor profiles among a population of cells. This approach consists of an algorithm for identifying individual fluorescent cells from fluorescent images, and an algorithm to compute the distribution of transcription factor profiles from the fluorescence intensity distribution by solving an inverse problem. The technique is applied to experimental data to derive the distribution of NF-κB concentrations from fluorescent images of a NF-κB GFP reporter system

Quantitative Modeling and Estimation in Systems Biology using Fluorescent Reporter Systems

Building quantitative models of biological systems is a challenging task as these models can consist of a very large number of components with complex interactions between them and the experimental data available for model validation is often sparse and noisy. The focus in this work is on modeling and parameter estimation of biological systems that are monitored using fluorescent reporter systems. Fluorescent reporter systems are widely used for various applications such as monitoring gene expression, protein localization and protein-protein interactions. This dissertation presents various techniques to facilitate modeling of biological systems containing fluorescent reporters with special attention given to challenges arising due to limited experimental data, simultaneous monitoring of multiple events and variability in the observed response due to phenotypic differences. First, an inverse problem is formulated to estimate the dynamics of transcription factors, a crucial molecule that initiates the transcription process, using data of fluorescence intensity profiles obtained from a fluorescent reporter system. The resulting inverse problem is ill-conditioned and it is solved with the aid of regularization techniques. The main contribution is that, with the presented technique, any complex dynamics of transcription factors can be estimated using limited data of fluorescence measurements. The technique has been evaluated using simulated data as well as experimental data of a GFP reporter system of STAT3. Second, an experimental design formulation is developed to facilitate the use of multiple fluorescent reporters, with overlapping emission spectra, in the same experiment. This work develops a criterion to select the fluorescent proteins for simultaneous use such that the accuracy in the estimated contributions of individual proteins to the overall observed intensity is maximized. This technique has been validated using mixtures of different E. coli strains which express different fluorescent proteins. Finally, a population balance model of a cell population containing a fluorescence reporter system is developed to describe the variability in the observed fluorescence in cells. Factors such as rate of fluorescent protein formation as well as partitioning of the fluorescent protein on cell division have been taken into account to describe the dynamics of fluorescence intensity distributions in the cell populations. The model has been used to obtain preliminary hypotheses to explain the difference in response of HeLa cells containing the Tet-on expression system on stimulation by different levels doxycycline. Thus, this work describes techniques for building robust predictive models of biological systems such as regularization for solving ill-posed estimation problems, experimental design techniques as well as using population balance modeling to model complex multi-scale dynamics. Moreover, while the examples discussed here are motivated for fluorescent reporter systems, the developed techniques can be used for different kinds of linear or non-linear dynamic biological systems

Dynamic Characterization of the IKK:κBα:NFκB Negative Feedback Loop Using Real-Time Bioluminescence Imaging

The transcription factor NF-κB is a pivotal regulator of mammalian cell function, modulating genes implicated in cellular stress responses, proliferation, differentiation, cell survival and apoptosis, as well as immune and inflammatory responses. Improper regulation of NF-κB signaling has been implicated in a myriad of human pathological disorders, including cardiovascular and neurodegenerative diseases, chronic inflammation, and various cancers. A key regulatory node within canonical NF-κB signaling is the IKK:NF-κB: IκBα negative feedback loop that plays a major role in regulating the strength and duration of NF-κB transcriptional activity. We have developed and characterized an unique bioluminescent reporter (κB5àIκBα-FLuc) that recapitulates this transcriptionally coupled negative feedback loop, and have extensively utilized this reporter to interrogate how diverse stimuli (i.e., ligand type, duration, concentration, sequential stimulation, etc.) impact the IKK:NF-κB: IκBα negative feedback loop in cellulo and in vivo. We found that the negative feedback loop exhibits differential and reproducible dynamic patterns in response to modulation of TNFα; concentration or pulse duration, and that responses to TNFα exhibited a remarkable degree of synchronicity at the level of single cells, cell populations, and in vivo. Furthermore, we discovered a TNFα-induced transient refractory period (lasting up to 120 min) during which cells were unable to fully degrade IκBα following a second TNFα challenge, and identified nuclear export of NF-κB: IκBα complexes as a rate-limiting step that may impact this refractory period. A high-throughput RNAi screen to identify new phosphatase and kinase regulators of TNFα-induced IKK:NF-κB: IκBα negative feedback loop dynamics revealed a vast array of different IκBα-FLuc dynamic profiles, highlighting the large number and diverse activities of kinases and phosphatases regulating the NF-κB pathway. Two of these hits, PTPRJ and DAPK3, have been validated and are the subjects of current investigations to understand the physiological and/or pathophysiological relevance in NF-κB, especially in the context of TNFα signaling during cancer and inflammation in the liver. In conclusion, our studies using dynamic, real-time bioluminescence imaging have demonstrated the utility of employing bioluminescent reporters alongside traditional biochemical assays, in silico modeling, and cell/molecular biology techniques to rigorously interrogate how diverse stimuli impact the IKK:NF-κB: IκBα negative feedback loop in single cells, cell populations, and at the organ- and tissue-level in vivo

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine