Molecular interaction maps--a diagrammatic graphical language for bioregulatory networks

Systems medicine in colorectal cancer: from a mathematical model toward a new type of clinical trial

Current colorectal cancer (CRC) treatment guidelines are primarily based on clinical features, such as cancer stage and grade. However, outcomes may be improved using molecular treatment guidelines. Potentially useful biomarkers include driver mutations and somatically inherited alterations, signaling proteins (their expression levels and (post) translational modifications), mRNAs, micro-RNAs and long noncoding RNAs. Moving to an integrated system is potentially very relevant. To implement such an integrated system: we focus on an important region of the signaling network, immediately above the G1-S restriction point, and discuss the reconstruction of a Molecular Interaction Map and interrogating it with a dynamic mathematical model. Extensive model pretraining achieved satisfactory, validated, performance. The model helps to propose future target combination priorities, and restricts drastically the number of drugs to be finally tested at a cellular, in vivo, and clinical-trial level. Our model allows for the inclusion of the unique molecular profiles of each individual patient's tumor. While existing clinical guidelines are well established, dynamic modeling may be used for future targeted combination therapies, which may progressively become part of clinical practice within the near future. WIREs Syst Biol Med 2016, 8:314-336. doi: 10.1002/wsbm.1342 For further resources related to this article, please visit the WIREs website.

Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.Starting from an initial "physiologic condition", the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.

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

Background

The 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.

Results

A 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.

Conclusions

The 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.

Rule-based cell systems model of aging using feedback loop motifs mediated by stress responses

Investigating the complex systems dynamics of the aging process requires integration of a broad range of cellular processes describing damage and functional decline co-existing with adaptive and protective regulatory mechanisms. We evolve an integrated generic cell network to represent the connectivity of key cellular mechanisms structured into positive and negative feedback loop motifs centrally important for aging. The conceptual network is casted into a fuzzy-logic, hybrid-intelligent framework based on interaction rules assembled from a priori knowledge. Based upon a classical homeostatic representation of cellular energy metabolism, we first demonstrate how positive-feedback loops accelerate damage and decline consistent with a vicious cycle. This model is iteratively extended towards an adaptive response model by incorporating protective negative-feedback loop circuits. Time-lapse simulations of the adaptive response model uncover how transcriptional and translational changes, mediated by stress sensors NF-kappaB and mTOR, counteract accumulating damage and dysfunction by modulating mitochondrial respiration, metabolic fluxes, biosynthesis, and autophagy, crucial for cellular survival. The model allows consideration of lifespan optimization scenarios with respect to fitness criteria using a sensitivity analysis. Our work establishes a novel extendable and scalable computational approach capable to connect tractable molecular mechanisms with cellular network dynamics underlying the emerging aging phenotype.

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.

Presenting and exploring biological pathways with PathVisio

Background

Biological pathways are a useful abstraction of biological concepts, and software tools to deal with pathway diagrams can help biological research. PathVisio is a new visualization tool for biological pathways that mimics the popular GenMAPP tool with a completely new Java implementation that allows better integration with other open source projects. The GenMAPP MAPP file format is replaced by GPML, a new XML file format that provides seamless exchange of graphical pathway information among multiple programs.

Results

PathVisio can be combined with other bioinformatics tools to open up three possible uses: visual compilation of biological knowledge, interpretation of high-throughput expression datasets, and computational augmentation of pathways with interaction information. PathVisio is open source software and available at .

Conclusion

PathVisio is a graphical editor for biological pathways, with flexibility and ease of use as primary goals.

SPIKE--a database, visualization and analysis tool of cellular signaling pathways

Background

Biological signaling pathways that govern cellular physiology form an intricate web of tightly regulated interlocking processes. Data on these regulatory networks are accumulating at an unprecedented pace. The assimilation, visualization and interpretation of these data have become a major challenge in biological research, and once met, will greatly boost our ability to understand cell functioning on a systems level.

Results

To cope with this challenge, we are developing the SPIKE knowledge-base of signaling pathways. SPIKE contains three main software components: 1) A database (DB) of biological signaling pathways. Carefully curated information from the literature and data from large public sources constitute distinct tiers of the DB. 2) A visualization package that allows interactive graphic representations of regulatory interactions stored in the DB and superposition of functional genomic and proteomic data on the maps. 3) An algorithmic inference engine that analyzes the networks for novel functional interplays between network components.

SPIKE is designed and implemented as a community tool and therefore provides a user-friendly interface that allows registered users to upload data to SPIKE DB. Our vision is that the DB will be populated by a distributed and highly collaborative effort undertaken by multiple groups in the research community, where each group contributes data in its field of expertise.

Conclusion

The integrated capabilities of SPIKE make it a powerful platform for the analysis of signaling networks and the integration of knowledge on such networks with omics data.

Chromatin challenges during DNA replication: a systems representation

In a recent review, A. Groth and coworkers presented a comprehensive account of nucleosome disassembly in front of a DNA replication fork, assembly behind the replication fork, and the copying of epigenetic information onto the replicated chromatin. Understanding those processes however would be enhanced by a comprehensive graphical depiction analogous to a circuit diagram. Accordingly, we have constructed a molecular interaction map (MIM) that preserves in essentially complete detail the processes described by Groth et al. The MIM organizes and elucidates the information presented by Groth et al. on the complexities of chromatin replication, thereby providing a tool for system-level comprehension of the effects of genetic mutations, altered gene expression, and pharmacologic intervention.

Depicting combinatorial complexity with the molecular interaction map notation

To help us understand how bioregulatory networks operate, we need a standard notation for diagrams analogous to electronic circuit diagrams. Such diagrams must surmount the difficulties posed by complex patterns of protein modifications and multiprotein complexes. To meet that challenge, we have designed the molecular interaction map (MIM) notation (http://discover.nci.nih.gov/mim/). Here we show the advantages of the MIM notation for three important types of diagrams: (1) explicit diagrams that define specific pathway models for computer simulation; (2) heuristic maps that organize the available information about molecular interactions and encompass the possible processes or pathways; and (3) diagrams of combinatorially complex models. We focus on signaling from the epidermal growth factor receptor family (EGFR, ErbB), a network that reflects the major challenges of representing in a compact manner the combinatorial complexity of multimolecular complexes. By comparing MIMs with other diagrams of this network that have recently been published, we show the utility of the MIM notation. These comparisons may help cell and systems biologists adopt a graphical language that is unambiguous and generally understood.

Rules for modeling signal-transduction systems

Formalized rules for protein-protein interactions have recently been introduced to represent the binding and enzymatic activities of proteins in cellular signaling. Rules encode an understanding of how a system works in terms of the biomolecules in the system and their possible states and interactions. A set of rules can be as easy to read as a diagrammatic interaction map, but unlike most such maps, rules have precise interpretations. Rules can be processed to automatically generate a mathematical or computational model for a system, which enables explanatory and predictive insights into the system's behavior. Rules are independent units of a model specification that facilitate model revision. Instead of changing a large number of equations or lines of code, as may be required in the case of a conventional mathematical model, a protein interaction can be introduced or modified simply by adding or changing a single rule that represents the interaction of interest. Rules can be defined and visualized by using graphs, so no specialized training in mathematics or computer science is necessary to create models or to take advantage of the representational precision of rules. Rules can be encoded in a machine-readable format to enable electronic storage and exchange of models, as well as basic knowledge about protein-protein interactions. Here, we review the motivation for rule-based modeling; applications of the approach; and issues that arise in model specification, simulation, and testing. We also discuss rule visualization and exchange and the software available for rule-based modeling.

Phosphorylation of BLM, dissociation from topoisomerase IIIalpha, and colocalization with gamma-H2AX after topoisomerase I-induced replication damage

Topoisomerase I-associated DNA single-strand breaks selectively trapped by camptothecins are lethal after being converted to double-strand breaks by replication fork collisions. BLM (Bloom's syndrome protein), a RecQ DNA helicase, and topoisomerase IIIalpha (Top3alpha) appear essential for the resolution of stalled replication forks (Holliday junctions). We investigated the involvement of BLM in the signaling response to Top1-mediated replication DNA damage. In BLM-complemented cells, BLM colocalized with promyelocytic leukemia protein (PML) nuclear bodies and Top3alpha. Fibroblasts without BLM showed an increased sensitivity to camptothecin, enhanced formation of Top1-DNA complexes, and delayed histone H2AX phosphorylation (gamma-H2AX). Camptothecin also induced nuclear relocalization of BLM, Top3alpha, and PML protein and replication-dependent phosphorylation of BLM on threonine 99 (T99p-BLM). T99p-BLM was also observed following replication stress induced by hydroxyurea. Ataxia telangiectasia mutated (ATM) protein and AT- and Rad9-related protein kinases, but not DNA-dependent protein kinase, appeared to play a redundant role in phosphorylating BLM. Following camptothecin treatment, T99p-BLM colocalized with gamma-H2AX but not with Top3alpha or PML. Thus, BLM appears to dissociate from Top3alpha and PML following its phosphorylation and facilitates H2AX phosphorylation in response to replication double-strand breaks induced by Top1. A defect in gamma-H2AX signaling in response to unrepaired replication-mediated double-strand breaks might, at least in part, explain the camptothecin-sensitivity of BLM-deficient cells.

Molecular interaction maps of bioregulatory networks: a general rubric for systems biology

A standard for bioregulatory network diagrams is urgently needed in the same way that circuit diagrams are needed in electronics. Several graphical notations have been proposed, but none has become standard. We have prepared many detailed bioregulatory network diagrams using the molecular interaction map (MIM) notation, and we now feel confident that it is suitable as a standard. Here, we describe the MIM notation formally and discuss its merits relative to alternative proposals. We show by simple examples how to denote all of the molecular interactions commonly found in bioregulatory networks. There are two forms of MIM diagrams. "Heuristic" MIMs present the repertoire of interactions possible for molecules that are colocalized in time and place. "Explicit" MIMs define particular models (derived from heuristic MIMs) for computer simulation. We show also how pathways or processes can be highlighted on a canonical heuristic MIM. Drawing a MIM diagram, adhering to the rules of notation, imposes a logical discipline that sharpens one's understanding of the structure and function of a network.

Tetrandrine induces early G1 arrest in human colon carcinoma cells by down-regulating the activity and inducing the degradation of G1-S-specific cyclin-dependent kinases and by inducing p53 and p21Cip1

Tetrandrine is an antitumor alkaloid isolated from the root of Stephania tetrandra. We find that micromolar concentrations of tetrandrine irreversibly inhibit the proliferation of human colon carcinoma cells in MTT and clonogenic assays by arresting cells in G(1). Tetrandrine induces G(1) arrest before the restriction point in nocodazole- and serum-starved synchronized HT29 cells, without affecting the G(1)-S transition in aphidicolin-synchronized cells. Tetrandrine-induced G(1) arrest is followed by apoptosis as shown by fluorescence-activated cell sorting, terminal deoxynucleotidyl transferase-mediated nick end labeling, and annexin V staining assays. Tetrandrine-induced early G(1) arrest is mediated by at least three different mechanisms. First, tetrandrine inhibits purified cyclin-dependent kinase 2 (CDK2)/cyclin E and CDK4 without affecting significantly CDK2/cyclin A, CDK1/cyclin B, and CDK6. Second, tetrandrine induces the proteasome-dependent degradation of CDK4, CDK6, cyclin D1, and E2F1. Third, tetrandrine increases the expression of p53 and p21(Cip1) in wild-type p53 HCT116 cells. Collectively, these results show that tetrandrine arrests cells in G(1) by convergent mechanisms, including down-regulation of E2F1 and up-regulation of p53/p21(Cip1).

Apoptosis defects and chemotherapy resistance: molecular interaction maps and networks

Intrinsic (innate) and acquired (adaptive) resistance to chemotherapy critically limits the outcome of cancer treatments. For many years, it was assumed that the interaction of a drug with its molecular target would yield a lethal lesion, and that determinants of intrinsic drug resistance should therefore be sought either at the target level (quantitative changes or/and mutations) or upstream of this interaction, in drug metabolism or drug transport mechanisms. It is now apparent that independent of the factors above, cellular responses to a molecular lesion can determine the outcome of therapy. This review will focus on programmed cell death (apoptosis) and on survival pathways (Bcl-2, Apaf-1, AKT, NF-kappaB) involved in multidrug resistance. We will present our molecular interaction mapping conventions to summarize the AKT and IkappaB/NF-kappaB networks. They complement the p53, Chk2 and c-Abl maps published recently. We will also introduce the 'permissive apoptosis-resistance' model for the selection of multidrug-resistant cells.