Gene Regulatory Signaling Research Articles

Inferring catalysis in biological systems.

In systems biology, one is often interested in the communication patterns between several species, such as genes, enzymes or proteins. These patterns become more recognisable when temporal experiments are performed. This temporal communication can be structured by reaction networks such as gene regulatory networks or signalling pathways. Mathematical modelling of data arising from such networks can reveal important details, thus helping to understand the studied system. In many cases, however, corresponding models still deviate from the observed data. This may be due to unknown but present catalytic reactions. From a modelling perspective, the question of whether a certain reaction is catalysed leads to a large increase of model candidates. For large networks the calibration of all possible models becomes computationally infeasible. We propose a method which determines a substantially reduced set of appropriate model candidates and identifies the catalyst of each reaction at the same time. This is incorporated in a multiple-step procedure which first extends the network by additional latent variables and subsequently identifies catalyst candidates using similarity analysis methods. Results from synthetic data examples suggest a good performance even for non-informative data with few observations. Applied on CD95 apoptotic pathway our method provides new insights into apoptosis regulation.

The RNA world in the 21st century-a systems approach to finding non-coding keys to clinical questions.

There was evidence that RNAs are a functionally rich class of molecules not only since the arrival of the next-generation sequencing technology. Non-coding RNAs (ncRNA) could be the key to accelerated diagnosis and enhanced prediction of disease and therapy outcomes as well as the design of advanced therapeutic strategies to overcome yet unsatisfactory approaches.In this review, we discuss the state of the art in RNA systems biology with focus on the application in the systems biomedicine field. We propose guidelines for analysing the role of microRNAs and long non-coding RNAs in human pathologies. We introduce RNA expression profiling and network approaches for the identification of stable and effective RNomics-based biomarkers, providing insights into the role of ncRNAs in disease regulation. Towards this, we discuss ways to model the dynamics of gene regulatory networks and signalling pathways that involve ncRNAs. We also describe data resources and computational methods for finding putative mechanisms of action of ncRNAs. Finally, we discuss avenues for the computer-aided design of novel RNA-based therapeutics.

Induction of hematopoietic and endothelial cell program orchestrated by ETS transcription factor ER71/ETV2.

The ETS factor ETV2 (aka ER71) is essential for the generation of the blood and vascular system, as ETV2 deficiency leads to a complete block in blood and endothelial cell formation and embryonic lethality in the mouse. However, the ETV2-mediated gene regulatory network and signaling governing hematopoietic and endothelial cell development are poorly understood. Here, we map ETV2 global binding sites and carry out in vitro differentiation of embryonic stem cells, and germ line and conditional knockout mouse studies to uncover mechanisms involved in the hemangiogenic fate commitment from mesoderm. We show that ETV2 binds to enhancers that specify hematopoietic and endothelial cell lineages. We find that the hemangiogenic progenitor population in the developing embryo can be identified as FLK1(high)PDGFRα(-). Notably, these hemangiogenic progenitors are exclusively sensitive to ETV2-dependent FLK1 signaling. Importantly, ETV2 turns on other Ets genes, thereby establishing an ETS hierarchy. Consequently, the hematopoietic and endothelial cell program initiated by ETV2 is maintained partly by other ETS factors through an ETS switching mechanism. These findings highlight the critical role that transient ETV2 expression plays in the regulation of hematopoietic and endothelial cell lineage specification and stability.

Chemical Master Equation Empirical Moment Closure

The numerical solution of the Chemical Master Equation (CME) governing gene regulatory networks and cell signaling processes remains a challenging task due to its complexity, exponentially growing with the number of species involved. When considering separated representations of the probability distribution function within the Proper Generalized Decomposition-PGD-frame-work the complexity of the CME grows only linearly with the number of state space dimensions. In order to speed up calculations moment-based descriptions are usually preferred, however these descriptions involve the necessity of using closure relations whose impact on the calculated solution is most of time unpredictable. In this work we propose an empirical closure, fitted from the solution of the chemical master equation, the last solved within the PGD framework.

Millisecond single-molecule localization microscopy combined with convolution analysis and automated image segmentation to determine protein concentrations in complexly structured, functional cells, one cell at a time.

We present a single-molecule tool called the CoPro (concentration of proteins) method that uses millisecond imaging with convolution analysis, automated image segmentation and super-resolution localization microscopy to generate robust estimates for protein concentration in different compartments of single living cells, validated using realistic simulations of complex multiple compartment cell types. We demonstrate its utility experimentally on model Escherichia coli bacteria and Saccharomyces cerevisiae budding yeast cells, and use it to address the biological question of how signals are transduced in cells. Cells in all domains of life dynamically sense their environment through signal transduction mechanisms, many involving gene regulation. The glucose sensing mechanism of S. cerevisiae is a model system for studying gene regulatory signal transduction. It uses the multi-copy expression inhibitor of the GAL gene family, Mig1, to repress unwanted genes in the presence of elevated extracellular glucose concentrations. We fluorescently labelled Mig1 molecules with green fluorescent protein (GFP) via chromosomal integration at physiological expression levels in living S. cerevisiae cells, in addition to the RNA polymerase protein Nrd1 with the fluorescent protein reporter mCherry. Using CoPro we make quantitative estimates of Mig1 and Nrd1 protein concentrations in the cytoplasm and nucleus compartments on a cell-by-cell basis under physiological conditions. These estimates indicate a ∼4-fold shift towards higher values in the concentration of diffusive Mig1 in the nucleus if the external glucose concentration is raised, whereas equivalent levels in the cytoplasm shift to smaller values with a relative change an order of magnitude smaller. This compares with Nrd1 which is not involved directly in glucose sensing, and which is almost exclusively localized in the nucleus under high and low external glucose levels. CoPro facilitates time-resolved quantification of protein concentrations in single functional cells, and enables the distributions of concentrations across a cell population to be measured. This could be useful in investigating several cellular processes that are mediated by proteins, especially where changes in protein concentration in a single cell in response to changes in the extracellular chemical environment are subtle and rapid and may be smaller than the variability across a cell population.

The TTSMI database: a catalog of triplex target DNA sites associated with genes and regulatory elements in the human genome.

A triplex target DNA site (TTS), a stretch of DNA that is composed of polypurines, is able to form a triple-helix (triplex) structure with triplex-forming oligonucleotides (TFOs) and is able to influence the site-specific modulation of gene expression and/or the modification of genomic DNA. The co-localization of a genomic TTS with gene regulatory signals and functional genome structures suggests that TFOs could potentially be exploited in antigene strategies for the therapy of cancers and other genetic diseases. Here, we present the TTS Mapping and Integration (TTSMI; http://ttsmi.bii.a-star.edu.sg) database, which provides a catalog of unique TTS locations in the human genome and tools for analyzing the co-localization of TTSs with genomic regulatory sequences and signals that were identified using next-generation sequencing techniques and/or predicted by computational models. TTSMI was designed as a user-friendly tool that facilitates (i) fast searching/filtering of TTSs using several search terms and criteria associated with sequence stability and specificity, (ii) interactive filtering of TTSs that co-localize with gene regulatory signals and non-B DNA structures, (iii) exploration of dynamic combinations of the biological signals of specific TTSs and (iv) visualization of a TTS simultaneously with diverse annotation tracks via the UCSC genome browser.

Abstract 3019: Aberrant NF-κB and Notch pathways promote CD133+ cancer stem cells in human primary cutaneous squamous cell carcinoma

Abstract The cancer stem cell (CSC) subpopulation has been implicated in inducing tumorigenesis and drug resistance, however, the unique gene program and regulatory signaling that contribute to the CSC phenotypes in human cutaneous squamous cell carcinoma (hcSCC) have not been defined. In this study, a newly improved cell sorting method was developed using a panel of multiple cell surface markers to isolate the small percentage of CD133+ cell population enriched for CSC in primary hcSCC specimens. These cells exhibited CSC features including enhanced spheroid formation in vitro and tumor formation in vivo. Distinct gene expression signatures of the CD133+ subset were profiled by comparing CD133+ CSC with CD133- cancer cells using microarray, which revealed that CD133+ cells expressed enriched stem cell and cancer-related gene programs. In the CD133+ subset, 80 differentially expressed stem cell genes were identified, with an additional set of 69 altered genes enriched in key regulatory pathways, including Notch, NF-κB, Wnt, EMT, and STAT signaling, using Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA). The expression patterns of combined signature genes of CSC and the key regulatory pathways were validated with Nanostring gene expression assay in independent tumor samples. Notch and NF-κB signaling emerged as important pathways. Inhibitors or siRNA knockdown of Notch1/2 and IKKα inhibited NF-κB activity and altered CSC phenotypes, including percentage of CD133+ cells, colony and spheroid formation. Elevated NF-κB signaling was observed in CSCs in the tumor tissues, where immunofluorescent staining showed co-localization of CD133+ and molecules of NF-κB pathway in SCC tumor specimens. Our data support the importance of aberrant NF-κB and Notch signaling in promoting CSC gene program and biologic functions, which could represent predictive diagnostic and prognostic parameters and potential targets for skin cancer prevention and treatment. Supported by NIDCD intramural projects ZIA-DC-000016, 73 and 74. Citation Format: Xinxin Quan, Nga Voong, Weiping Chen, Jamie Coupar, Steven Lee, David W. Petersen, Daniel C. Edelman, Paul S. Meltzer, Andrew Montemarano, Martin Braun, Jonathan Vogel, Carter Van Waes, Zhong Chen. Aberrant NF-κB and Notch pathways promote CD133+ cancer stem cells in human primary cutaneous squamous cell carcinoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3019. doi:10.1158/1538-7445.AM2014-3019

Differential Regulation of White-Opaque Switching by Individual Subunits of Candida albicans Mediator

The multisubunit eukaryotic Mediator complex integrates diverse positive and negative gene regulatory signals and transmits them to the core transcription machinery. Mutations in individual subunits within the complex can lead to decreased or increased transcription of certain subsets of genes, which are highly specific to the mutated subunit. Recent studies suggest a role for Mediator in epigenetic silencing. Using white-opaque morphological switching in Candida albicans as a model, we have shown that Mediator is required for the stability of both the epigenetic silenced (white) and active (opaque) states of the bistable transcription circuit driven by the master regulator Wor1. Individual deletions of eight C. albicans Mediator subunits have shown that different Mediator subunits have dramatically diverse effects on the directionality, frequency, and environmental induction of epigenetic switching. Among the Mediator deletion mutants analyzed, only Med12 has a steady-state transcriptional effect on the components of the Wor1 circuit that clearly corresponds to its effect on switching. The MED16 and MED9 genes have been found to be among a small subset of genes that are required for the stability of both the white and opaque states. Deletion of the Med3 subunit completely destabilizes the opaque state, even though the Wor1 transcription circuit is intact and can be driven by ectopic expression of Wor1. The highly impaired ability of the med3 deletion mutant to mate, even when Wor1 expression is ectopically induced, reveals that the activation of the Wor1 circuit can be decoupled from the opaque state and one of its primary biological consequences.

MicroRNAs as therapeutic targets in chemoresistance

Despite substantial progress in understanding the cancer signaling network, effective therapies remain scarce due to insufficient disruption of oncogenic pathways, drug resistance and drug-induced toxicity. New and more creative approaches are therefore required for the treatment of cancer. MicroRNAs (miRNAs) are a family of small noncoding RNAs that regulate gene expression by sequence-selective targeting of mRNAs, leading to a translational repression or mRNA degradation. Experimental evidence demonstrates that dysregulation of specific miRNAs leads to drug resistance in different cancers and correction of these miRNAs using miRNA mimics or antagomiRs can normalize the gene regulatory network and signaling pathways and sensitize cancerous cells to chemotherapy. Therefore, miRNA-based gene therapy provides an attractive anti-tumor approach for integrated cancer therapy. Here, we will discuss the involvement of microRNAs in chemotherapy resistance and focus on recent advancements in the development and delivery of miRNA-based cancer therapeutics.

Network inference using steady-state data and Goldbeter–Koshland kinetics

Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks of interest (e.g. gene regulatory or protein signalling networks) are generally nonlinear. In many settings, knowledge is available concerning relevant chemical kinetics. However, existing network inference methods for continuous, steady-state data are typically rooted in statistical formulations, which do not exploit chemical kinetics to guide inference.Herein, we present an approach to network inference for steady-state data that is rooted in non-linear descriptions of biochemical mechanism. We use equilibrium analysis of chemical kinetics to obtain functional forms that are in turn used to infer networks using steady-state data. The approach we propose is directly applicable to conventional steady-state gene expression or proteomic data and does not require knowledge of either network topology or any kinetic parameters. We illustrate the approach in the context of protein phosphorylation networks, using data simulated from a recent mechanistic model and proteomic data from cancer cell lines. In the former, the true network is known and used for assessment, whereas in the latter, results are compared against known biochemistry. We find that the proposed methodology is more effective at estimating network topology than methods based on linear models.mukherjeelab.nki.nl/CODE/GK_Kinetics.zipc.j.oates@warwick.ac.uk; s.mukherjee@nki.nlSupplementary data are available at Bioinformatics online.

Investigating the properties of bio-chemical networks of artificial organisms with opposing behaviours

Organisms, be it singled-celled organisms or multi-cellular organisms, are constantly faced with opposing objectives requiring different sets of behaviours. These behaviours can be classified into two, predatory behaviours or anti-prey behaviours, with one set of behaviours causing an opposite effect to the other. A healthy organism aims to achieve its equilibrium state or to be in homeostasis. Homeostasis is achieved when a balance between the two opposing behaviours is created and maintained. This raises some questions: is there an innate mechanism that encodes for these categories of behaviours? Is there also an innate mechanism(s) that resolves conflicts and allows switching between these two opposing behaviours? If we consider artificial organisms as single-celled organisms, how do the organisms’ gene regulatory network, metabolic network and/or signalling network (their biochemical networks) maintain homeostasis of the organisms? This paper investigates the properties of the networks of best evolved artificial organisms, in order to help answer these questions, and guide the evolutionary development of controllers for artificial systems.

Network inference using steady-state data and Goldbeter-Koshland kinetics. [corrected

Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks of interest (e.g. gene regulatory or protein signalling networks) are generally nonlinear. In many settings, knowledge is available concerning relevant chemical kinetics. However, existing network inference methods for continuous, steady-state data are typically rooted in statistical formulations, which do not exploit chemical kinetics to guide inference. Herein, we present an approach to network inference for steady-state data that is rooted in non-linear descriptions of biochemical mechanism. We use equilibrium analysis of chemical kinetics to obtain functional forms that are in turn used to infer networks using steady-state data. The approach we propose is directly applicable to conventional steady-state gene expression or proteomic data and does not require knowledge of either network topology or any kinetic parameters. We illustrate the approach in the context of protein phosphorylation networks, using data simulated from a recent mechanistic model and proteomic data from cancer cell lines. In the former, the true network is known and used for assessment, whereas in the latter, results are compared against known biochemistry. We find that the proposed methodology is more effective at estimating network topology than methods based on linear models. mukherjeelab.nki.nl/CODE/GK_Kinetics.zip c.j.oates@warwick.ac.uk; s.mukherjee@nki.nl Supplementary data are available at Bioinformatics online.

Reduction of the chemical master equation for gene regulatory networks using proper generalized decompositions

The numerical solution of the chemical master equation (CME) governing gene regulatory networks and cell signaling processes remains a challenging task owing to its complexity, exponentially growing with the number of species involved. Although most of the existing techniques rely on the use of Monte Carlo-like techniques, we present here a new technique based on the approximation of the unknown variable (the probability of having a particular chemical state) in terms of a finite sum of separable functions. In this framework, the complexity of the CME grows only linearly with the number of state space dimensions. This technique generalizes the so-called Hartree approximation, by using terms as needed in the finite sums decomposition for ensuring convergence. But noteworthy, the ease of the approximation allows for an easy treatment of unknown parameters (as is frequently the case when modeling gene regulatory networks, for instance). These unknown parameters can be considered as new space dimensions. In this way, the proposed method provides solutions for any value of the unknown parameters (within some interval of arbitrary size) in one execution of the program.

BioNetSim: a Petri net-based modeling tool for simulations of biochemical processes

BioNetSim, a Petri net-based software for modeling and simulating biochemistry processes, is developed, whose design and implement are presented in this paper, including logic construction, real-time access to KEGG (Kyoto Encyclopedia of Genes and Genomes), and BioModel database. Furthermore, glycolysis is simulated as an example of its application. BioNetSim is a helpful tool for researchers to download data, model biological network, and simulate complicated biochemistry processes. Gene regulatory networks, metabolic pathways, signaling pathways, and kinetics of cell interaction are all available in BioNetSim, which makes modeling more efficient and effective. Similar to other Petri net-based softwares, BioNetSim does well in graphic application and mathematic construction. Moreover, it shows several powerful predominances. (1) It creates models in database. (2) It realizes the real-time access to KEGG and BioModel and transfers data to Petri net. (3) It provides qualitative analysis, such as computation of constants. (4) It generates graphs for tracing the concentration of every molecule during the simulation processes.

A Parallel Algorithm for Reverse Engineering Gene Regulatory Networks

Dynamic biological systems, such as gene regulatory networks (GRNs) and protein signaling networks, are often represented as systems of ordinary differential equations. Such equations can be utilized in reverse engineering these biological networks, specifically since identifying these networks is challenging due to the cost of the necessary experiments growing with at least the square of the size of the system. Moreover, the number of possible models, proportional to the number of directed graphs connecting nodes representing the variables in the system, suffers from combinatorial explosion as the size of the system grows. Therefore, exhaustive searches for systems of nontrivial complexity are not feasible. Here we describe a practical and scalable algorithm for determining candidate network interactions based on decomposing an N-dimensional system into N one-dimensional problems. The algorithm was tested on in silico networks based on known biological GRNs. The computational complexity of the network identification is shown to increase as N2 while a parallel implementation achieves essentially linear speedup with the increasing number of processing cores. For each in silico network tested, the algorithm successfully predicts a candidate network that reproduces the network dynamics. This approach dramatically reduces the computational demand required for reverse engineering GRNs and produces a wealth of exploitable information in the process. Moreover, the candidate network topologies returned by the algorithm can be used to design future experiments aimed at gathering informative data capable of further resolving the true network topology.

Mathematical modeling of hematological malignancies

Mathematical models addressing important aspects of hematological malignancies have recently facilitated an improved understanding of the involved complex biological processes and the prediction of potential targets for therapeutic approaches. These models investigate a wide spectrum of topics ranging from metabolic processes, gene regulatory networks and signal transduction up to the behavior of cell populations. However, despite this range of biological processes, the modeling strategies share many common features. Biological knowledge is translated into abstract descriptions representing complex networks and the parameters of these mathematical models are derived from literature data or estimated from experimental measurements. The established mathematical models are used to interrogate key properties of the investigated system by model simulations. These predictions are validated based on previously published or novel experiments. Additionally, new drug targets are predicted or novel insights into biological processes are provided. Here, we summarize the strategies employed to establish four mathematical models that address different processes in leukemia and lymphoma cells. Furthermore, we show how these systems biology approaches could contribute to elucidate the pathobiology of hematological malignancies.

Mathematical modeling of hematological malignancies

Mathematical models addressing important aspects of hematological malignancies have recently facilitated an improved understanding of the involved complex biological processes and the prediction of potential targets for therapeutic approaches. These models investigate a wide spectrum of topics ranging from metabolic processes, gene regulatory networks and signal transduction up to the behavior of cell populations. However, despite this range of biological processes, the modeling strategies share many common features. Biological knowledge is translated into abstract descriptions representing complex networks and the parameters of these mathematical models are derived from literature data or estimated from experimental measurements. The established mathematical models are used to interrogate key properties of the investigated system by model simulations. These predictions are validated based on previously published or novel experiments. Additionally, new drug targets are predicted or novel insights into biological processes are provided. Here, we summarize the strategies employed to establish four mathematical models that address different processes in leukemia and lymphoma cells. Furthermore, we show how these systems biology approaches could contribute to elucidate the pathobiology of hematological malignancies.

Addressing parameter identifiability by model-based experimentation

Mathematical description of biological processes such as gene regulatory networks or signalling pathways by dynamic models utilising ordinary differential equations faces challenges if the model parameters like rate constants are estimated from incomplete and noisy experimental data. Typically, biological networks are only partially observed. Only a fraction of the modelled molecular species is measurable directly. This can result in structurally non-identifiable model parameters. Furthermore, practical non-identifiability can arise from limited amount and quality of experimental data. In the challenge of growing model complexity on one side, and experimental limitations on the other side, both types of non-identifiability arise frequently in systems biological applications often prohibiting reliable prediction of system dynamics. On theoretical grounds this article summarises how and why both types of non-identifiability arise. It exemplifies pitfalls where models do not yield reliable predictions of system dynamics because of non-identifiabilities. Subsequently, several approaches for identifiability analysis proposed in the literature are discussed. The aim is to provide an overview of applicable methods for detecting parameter identifiability issues. Once non-identifiability is detected, it can be resolved either by experimental design, measuring additional data under suitable conditions; or by model reduction, tailoring the size of the model to the information content provided by the experimental data. Both strategies enhance model predictability and will be elucidated by an example application. [Includes supplementary material].

A parallel algorithm for reverse engineering of biological networks.

Dynamic biological systems, such as gene regulatory networks (GRNs) and protein signaling networks, are often represented as systems of ordinary differential equations. Such equations can be utilized in reverse engineering these biological networks, specifically since identifying these networks is challenging due to the cost of the necessary experiments growing with at least the square of the size of the system. Moreover, the number of possible models, proportional to the number of directed graphs connecting nodes representing the variables in the system, suffers from combinatorial explosion as the size of the system grows. Therefore, exhaustive searches for systems of nontrivial complexity are not feasible. Here we describe a practical and scalable algorithm for determining candidate network interactions based on decomposing an N-dimensional system into N one-dimensional problems. The algorithm was tested on in silico networks based on known biological GRNs. The computational complexity of the network identification is shown to increase as N(2) while a parallel implementation achieves essentially linear speedup with the increasing number of processing cores. For each in silico network tested, the algorithm successfully predicts a candidate network that reproduces the network dynamics. This approach dramatically reduces the computational demand required for reverse engineering GRNs and produces a wealth of exploitable information in the process. Moreover, the candidate network topologies returned by the algorithm can be used to design future experiments aimed at gathering informative data capable of further resolving the true network topology.

Polynomial algebra of discrete models in systems biology

An increasing number of discrete mathematical models are being published in Systems Biology, ranging from Boolean network models to logical models and Petri nets. They are used to model a variety of biochemical networks, such as metabolic networks, gene regulatory networks and signal transduction networks. There is increasing evidence that such models can capture key dynamic features of biological networks and can be used successfully for hypothesis generation. This article provides a unified framework that can aid the mathematical analysis of Boolean network models, logical models and Petri nets. They can be represented as polynomial dynamical systems, which allows the use of a variety of mathematical tools from computer algebra for their analysis. Algorithms are presented for the translation into polynomial dynamical systems. Examples are given of how polynomial algebra can be used for the model analysis. alanavc@vt.edu Supplementary data are available at Bioinformatics online.