Mathematical and computational models have become indispensable tools for integrating and interpreting heterogeneous biological data, understanding fundamental principles of biological system functions, generating reliable testable hypotheses, and identifying potential diagnostic markers and therapeutic targets. Thus, such tools are now routinely used in the theoretical and experimental systematic investigation of biological system dynamics. Here, we discuss model building as an essential part of the theoretical and experimental analysis of biomolecular network dynamics. Specifically, we describe a procedure for defining kinetic equations and parameters of biomolecular processes, and we illustrate the use of fractional activity functions for modeling gene expression regulation by single and multiple regulators. We further discuss the evaluation of model complexity and the selection of an optimal model based on information criteria. Finally, we discuss the critical roles of sensitivity, robustness analysis, and optimal experiment design in the model building cycle.
Biomolecular network; Differential equation; Dynamical system; Inverse problem; Mathematical model; Systems biology
Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform these data into biological knowledge, for example, how to use these data to answer questions such as: Which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network reconstruction, and network interrogation. Here we provide an overview of network analysis including a step-by-step guide on how to perform and use this approach to investigate a biological question. In this guide, we also include the software packages that we and others employ for each of the steps of a network analysis workflow.
network reconstruction; network interrogation; systems biology; big data; data integration; inter-omics network; transkingdom network
We report the first systems biology investigation of regulators controlling arterial plaque macrophage transcriptional changes in response to lipid lowering in vivo in two distinct mouse models of atherosclerosis regression. Transcriptome measurements from plaque macrophages from the Reversa mouse were integrated with measurements from an aortic transplant-based mouse model of plaque regression. Functional relevance of the genes detected as differentially expressed in plaque macrophages in response to lipid lowering in vivo was assessed through analysis of gene functional annotations, overlap with in vitro foam cell studies, and overlap of associated eQTLs with human atherosclerosis/CAD risk SNPs. To identify transcription factors that control plaque macrophage responses to lipid lowering in vivo, we used an integrative strategy – leveraging macrophage epigenomic measurements – to detect enrichment of transcription factor binding sites upstream of genes that are differentially expressed in plaque macrophages during regression. The integrated analysis uncovered eight transcription factor binding site elements that were statistically overrepresented within the 5′ regulatory regions of genes that were upregulated in plaque macrophages in the Reversa model under maximal regression conditions and within the 5′ regulatory regions of genes that were upregulated in the aortic transplant model during regression. Of these, the TCF/LEF binding site was present in promoters of upregulated genes related to cell motility, suggesting that the canonical Wnt signaling pathway may be activated in plaque macrophages during regression. We validated this network-based prediction by demonstrating that β-catenin expression is higher in regressing (vs. control group) plaques in both regression models, and we further demonstrated that stimulation of canonical Wnt signaling increases macrophage migration in vitro. These results suggest involvement of canonical Wnt signaling in macrophage emigration from the plaque during lipid lowering-induced regression, and they illustrate the discovery potential of an epigenome-guided, systems approach to understanding atherosclerosis regression.
Atherosclerosis, a progressive accumulation of lipid-rich plaque within arteries, is an inflammatory disease in which the response of macrophages (a key cell type of the innate immune system) to plasma lipoproteins plays a central role. In humans, the goal of significantly reducing already-established plaque through drug treatments, including statins, remains elusive. In mice, atherosclerosis can be reversed by experimental manipulations that lower circulating lipid levels. A common feature of many regression models is that macrophages transition to a less inflammatory state and emigrate from the plaque. While the molecular regulators that control these responses are largely unknown, we hypothesized that by integrating global measurements of macrophage gene expression in regressing plaques with measurements of the macrophage chromatin landscape, we could identify key molecules that control macrophage responses to the lowering of circulating lipid levels. Our systems biology analysis of plaque macrophages yielded a network in which the Wnt signaling pathway emerged as a candidate upstream regulator. Wnt signaling is known to affect both inflammation and the ability of macrophages to migrate from one location to another, and our targeted validation studies provide evidence that Wnt signaling is increased in plaque macrophages during regression. Our findings both demonstrate the power of a systems approach to uncover candidate regulators of regression and to identify a potential new therapeutic target.
Genomic information is encoded on a wide range of distance scales, ranging from tens of base pairs to megabases. We developed a multiscale framework to analyze and visualize the information content of genomic signals. Different types of signals, such as GC content or DNA methylation, are characterized by distinct patterns of signal enrichment or depletion across scales spanning several orders of magnitude. These patterns are associated with a variety of genomic annotations, including genes, nuclear lamina associated domains, and repeat elements. By integrating the information across all scales, as compared to using any single scale, we demonstrate improved prediction of gene expression from Polymerase II chromatin immunoprecipitation sequencing (ChIP-seq) measurements and we observed that gene expression differences in colorectal cancer are not most strongly related to gene body methylation, but rather to methylation patterns that extend beyond the single-gene scale.
Inflammation and fibrosis are intertwined in multiple disease processes. We have previously found that over-expression of urokinase plasminogen activator in macrophages induces spontaneous macrophage accumulation and fibrosis specific to the heart in mice. Understanding the relationship between inflammation and fibrosis in the heart is critical to developing therapies for diverse myocardial diseases. Therefore, we sought to determine if uPA induces changes in macrophage function that promote cardiac collagen accumulation.
Methods and Results
We analyzed the effect of the uPA transgene on expression of pro-inflammatory (M1) and pro-fibrotic (M2) genes and proteins in hearts and isolated macrophages of uPA overexpressing mice. We found that although there was elevation of the pro-inflammatory cytokine IL-6 in hearts of transgenic mice, IL-6 is not a major effector of uPA induced cardiac fibrosis. However, uPA expressing bone marrow-derived macrophages are polarized to express M2 genes in response to IL-4 stimulation, and these M2 genes are upregulated in uPA expressing macrophages following migration to the heart. In addition, while uPA expressing macrophages express a transcriptional profile that is seen in tumor–associated macrophages, these macrophages promote collagen expression in cardiac but not embryonic fibroblasts.
Urokinase plasminogen activator induces an M2/profibrotic phenotype in macrophages that is fully expressed after migration of macrophages into the heart. Understanding the mechanisms by which uPA modulates macrophage function may reveal insights into diverse pathologic processes.
Point-of-care diagnostic assays that are rapid, easy-to-use, and low-cost are needed for use in low-resource settings; the lateral flow test has become the standard bioassay format in such settings because it meets those criteria. However, for a number of analytes, conventional lateral flow tests lack the sensitivity needed to have clinical utility. To address this limitation, we are developing a paper network platform that extends the conventional lateral flow test to two dimensions. The two-dimensional structures allow incorporation of multi-step processes for improved sensitivity, while still retaining the positive aspects of conventional lateral flow tests. Here we create an easy-to-use, signal-amplified immunoassay based on a modified commercial strip test for human chorionic gonadotropin, the hormone used to detect pregnancy, and demonstrate an improved limit of detection compared to a conventional lateral flow assay. These results highlight the potential of the paper network platform to enhance access to high-quality diagnostic capabilities in low-resource settings in the developed and developing worlds.
two-dimensional paper networks; lateral flow assays; chemical signal amplification; gold enhancement of gold nanoparticles; immunoassay
The transcription factor ATF3 inhibits lipid body formation in macrophages during atherosclerosis in part by dampening the expression of cholesterol 25-hydroxylase.
Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of lipid-loaded macrophages in the arterial wall. We demonstrate that macrophage lipid body formation can be induced by modified lipoproteins or by inflammatory Toll-like receptor agonists. We used an unbiased approach to study the overlap in these pathways to identify regulators that control foam cell formation and atherogenesis. An analysis method integrating epigenomic and transcriptomic datasets with a transcription factor (TF) binding site prediction algorithm suggested that the TF ATF3 may regulate macrophage foam cell formation. Indeed, we found that deletion of this TF results in increased lipid body accumulation, and that ATF3 directly regulates transcription of the gene encoding cholesterol 25-hydroxylase. We further showed that production of 25-hydroxycholesterol (25-HC) promotes macrophage foam cell formation. Finally, deletion of ATF3 in Apoe−/− mice led to in vivo increases in foam cell formation, aortic 25-HC levels, and disease progression. These results define a previously unknown role for ATF3 in controlling macrophage lipid metabolism and demonstrate that ATF3 is a key intersection point for lipid metabolic and inflammatory pathways in these cells.
A common regulatory motif, where a heterodimeric transcriptional regulator positively autoregulates only one of its components, is found to have particular properties that enable precise and robust control of cellular responses to environmental stimuli, providing an explanation for the prevalence of this motif in evolved regulatory networks.
Many important biological systems rely on regulation by dimers of proteins which upregulate the transcription of numerous targets, including one, and only one, of the dimer pair. This is termed asymmetric self-upregulation.ASymmetric Self-UpREgulated (ASSURE) networks confer rapid induction of their targets and their network behaviors are robust to parameter variation—both features appear to have contributed to the prevalence of the network across widely different biological systems.Likely evolutionary precursors to ASSURE networks are symmetrically self-upregulated network mediated by homodimers. In silico and experimental studies demonstrate that the ASSURE network confers a competitive advantage over its symmetrical counterpart.
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXRα/RXRα), adipocyte differentiation (PPARγ/RXRα), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.
heterodimer; kinetic model; positive feedback; regulatory network motif; robustness
Two-dimensional paper networks (2DPNs) hold great potential for transcending the capabilities and performance of today's paper-based analytical devices. Specifically, 2DPNs enable sophisticated multi-step chemical processing sequences for sample pretreatment and analysis at a cost and ease-of-use that make them appropriate for use in settings with low resources. A quantitative understanding of flow in paper networks is essential to realizing the potential of these networks. In this report, we provide a framework for understanding flow in simple 2DPNs using experiments, analytical expressions, and computational simulations.
two-dimensional paper networks; paper microfluidics; fluid transport
Atherosclerosis, a chronic inflammatory disease of the vascular system, presents significant challenges to developing effective molecular diagnostics and novel therapies. A systems biology approach integrating data from large-scale measurements (e.g., transcriptomics, proteomics, and genomics) is successfully contributing to deciphering regulatory networks underlying the response of many different cellular systems to perturbations. Such a network analysis strategy using pathway information and data from multiple measurement platforms, tissues, and species is a promising approach to elucidate the mechanistic underpinnings of complex diseases. Here, we present our views on the contributions that a systems approach can bring to the study of atherosclerosis, propose ways to tackle the complexity of the disease in a systems manner and review recent systems-level studies of the disease.
atherosclerosis; systems biology; network analysis
Motivation: Histone acetylation (HAc) is associated with open chromatin, and HAc has been shown to facilitate transcription factor (TF) binding in mammalian cells. In the innate immune system context, epigenetic studies strongly implicate HAc in the transcriptional response of activated macrophages. We hypothesized that using data from large-scale sequencing of a HAc chromatin immunoprecipitation assay (ChIP-Seq) would improve the performance of computational prediction of binding locations of TFs mediating the response to a signaling event, namely, macrophage activation.
Results: We tested this hypothesis using a multi-evidence approach for predicting binding sites. As a training/test dataset, we used ChIP-Seq-derived TF binding site locations for five TFs in activated murine macrophages. Our model combined TF binding site motif scanning with evidence from sequence-based sources and from HAc ChIP-Seq data, using a weighted sum of thresholded scores. We find that using HAc data significantly improves the performance of motif-based TF binding site prediction. Furthermore, we find that within regions of high HAc, local minima of the HAc ChIP-Seq signal are particularly strongly correlated with TF binding locations. Our model, using motif scanning and HAc local minima, improves the sensitivity for TF binding site prediction by ∼50% over a model based on motif scanning alone, at a false positive rate cutoff of 0.01.
Availability: The data and software source code for model training and validation are freely available online at http://magnet.systemsbiology.net/hac.
Contact: email@example.com; firstname.lastname@example.org
Supplementary information: Supplementary data are available at Bioinformatics online.
High throughput sequencing has become an increasingly important tool for biological research. However, the existing software systems for managing and processing these data have not provided the flexible infrastructure that research requires.
Existing software solutions provide static and well-established algorithms in a restrictive package. However as high throughput sequencing is a rapidly evolving field, such static approaches lack the ability to readily adopt the latest advances and techniques which are often required by researchers. We have used a loosely coupled, service-oriented infrastructure to develop SeqAdapt. This system streamlines data management and allows for rapid integration of novel algorithms. Our approach also allows computational biologists to focus on developing and applying new methods instead of writing boilerplate infrastructure code.
The system is based around the Addama service architecture and is available at our website as a demonstration web application, an installable single download and as a collection of individual customizable services.
Atherosclerosis, a chronic inflammatory disease of the vascular system, presents significant challenges to developing effective molecular diagnostics and novel therapies. A systems biology approach integrating data from large-scale measurements (e.g. transcriptomics, proteomics and genomics) is successfully contributing to deciphering regulatory networks underlying the response of many different cellular systems to perturbations. Such a network analysis strategy using pathway information and data from multiple measurement platforms, tissues and species is a promising approach to elucidate the mechanistic underpinnings of complex diseases. Here, we present our views on the contributions that a systems approach can bring to the study of atherosclerosis, propose ways to tackle the complexity of the disease in a systems manner and review recent systems-level studies of the disease.
atherosclerosis; systems biology; network analysis
The innate immune system is a two-edged sword; it is absolutely required for host defense against infection but, uncontrolled, can trigger a plethora of inflammatory diseases. Here we used systems biology approaches to predict and validate a gene regulatory network involving a dynamic interplay between the transcription factors NF-κB, C/EBPδ, and ATF3 that controls inflammatory responses. We mathematically modeled transcriptional regulation of Il6 and Cebpd genes and experimentally validated the prediction that the combination of an initiator (NF-κB), an amplifier (C/EBPδ) and an attenuator (ATF3) forms a regulatory circuit that discriminates between transient and persistent Toll-like receptor 4-induced signals. Our results suggest a mechanism that enables the innate immune system to detect the duration of infection and to respond appropriately.
We demonstrate the resonance wavelength-dependent signal of colloidal gold nanoparticles adsorbed to a planar gold surface in surface plasmon resonance (SPR)-based detection. Experimental measurements of the SPR signal as a function of particle surface coverage are presented for three different resonance wavelengths. The SPR signal due to the colloidal gold nanoparticles varies across the resonance wavelengths of 650 nm, 770 nm, and 920 nm. The experimental SPR curves show good agreement with the results of a Lorentz absorbance model at the lower particle surface coverages investigated. The results demonstrate an almost 2-fold signal difference for a subset of the experimental conditions explored.
We describe the resonance wavelength-dependent signal of absorptive particles in surface plasmon resonance (SPR)-based detection using both modeling and experimental results. The particles, gold nanocages, have a significant absorption cross-section in the nearinfrared (NIR), resulting in a wavelength-dependent refractive index as measured by SPR. The SPR signal due to the nanocages varies by 4-fold over resonance wavelengths from 650 nm to 950 nm. The greatest SPR signal occurs at the longest resonance wavelengths; its magnitude is due to the inherent increase in sensitivity of SPR on gold with increasing wavelength and the optical absorption properties of the nanocages.
Macrophages are versatile immune cells that can detect a variety of pathogen-associated molecular patterns through their Toll-like receptors (TLRs). In response to microbial challenge, the TLR-stimulated macrophage undergoes an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network poses significant challenges and requires the integration of multiple experimental data types. In this work, we inferred a transcriptional network underlying TLR-stimulated murine macrophage activation. Microarray-based expression profiling and transcription factor binding site motif scanning were used to infer a network of associations between transcription factor genes and clusters of co-expressed target genes. The time-lagged correlation was used to analyze temporal expression data in order to identify potential causal influences in the network. A novel statistical test was developed to assess the significance of the time-lagged correlation. Several associations in the resulting inferred network were validated using targeted ChIP-on-chip experiments. The network incorporates known regulators and gives insight into the transcriptional control of macrophage activation. Our analysis identified a novel regulator (TGIF1) that may have a role in macrophage activation.
Macrophages play a vital role in host defense against infection by recognizing pathogens through pattern recognition receptors, such as the Toll-like receptors (TLRs), and mounting an immune response. Stimulation of TLRs initiates a complex transcriptional program in which induced transcription factor genes dynamically regulate downstream genes. Microarray-based transcriptional profiling has proved useful for mapping such transcriptional programs in simpler model organisms; however, mammalian systems present difficulties such as post-translational regulation of transcription factors, combinatorial gene regulation, and a paucity of available gene-knockout expression data. Additional evidence sources, such as DNA sequence-based identification of transcription factor binding sites, are needed. In this work, we computationally inferred a transcriptional network for TLR-stimulated murine macrophages. Our approach combined sequence scanning with time-course expression data in a probabilistic framework. Expression data were analyzed using the time-lagged correlation. A novel, unbiased method was developed to assess the significance of the time-lagged correlation. The inferred network of associations between transcription factor genes and co-expressed gene clusters was validated with targeted ChIP-on-chip experiments, and yielded insights into the macrophage activation program, including a potential novel regulator. Our general approach could be used to analyze other complex mammalian systems for which time-course expression data are available.
In transcriptional regulatory networks, the coincident binding of a combination of factors to regulate a gene implies the existence of complex mechanisms to control both the gene expression profile and specificity of the response. Unraveling this complexity is a major challenge to biologists. Here, a novel network topology-based clustering approach was applied to condition-specific genome-wide chromatin localization and expression data to characterize a dynamic transcriptional regulatory network responsive to the fatty acid oleate. A network of four (predicted) regulators of the response (Oaf1p, Pip2p, Adr1p and Oaf3p) was investigated. By analyzing trends in the network structure, we found that two groups of multi-input motifs form in response to oleate, each controlling distinct functional classes of genes. This functionality is contributed in part by Oaf1p, which is a component of both types of multi-input motifs and has two different regulatory activities depending on its binding context. The dynamic cooperation between Oaf1p and Pip2p appears to temporally synchronize the two different responses. Together, these data suggest a network mechanism involving dynamic combinatorial control for coordinating transcriptional responses.
Oaf3p; oleate; peroxisome; regulatory network; stress response
Signaling-protein mRNAs tend to have long untranslated regions (UTRs) containing binding sites for RNA-binding proteins regulating gene expression. Here we show that a PUF-family RNA-binding protein, Mpt5, represses the yeast MAP-kinase pathway controlling differentiation to the filamentous form. Mpt5 represses the protein levels of two pathway components, the Ste7 MAP-kinase kinase and the Tec1 transcriptional activator, and negatively regulates the kinase activity of the Kss1 MAP kinase. Moreover, Mpt5 specifically inhibits the output of the pathway in the absence of stimuli, and thereby prevents inappropriate cell differentiation. The results provide an example of what may be a genome-scale level of regulation at the interface of signaling networks and protein-RNA binding networks.