Molecular Interaction Map Research Articles

Bagging Statistical Network Inference from Large-Scale Gene Expression Data

Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository.

Scanning image correlation spectroscopy

Molecular interactions are at the origin of life. How molecules get at different locations in the cell and how they locate their partners is a major and partially unresolved question in biology that is paramount to signaling. Spatio-temporal correlations of fluctuating fluorescently tagged molecules reveal how they move, interact, and bind in the different cellular compartments. Methods based on fluctuations represent a remarkable technical advancement in biological imaging. Here we discuss image analysis methods based on spatial and temporal correlation of fluctuations, raster image correlation spectroscopy, number and brightness, and spatial cross-correlations that give us information about how individual molecules move in cells and interact with partners at the single molecule level. These methods can be implemented with a standard laser scanning microscope and produce a cellular level spatio-temporal map of molecular interactions.

Rewiring carbon catabolite repression for microbial cell factory

Carbon catabolite repression (CCR) is a key regulatory system found in most microorganisms that ensures preferential utilization of energy-efficient carbon sources. CCR helps microorganisms obtain a proper balance between their metabolic capacity and the maximum sugar uptake capability. It also constrains the deregulated utilization of a preferred cognate substrate, enabling microorganisms to survive and dominate in natural environments. On the other side of the same coin lies the tenacious bottleneck in microbial production of bioproducts that employs a combination of carbon sources in varied proportion, such as lignocellulose-derived sugar mixtures. Preferential sugar uptake combined with the transcriptional and/or enzymatic exclusion of less preferred sugars turns out one of the major barriers in increasing the yield and productivity of fermentation process. Accumulation of the unused substrate also complicates the downstream processes used to extract the desired product. To overcome this difficulty and to develop tailor-made strains for specific metabolic engineering goals, quantitative and systemic understanding of the molecular interaction map behind CCR is a prerequisite. Here we comparatively review the universal and strain-specific features of CCR circuitry and discuss the recent efforts in developing synthetic cell factories devoid of CCR particularly for lignocellulose- based biorefinery.

Fast and accurate modeling of protein-protein interactions by combining template-interface-based docking with flexible refinement.

The similarity between folding and binding led us to posit the concept that the number of protein-protein interface motifs in nature is limited, and interacting protein pairs can use similar interface architectures repeatedly, even if their global folds completely vary. Thus, known protein-protein interface architectures can be used to model the complexes between two target proteins on the proteome scale, even if their global structures differ. This powerful concept is combined with a flexible refinement and global energy assessment tool. The accuracy of the method is highly dependent on the structural diversity of the interface architectures in the template dataset. Here, we validate this knowledge-based combinatorial method on the Docking Benchmark and show that it efficiently finds high-quality models for benchmark complexes and their binding regions even in the absence of template interfaces having sequence similarity to the targets. Compared to "classical" docking, it is computationally faster; as the number of target proteins increases, the difference becomes more dramatic. Further, it is able to distinguish binders from nonbinders. These features allow performing large-scale network modeling. The results on an independent target set (proteins in the p53 molecular interaction map) show that current method can be used to predict whether a given protein pair interacts. Overall, while constrained by the diversity of the template set, this approach efficiently produces high-quality models of protein-protein complexes. We expect that with the growing number of known interface architectures, this type of knowledge-based methods will be increasingly used by the broad proteomics community.

Functional Dissection of Dynamic Molecular Networks in Innate Immunity

Innate immune processes are very much centered on the management of molecular interactions. A variety of activities demand it: Detection of pathogens and danger signals, distinction of self- from non-self molecules, signaling to initiate both transcriptional and non-transcriptional responses, neutralization of pathogens, resolution of infection and inflammation. In the dynamic course of innate immune responses, one can roughly distinguish between a first alarm-calling phase leading to the secretion of type I interferons and pro-inflammatory cytokines and a response and execution phase during which pathogens are neutralized, presented to cells of the adaptive immune system, and the alarm is switched off. Using affinity proteomics coupled to mass spectrometry, bioinformatics, pathogen infections, RNAi and gene inactivation in mice for selected cases, we have step-wise mapped the cellular proteins involved in these processes and monitored interactions among themselves as well as with perturbing proteins from invading pathogens. We have also used pathogen-associated molecular patterns as affinity baits to map the cell’s recognition machinery. Overall, we have obtained an overview of the dynamic assembly, disassembly and regulation of several protein complexes essential for innate immunity, such as the IFIT, TLR and NLRPs complexes as well as on the logic that a variety of different pathogens use to overturn cellular processes to their own advantage. Comprehensive and time-resolved maps of molecular interactions under defined inflammatory and infectious conditions in the future will help obtained a more detailed understanding of the innate immunity programs. From a biochemical and systems-biological point of view it is already clear that subtle calibration of accurate molecular interactions is of uttermost importance for the cell to safeguard its homeostasis in face of perilous environmental challenges such as pathogen infections.

PathVisio-Validator: a rule-based validation plugin for graphical pathway notations

The PathVisio-Validator plugin aims to simplify the task of producing biological pathway diagrams that follow graphical standardized notations, such as Molecular Interaction Maps or the Systems Biology Graphical Notation. This plugin assists in the creation of pathway diagrams by ensuring correct usage of a notation, and thereby reducing ambiguity when diagrams are shared among biologists. Rulesets, needed in the validation process, can be generated for any graphical notation that a developer desires, using either Schematron or Groovy. The plugin also provides support for filtering validation results, validating on a subset of rules, and distinguishing errors and warnings.

Mathematical modeling of fission yeast Schizosaccharomyces pombe cell cycle: exploring the role of multiple phosphatases

Cell cycle is the central process that regulates growth and division in all eukaryotes. Based on the environmental condition sensed, the cell lies in a resting phase G0 or proceeds through the cyclic cell division process (G1→S→G2→M). These series of events and phase transitions are governed mainly by the highly conserved Cyclin dependent kinases (Cdks) and its positive and negative regulators. The cell cycle regulation of fission yeast Schizosaccharomyces pombe is modeled in this study. The study exploits a detailed molecular interaction map compiled based on the published model and experimental data. There are accumulating evidences about the prominent regulatory role of specific phosphatases in cell cycle regulations. The current study emphasizes the possible role of multiple phosphatases that governs the cell cycle regulation in fission yeast S. pombe. The ability of the model to reproduce the reported regulatory profile for the wild-type and various mutants was verified though simulations. The online version of this article (doi:10.1007/s11693-011-9090-7) contains supplementary material, which is available to authorized users.

A comprehensive molecular interaction map for Hepatitis B virus and drug designing of a novel inhibitor for Hepatitis B X protein.

Hepatitis B virus (HBV) infection is a leading source of liver diseases such as hepatitis, cirrhosis and hepatocellular carcinoma. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Hepatitis B virus (HBV). Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by HBV. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. We find that some nodes in the network that prove to be topologically important, in particular HBx is also known to be important target protein used for the treatment of HBV. Therefore, HBx protein is the preferential choice for inhibition to stop the proteolytic processing. Hence, the 3D structure of HBx protein was downloaded from PDB. Ligands for the active site were designed using LIGBUILDER. The HBx protein's active site was explored to find out the critical interactions pattern for inhibitor binding using molecular docking methodology using AUTODOCK Vina. It should be noted that these predicted data should be validated using suitable assays for further consideration.

PathVisio-MIM: PathVisio plugin for creating and editing Molecular Interaction Maps (MIMs)

A plugin for the Java-based PathVisio pathway editor has been developed to help users draw diagrams of bioregulatory networks according to the Molecular Interaction Map (MIM) notation. Together with the core PathVisio application, this plugin presents a simple to use and cross-platform application for the construction of complex MIM diagrams with the ability to annotate diagram elements with comments, literature references and links to external databases. This tool extends the capabilities of the PathVisio pathway editor by providing both MIM-specific glyphs and support for a MIM-specific markup language file format for exchange with other MIM-compatible tools and diagram validation. The PathVisio-MIM plugin is freely available and works with versions of PathVisio 2.0.11 and later on Windows, Mac OS X and Linux. Information about MIM notation and the MIMML format is available at http://discover.nci.nih.gov/mim. The plugin, along with diagram examples, instructions and Java source code, may be downloaded at http://discover.nci.nih.gov/mim/mim_pathvisio.html.

A formal MIM specification and tools for the common exchange of MIM diagrams: an XML-Based format, an API, and a validation method

BackgroundThe Molecular Interaction Map (MIM) notation offers a standard set of symbols and rules on their usage for the depiction of cellular signaling network diagrams. Such diagrams are essential for disseminating biological information in a concise manner. A lack of software tools for the notation restricts wider usage of the notation. Development of software is facilitated by a more detailed specification regarding software requirements than has previously existed for the MIM notation.ResultsA formal implementation of the MIM notation was developed based on a core set of previously defined glyphs. This implementation provides a detailed specification of the properties of the elements of the MIM notation. Building upon this specification, a machine-readable format is provided as a standardized mechanism for the storage and exchange of MIM diagrams. This new format is accompanied by a Java-based application programming interface to help software developers to integrate MIM support into software projects. A validation mechanism is also provided to determine whether MIM datasets are in accordance with syntax rules provided by the new specification.ConclusionsThe work presented here provides key foundational components to promote software development for the MIM notation. These components will speed up the development of interoperable tools supporting the MIM notation and will aid in the translation of data stored in MIM diagrams to other standardized formats. Several projects utilizing this implementation of the notation are outlined herein. The MIM specification is available as an additional file to this publication. Source code, libraries, documentation, and examples are available at http://discover.nci.nih.gov/mim.

Abstract 4911: Molecular investigation of the FTI/Taxol synergy using an integrated framework of transcriptome analysis

Abstract Farnesyltransferase inhibitors (FTIs) are a class of anticancer drugs that have exhibited modest activity in the clinic as single agents, but were found to have promising activity in combination with the microtubule-stabilizing drug Taxol (TX), both in vitro and in vivo. Clinical trials with an FTI/TX combination have demonstrated a clinical benefit for a subset of patients refractory to previous taxane-treatment. However, the precise cellular machinery underlying the FTI/TX synergistic drug combination still needs to be elucidated. To this end, we undertook an unbiased experimental/computational approach designed to identify the key cellular components that either contribute to or drive the therapeutic synergy of this drug combination. The approach combined (i) measurements of drug-mediated changes on tubulin biomarkers and overall survival; (ii) whole-genome transcriptome analysis using the combination index drug synergy algorithm; (iii) utilization of interaction and functional databases to identify pathway connections and (iv) a reverse-engineering forward simulation (REFS) approach to identify genes that drive synergy. Drug-induced transcriptome changes were examined using whole-genome Affymetrix HGU133 Plus 2 microarrays. Gene changes deemed significant using a fold change criterion, identified as synergistic with the combination index analysis, and their biological relevance was assessed by experimental validation by qPCR and siRNA studies. Also, the transcriptome changes unique to the LNF/TX combination were evaluated in the context of detailed molecular interaction maps and groups of biological function. Our analyses identified several major network hubs that include: (i) overexpression of the class II β-tubulin isotype TUBB2A and of several heat shock protein such as HSP40 and HSP70 and (ii) synergistic overexpression of cell cycle genes CDKN1A and MDM2 (iii) through REFS modeling, prosaposin and granulin were identified as drivers of drug synergy (iv) induction of the focal adhesion molecule paxillin (PXN). Interestingly, stable knockdown of protein farnesyltransferase mimicked the effects of FTI treatment and resulted in strong substratum attachment and migration defects suggesting that PXN could be regulated by farnesyltransferase and its pharmacologic inhibitors. Ultimately, we plan to check the expression levels of the key genes in tumor biopsies from patients enrolled in the Phase 1 FTI/Taxotere clinical trial and correlate their expression in pre- and post-treatment biopsies with clinical response to the combination chemotherapy. Herein, we demonstrate a combined experimental and computational approach that includes functional analysis, data-driven simulation and experimental validation, and its power to identify key cellular components, both known and novel, in the anticancer activity of drug combinations exhibiting therapeutic synergy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4911. doi:10.1158/1538-7445.AM2011-4911

Dynamic Simulations of Pathways Downstream of ERBB-Family, Including Mutations and Treatments: Concordance with Experimental Results

The pathways downstream of ErbB-family proteins are very important in BC, especially when considering treatment with onco-protein inhibitors. We studied and implemented dynamic simulations of four downstream pathways and described the fragment of the signaling network we evaluated as a Molecular Interaction Map. Our simulations, enacted using Ordinary Differential Equations, involved 242 modified species and complexes, 279 reversible reactions and 111 catalytic reactions. Mutations within a single pathway tended to be mutually exclusive; only inhibitors acting at, or downstream (not upstream), of a given mutation were active. A double alteration along two distinct pathways required the inhibition of both pathways. We started an analysis of sensitivity/robustness of our network, and we systematically introduced several individual fluctuations of total concentrations of independent molecular species. Only very few cases showed significant sensitivity. We transduced the ErbB2 over-expressing BC line, BT474, with the HRAS (V12) mutant, then treated it with ErbB-family and phosphorylated MEK (MEKPP) inhibitors, Lapatinib and U0126, respectively. Experimental and simulation results were highly concordant, showing statistical significance for both pathways and for two respective endpoints, i.e. phosphorylated active forms of ERK and Akt, p one tailed = .0072 and = .0022, respectively. Working with a complex 39 basic species signaling network region, this technology facilitates both comprehension and effective, efficient and accurate modeling and data interpretation. Dynamic network simulations we performed proved to be both practical and valuable for a posteriori comprehension of biological networks and signaling, thereby greatly facilitating handling, and thus complete exploitation, of biological data.

Bioinformatics approach to identifying molecular biomarkers and networks in multiple sclerosis

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) white matter mediated by an autoimmune process triggered by a complex interplay between genetic and environmental factors, in which the precise molecular pathogenesis remains to be comprehensively characterized. The global analysis of genome, transcriptome, proteome and metabolome, collectively termed omics, promotes us to characterize the genome-wide molecular basis of MS. However, as omics studies produce high-throughput experimental data at one time, it is often difficult to find out the meaningful biological implications from huge datasets. Recent advances in bioinformatics and systems biology have made major breakthroughs by illustrating the cell-wide map of complex molecular interactions with the aid of the literature-based knowledgebase of molecular pathways. The integration of omics data derived from the disease-affected cells and tissues with underlying molecular networks provides a rational approach not only to identifying the disease-relevant molecular markers and pathways, but also to designing the network-based effective drugs for MS. (Clin. Exp. Neuroimmunol. doi: 10.1111/j.1759-1961.2010.00013.x, 2010)

Abstract SY19-04: A systems biology approach reveals master regulators of the mesenchymal phenotype of glioblastoma

Abstract A key challenge in cancer research is the identification of master regulators associated with oncogenesis and tumor progression, i.e., gene products that are necessary and/or sufficient to induce these events. We show that this problem can be successfully tackled by reverse-engineering and unbiased interrogation of context-specific maps of molecular interactions, commonly known as Interactomes. Specifically, it has been shown that a mesenchymal phenotype is the hallmark of tumor aggressiveness in human malignant glioma. Yet, the regulatory programs determining the associated signature are unknown. We used the ARACNe reverse-engineering algorithm to infer a genome-wide transcriptional interactome, from a large collection of high-grade glioma samples. Interactome-based analysis of the mesenchymal signature of high-grade gliomas revealed a regulatory module causally related to its transcriptional activation. Two transcription factors (C/EBP and Stat3) emerged as synergistic initiators and master regulators of mesenchymal transformation. Indeed, ectopic co-expression of C/EBPβ and Stat3 was sufficient to reprograms neural stem cells along an aberrant mesenchymal lineage. Conversely, co-silencing of these transcription factors in high-grade glioma cells led to collapse of the mesenchymal signature and systematic reduction in tumor aggressiveness. In human glioma, expression of C/EBPβ and Stat3 correlates with mesenchymal differentiation and predicts poor clinical outcome. Citation Format: Andrea Califano. A systems biology approach reveals master regulators of the mesenchymal phenotype of glioblastoma [abstract]. In: Proceedings of the AACR 101st Annual Meeting 2010; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr SY19-04

Abstract 115: An integrated framework of transcriptome analysis leads to mechanistic insights into the FTI/Taxol synergy

Abstract A novel class of anticancer agents, the farnesyltransferase inhibitors (FTIs), has exhibited modest activity in the clinic as single agents, but was found to have promising activity in combination with the microtubule-stabilizing drug Taxol (TX), both in vitro and in vivo models. Recent phase I and II oncology clinical trials with an FTI/TX combination have demonstrated a clinical benefit for a subset of patients refractory to previous taxane-treatment. However, the precise cellular machinery underlying the FTI/TX synergistic drug combination still needs to be better understood. To this end, we undertook an unbiased experimental/computational approach that combined (i) measurements of drug-mediated changes on tubulin biomarkers and overall survival; (ii) whole-genome transcriptome analysis; (iii) utilization of interaction and functional databases to identify functional and pathway connections, (iv) computational modeling and prediction based on an in silico model predictive of drug synergy. Drug-induced transcriptome changes were examined using whole-genome Affymetrix HGU133 Plus 2 microarrays. The raw data were analyzed using two global statistical analysis methods (ANOVA and SAM). Gene changes that passed both tests, using a fold change criterion, were deemed significant and subjected to experimental validation by qPCR and siRNA. The transcriptome changes that were unique to the LNF/TX combination were evaluated in the context of detailed molecular interaction maps and groups of biological function. Our analyses identified several major network hubs that include: (i) repression of the mitotic spindle-checkpoint kinase, polo-like kinase 1 (Plk1) which is also implicated in carcinogenesis and was shown to directly bind and phosphorylate α- and β-tubulins. (ii) overexpression of CDKN1A and MDM2, both of which are shown to be inversely regulated by Plk1; (iii) overexpression of the class II beta tubulin isotype TUBB2A and of several heat shock protein such as HSPA1A and DNAJB9 and (iv) induction of the focal adhesion molecule paxillin (PXN). Interestingly, stable knockdown of protein farnesyltransferase mimicked the effects of FTI treatment and resulted in strong substratum attachment and migration defects suggesting that paxillin could be regulated by farnesyltransferase and its pharmacologic inhibitors. Functional studies to elucidate this mechanism are currently underway. In summary, we demonstrate a combined experimental and computational approach that includes functional analysis, data-driven simulation and experimental validation, and its power to identify key cellular components, both known and novel, in the anticancer activity of drug combinations exhibiting therapeutic synergy. (Supported by NCI P50 CA128613 & P01 CA116676) Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 115.

Predicted Functions of MdmX in Fine-Tuning the Response of p53 to DNA Damage

Tumor suppressor protein p53 is regulated by two structurally homologous proteins, Mdm2 and MdmX. In contrast to Mdm2, MdmX lacks ubiquitin ligase activity. Although the essential interactions of MdmX are known, it is not clear how they function to regulate p53. The regulation of tumor suppressor p53 by Mdm2 and MdmX in response to DNA damage was investigated by mathematical modeling of a simplified network. The simplified network model was derived from a detailed molecular interaction map (MIM) that exhibited four coherent DNA damage response pathways. The results suggest that MdmX may amplify or stabilize DNA damage-induced p53 responses via non-enzymatic interactions. Transient effects of MdmX are mediated by reservoirs of p53∶MdmX and Mdm2∶MdmX heterodimers, with MdmX buffering the concentrations of p53 and/or Mdm2. A survey of kinetic parameter space disclosed regions of switch-like behavior stemming from such reservoir-based transients. During an early response to DNA damage, MdmX positively or negatively regulated p53 activity, depending on the level of Mdm2; this led to amplification of p53 activity and switch-like response. During a late response to DNA damage, MdmX could dampen oscillations of p53 activity. A possible role of MdmX may be to dampen such oscillations that otherwise could produce erratic cell behavior. Our study suggests how MdmX may participate in the response of p53 to DNA damage either by increasing dependency of p53 on Mdm2 or by dampening oscillations of p53 activity and presents a model for experimental investigation.

A comprehensive molecular interaction map of the budding yeast cell cycle

With the accumulation of data on complex molecular machineries coordinating cell-cycle dynamics, coupled with its central function in disease patho-physiologies, it is becoming increasingly important to collate the disparate knowledge sources into a comprehensive molecular network amenable to systems-level analyses. In this work, we present a comprehensive map of the budding yeast cell-cycle, curating reactions from ∼600 original papers. Toward leveraging the map as a framework to explore the underlying network architecture, we abstract the molecular components into three planes—signaling, cell-cycle core and structural planes. The planar view together with topological analyses facilitates network-centric identification of functions and control mechanisms. Further, we perform a comparative motif analysis to identify around 194 motifs including feed-forward, mutual inhibitory and feedback mechanisms contributing to cell-cycle robustness. We envisage the open access, comprehensive cell-cycle map to open roads toward community-based deeper understanding of cell-cycle dynamics.

A Process Calculus for Molecular Interaction Maps

We present the MIM calculus, a modeling formalism with a strong biological basis, which provides biologically-meaningful operators for representing the interaction capabilities of molecular species. The operators of the calculus are inspired by the reaction symbols used in Molecular Interaction Maps (MIMs), a diagrammatic notation used by biologists. Models of the calculus can be easily derived from MIM diagrams, for which an unambiguous and executable interpretation is thus obtained. We give a formal definition of the syntax and semantics of the MIM calculus, and we study properties of the formalism. A case study is also presented to show the use of the calculus for modeling biomolecular networks.

Challenges for automatically extracting molecular interactions from full-text articles

BackgroundThe increasing availability of full-text biomedical articles will allow more biomedical knowledge to be extracted automatically with greater reliability. However, most Information Retrieval (IR) and Extraction (IE) tools currently process only abstracts. The lack of corpora has limited the development of tools that are capable of exploiting the knowledge in full-text articles. As a result, there has been little investigation into the advantages of full-text document structure, and the challenges developers will face in processing full-text articles.ResultsWe manually annotated passages from full-text articles that describe interactions summarised in a Molecular Interaction Map (MIM). Our corpus tracks the process of identifying facts to form the MIM summaries and captures any factual dependencies that must be resolved to extract the fact completely. For example, a fact in the results section may require a synonym defined in the introduction. The passages are also annotated with negated and coreference expressions that must be resolved.We describe the guidelines for identifying relevant passages and possible dependencies. The corpus includes 2162 sentences from 78 full-text articles. Our corpus analysis demonstrates the necessity of full-text processing; identifies the article sections where interactions are most commonly stated; and quantifies the proportion of interaction statements requiring coherent dependencies. Further, it allows us to report on the relative importance of identifying synonyms and resolving negated expressions. We also experiment with an oracle sentence retrieval system using the corpus as a gold-standard evaluation set.ConclusionWe introduce the MIM corpus, a unique resource that maps interaction facts in a MIM to annotated passages within full-text articles. It is an invaluable case study providing guidance to developers of biomedical IR and IE systems, and can be used as a gold-standard evaluation set for full-text IR tasks.

Network architecture of signaling from uncoupled helicase-polymerase to cell cycle checkpoints and trans-lesion DNA synthesis

When replication is blocked by a template lesion or polymerase inhibitor while helicase continues unwinding the DNA, single stranded DNA (ssDNA) accumulates and becomes coated with RPA, which then initiates signals via PCNA mono-ubiquitination to activate trans-lesion polymerases and via ATR and Chk1 to inhibit Cdk2-dependent cell cycle progression. The signals are conveyed by way of a complex network of molecular interactions. To clarify those complexities, we have constructed a molecular interaction map (MIM) using a novel hierarchical assembly procedure. Molecules were arranged on the map in hierarchical levels according to interaction step distance from the DNA region of stalled replication. The hierarchical MIM allows us to disentangle the network’s interlocking pathways and loops and to suggest functionally significant features of network architecture. The MIM shows how parallel pathways and multiple feedback loops can provide failsafe and robust switch-like responses to replication stress. Within the central level of hierarchy ATR and Claspin together appear to function as a nexus that conveys signals from many sources to many destinations. We noted a division of labor between those two molecules, separating enzymatic and structural roles. In addition, the network architecture disclosed by the hierarchical map, suggested a speculative model for how molecular crowding and the granular localization of network components in the cell nucleus can facilitate function.