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Predicting genetic interactions from Boolean models of biological networks
Abstract
Genetic interaction can be defined as a deviation of the phenotypic
quantitative effect of a double gene mutation from the effect predicted from
single mutations using a simple (e.g., multiplicative or linear additive)
statistical model. Experimentally characterized genetic interaction networks in
model organisms provide important insights into relationships between different
biological functions. We describe a computational methodology allowing to
systematically and quantitatively characterize a Boolean mathematical model of
a biological network in terms of genetic interactions between all loss of
function and gain of function mutations with respect to all model phenotypes or
outputs. We use the probabilistic framework defined in MaBoSS software, based
on continuous time Markov chains and stochastic simulations. In addition, we
suggest several computational tools for studying the distribution of double
mutants in the space of model phenotype probabilities. We demonstrate this
methodology on three published models for each of which we derive the genetic
interaction networks and analyze their properties. We classify the obtained
interactions according to their class of epistasis, dependence on the chosen
initial conditions and phenotype. The use of this methodology for validating
mathematical models from experimental data and designing new experiments is
discussed.
... Many different types of mutations have been used to investigate dynamic behaviors of biological networks; these have focused on essential components identification [1,2], genetic interactions prediction [3], network intervention [4], and the relationship between dynamic and structural properties [5][6][7]. In addition, many computational tools have been developed to support in silico simulations based on these mutations. ...
... In genetic interactions, synergy occurs when the contribution of two mutations to the phenotype of a double mutant exceeds the expectations from the additive effects of the individual mutations [74]. Most previous studies have focused on synergy effects based on node-based mutations such as knockout and overexpression mutations [3,[75][76][77][78], because they tried to determine the functional roles of a gene or a protein. On the other hand, no previous experimental study focused on the synergistic effects of edgetic mutations. ...
... For example, we can identify some essential components [1,2] by examining the sensitivity values of the interested components. In addition, it can be used to predict genetic interactions [3] by comparing the sensitivity value of a double gene mutation from the value predicted from single mutations, and reveal the network intervention [4] by applying the state-flip mutation subject to a single gene. It is also possible to investigate an emergent property by examining the relationship between dynamic and structural properties [5][6][7]. ...
There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel user-defined mutations. In addition, RMut can specify the mutation area and the duration time for more precise analysis. RMut can be used to analyze large-scale networks because it is implemented in a parallel algorithm using the OpenCL library. In the first case study, we observed that the real biological networks were most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively. In the second case study, we showed that edgetic mutations can predict drug-targets better than node-based mutations. Finally, we examined the network sensitivity to double edge-removal mutations and found an interesting synergistic effect. Taken together, these findings indicate that RMut is a flexible R package to efficiently analyze network sensitivity to various types of mutations. RMut is available at https://github.com/csclab/RMut.
... The mutant probabilities obtained with MaBoSS in the previous step can be used to analyse the effect of mutations on double mutants. This epistasis study explores the combined effects of all double mutations in comparison with wild type and single mutants [53]. The method was applied to the metastasis model [13,53] with respect to all outputs: Homeostasis, EMT, Migration, Metastasis, Apoptosis and Cell Cycle Arrest ( Figure 5). ...
... This epistasis study explores the combined effects of all double mutations in comparison with wild type and single mutants [53]. The method was applied to the metastasis model [13,53] with respect to all outputs: Homeostasis, EMT, Migration, Metastasis, Apoptosis and Cell Cycle Arrest ( Figure 5). ...
Mathematical models can serve as a tool to formalize biological knowledge from diverse sources, to investigate biological questions in a formal way, to test experimental hypotheses, to predict the effect of perturbations and to identify underlying mechanisms. We present a pipeline of computational tools that performs a series of analyses to explore a logical model's properties. A logical model of initiation of the metastatic process in cancer is used as a transversal example. We start by analysing the structure of the interaction network constructed from the literature or existing databases. Next, we show how to translate this network into a mathematical object, specifically a logical model, and how robustness analyses can be applied to it. We explore the visualization of the stable states, defined as specific attractors of the model, and match them to cellular fates or biological read-outs. With the different tools we present here, we explain how to assign to each solution of the model a probability and how to identify genetic interactions using mutant phenotype probabilities. Finally, we connect the model to relevant experimental data: we present how some data analyses can direct the construction of the network , and how the solutions of a mathematical model can also be compared with experimental data, with a particular focus on high-throughput data in cancer biology. A step-by-step tutorial is provided as a Supplementary Material and all models, tools and scripts are provided on an accompanying website: https://github.com/sysbio-curie/Logical_modelling_pipeline. Arnau Montagud is a postdoctoral researcher in
... The GINsim tool [26] constructs the abstracted BRN of the DENGUE pathway as depicted in Figure 1 from the detailed DENGUE pathway. It is important to note that the biological Regulatory Network of DENGUE pathways is a unique contribution in and of itself [27]. We developed it following a thorough literature search of the relevant molecular entities and interactions, and then we methodically abstracted them as we have done in previous work [12]. ...
Dengue is an acute viral illness caused by the RNA virus of the family Flaviviridae and spread by Aedes mosquitoes. Intense dengue is a spreading cause of serious disease and death in some 'Asian and South American' countries. Pathogenesis is associated with the amended functioning of our innate immune system during infection. Toll Like Receptor is influential for the involucre of innate immunity able to cause dengue infection disease like pattern receptor recognition. Toll-like receptors induced by injury of a certain severity arbitrate activation of interferons and Fc receptors arbitrate the involucre of cytokines. Clearance of dengue virus is associated with interferon protein; however regulatory mechanisms have been adopted against this modified effect. The clearance is considered to be a steady state known to be characterized by a low threshold level of DENV. The pathogenic state is characterized by a high threshold level of SOCS. SOCS protein is also induced due to interferon and cytokine-amended signaling, which can subsequently play its part in the regulation of interferon and cytokine production. Our hypothesis in this research the innate immunity system is associated between the pathogenesis of the dengue virus and the SOCS-amended inhibition. We used the static formalism model of the biological regulatory network of Toll-like receptors induced by the pathogenesis of dengue amended signaling pathway. A model verification method used in GINsim was used to deduce the logical parameters for the qualitative modelling. Therefore, a multidisciplinary and translational study we constructed a formal model constraint the approach with a static and integrative computational analysis, which may identify new immunopathological mechanisms and biomarkers for differential diagnosis, opening the way for the development of specific therapies that will reduce mortality and induce morbidity by dengue virus.
... This genetic interaction study uses the following equation for each gene pair, which is equation 2 in Calzone et al., 2015: ...
Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell-line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell-line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.
... This genetic interaction study uses the following equation for each gene pairs, which is equation 2 in Calzone et al, (2015): ...
Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated.
We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell-line-specific models were built to validate our approach with dose-response data of several drugs.
The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell-line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalized Boolean models and illustrate how they can be used for precision oncology.
... Dealing with this limitation will be particularly important if genome-scale models are to be benchmarked against systematic genetic interaction datasets such as Costanzo and colleagues [97]. Existing approaches to prediction of genetic interactions from logical models still do not allow for partial loss of function mutations, for example, Calzone and colleagues [98]. If a methodology to understand the impact of partial loss of function mutations could be developed, this would open the doors to the use of logical models to predict phenotype from genotype at the genome scale, with important applications to personalised medicine. ...
The transition from mitosis into the first gap phase of the cell cycle in budding yeast is controlled by the Mitotic Exit Network (MEN). The network interprets spatiotemporal cues about the progression of mitosis and ensures that release of Cdc14 phosphatase occurs only after completion of key mitotic events. The MEN has been studied intensively; however, a unified understanding of how localisation and protein activity function together as a system is lacking. In this paper, we present a compartmental, logical model of the MEN that is capable of representing spatial aspects of regulation in parallel to control of enzymatic activity. We show that our model is capable of correctly predicting the phenotype of the majority of mutants we tested, including mutants that cause proteins to mislocalise. We use a continuous time implementation of the model to demonstrate that Cdc14 Early Anaphase Release (FEAR) ensures robust timing of anaphase, and we verify our findings in living cells. Furthermore, we show that our model can represent measured cell–cell variation in Spindle Position Checkpoint (SPoC) mutants. This work suggests a general approach to incorporate spatial effects into logical models. We anticipate that the model itself will be an important resource to experimental researchers, providing a rigorous platform to test hypotheses about regulation of mitotic exit.
... Each variable is explicitly parameterized by the rates of switching on and off but remains discrete. The formalism has been applied for predicting appearance of metastases in epithelial cancers [8], genetic interactions [30], or mutual exclusivity or cooccurrence of mutations in bladder cancer [24 ]. ...
Mathematical modeling of biological networks is a promising approach to understand the complexity of cancer progression, which can be understood as accumulated abnormalities in the kinetics of cellular biochemistry. Two major modeling formalisms (languages) have been used for this purpose in the last couple of decades: one is based on the application of classical chemical kinetics of reaction networks and the other one is based on discrete kinetics representation (called logical formalism for simplicity here), governed by logical state update rules. In this short review, we remind the reader how these two methodologies complement each other but also present the fast and recent development of semi-quantitative approaches for modeling large biological networks, with a spectrum of complementary ideas each inheriting and combining features of both modeling formalisms. We also notice an increasing influence of the recent success of machine learning and artificial intelligence onto the methodology of mathematical network modeling in cancer research, leading to appearance of a number of pragmatic hybrid approaches.
To illustrate the two approaches, logical versus kinetic modeling, we provide an example describing the same biological process with different description granularity in both discrete and continuous formalisms. The model focuses on a central question in cancer biology: understanding the mechanisms of metastasis appearance.
We conclude that despite significant progress in development of modeling ideas, predicting response of large biological networks involved in cancer to various perturbations remains a major unsolved challenge in cancer systems biology.
... Their value changes over time according to the state of their parent nodes. Exploring the dynamical properties of those computational models, such as reachability, i.e., the ability to evolve to a particular state, or attractors, i.e., the long-run behaviours, allows understanding part of important cellular processes [7][8][9]. ...
Background
Numerous cellular differentiation processes can be captured using discrete qualitative models of biological regulatory networks. These models describe the temporal evolution of the state of the network subject to different competing transitions, potentially leading the system to different attractors. This paper focusses on the formal identification of states and transitions that are crucial for preserving or pre-empting the reachability of a given behaviour.
Methods
In the context of non-deterministic automata networks, we propose a static identification of so-called bifurcations, i.e., transitions after which a given goal is no longer reachable. Such transitions are naturally good candidates for controlling the occurrence of the goal, notably by modulating their propensity. Our method combines Answer-Set Programming with static analysis of reachability properties to provide an under-approximation of all the existing bifurcations.
Results
We illustrate our discrete bifurcation analysis on several models of biological systems, for which we identify transitions which impact the reachability of given long-term behaviour. In particular, we apply our implementation on a regulatory network among hundreds of biological species, supporting the scalability of our approach.
Conclusions
Our method allows a formal and scalable identification of transitions which are responsible for the lost of capability to reach a given state. It can be applied to any asynchronous automata networks, which encompass Boolean and multi-valued models. An implementation is provided as part of the Pint software, available at http://loicpauleve.name/pint.
Electronic supplementary material
The online version of this article (doi:10.1186/s13015-017-0110-3) contains supplementary material, which is available to authorized users.
... As a possible first step in this direction, we suggest to systematically compare the effect between single and double perturbations. 84 A drug-drug interaction denoting synergy or antagonism is found when the phenotypic quantitative effect of a double perturbation deviates from the effect predicted by the simple linear additive combination of single perturbations, and can suggest candidate targets for drug combinations. Each interaction is thus associated with a score: The intensity of the color is proportional to the score value. ...
Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).
... gene/protein/phenotype), theoretical results can be confronted to many types of experiments, including singlecell behaviors. MaBoSS has already proved its efficiency in biological interpretation of cancer-related models (Calzone et al., 2015;Cohen et al., 2015;Remy et al., 2015;Verlingue et al., 2016) and is constantly evolving. ...
Motivation:
Modeling of signaling pathways is an important step towards the understanding and the treatment of diseases such as cancers, HIV or auto-immune diseases. MaBoSS is a software that allows to simulate populations of cells and to model stochastically the intracellular mechanisms that are deregulated in diseases. MaBoSS provides an output of a Boolean model in the form of time-dependent probabilities, for all biological entities (genes, proteins, phenotypes, etc.) of the model.
Results:
We present a new version of MaBoSS (2.0), including an updated version of the core software and an environment. With this environment, the needs for modeling signaling pathways are facilitated, including model construction, visualization, simulations of mutations, drug treatments and sensitivity analyses. It offers a framework for automated production of theoretical predictions.
Availability and implementation:
MaBoSS software can be found at https://maboss.curie.fr , including tutorials on existing models and examples of models.
Contact:
gautier.stoll@upmc.fr or laurence.calzone@curie.fr.
Supplementary information:
Supplementary data are available at Bioinformatics online.
... High-throughput sequencing technologies enable characterization of the genetic and epigenetic make-up and the transcriptional signature of tumors, and, by correlation analysis, how the one affects the other. Andrei Zinovyev described a computational method based on a Boolean mathematical model to predict genetic interactions and thus explain deviations of the phenotypic quantitative effect of multiple gene mutations from their simple additive effect as applied to Ewing sarcoma [13]. Theo Papamarkou discussed a novel mathematical method to also integrate so far poorly investigated RNA editing effects in gene regulatory networks based on RNA-seq data. ...
Despite multimodal treatment, long term outcome for patients with Ewing sarcoma is still poor. The second "European interdisciplinary Ewing sarcoma research summit" assembled a large group of scientific experts in the field to discuss their latest unpublished findings on the way to the identification of novel therapeutic targets and strategies. Ewing sarcoma is characterized by a quiet genome with presence of an EWSR1-ETS gene rearrangement as the only and defining genetic aberration. RNA-sequencing of recently described Ewing-like sarcomas with variant translocations identified them as biologically distinct diseases. Various presentations adressed mechanisms of EWS-ETS fusion protein activities with a focus on EWS-FLI1. Data were presented shedding light on the molecular underpinnings of genetic permissiveness to this disease uncovering interaction of EWS-FLI1 with recently discovered susceptibility loci. Epigenetic context as a consequence of the interaction between the oncoprotein, cell type, developmental stage, and tissue microenvironment emerged as dominant theme in the discussion of the molecular pathogenesis and inter- and intra-tumor heterogeneity of Ewing sarcoma, and the difficulty to generate animal models faithfully recapitulating the human disease. The problem of preclinical development of biologically targeted therapeutics was discussed and promising perspectives were offered from the study of novel in vitro models. Finally, it was concluded that in order to facilitate rapid pre-clinical and clinical development of novel therapies in Ewing sarcoma, the community needs a platform to maintain knowledge of unpublished results, systems and models used in drug testing and to continue the open dialogue initiated at the first two Ewing sarcoma summits.
... Thus, although GI prediction was only an indirect goal of this study, it laid down a path to utilize phenotype data, which are intimately connected to the definition of GIs. Calzone et al. (2015) formalized this relationship between phenotypes, loss or gain of function mutations, and GIs by statistically projecting the available data about these characteristics GI prediction. Lu et al. (2013) proposed an innovative approach of identifying synthetic lethal gene pairs based on evolutionary patterns. ...
A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI – synthetic sickness or synthetic lethality – involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.
... VidaExpert software is used in mathematical modeling of biological systems for visualizing multidimensional trajectories of the dynamical systems [40], distribution of stable states of discrete models of biological mechanisms [41]. Recently, ViDaExpert software was applied for visualizing the properties of genetic interactions predicted from mathematical modeling of regulatory networks [42]. Mapping biological networks with associated multi-variate data, applying elastic maps approach, was used for constructing the data-driven layouts of biological networks [43]. ...
Method of elastic maps allows fast learning of non-linear principal manifolds for large datasets. We present user-friendly implementation of the method in ViDaExpert software. Equipped with several dialogs for configuring data point representations (size, shape, color) and fast 3D viewer, ViDaExpert is a handy tool allowing to construct an interactive 3D-scene representing a table of data in multidimensional space and perform its quick and insightfull statistical analysis, from basic to advanced methods. We list several recent application examples of manifold learning by method of elastic maps in various fields of life sciences.
... Genetic interaction between two genes reflects their synergistic (negative interactions) or mutually alleviating (positive interactions) functions The strength of genetic interactions is characterized by an epistatic score which quantifies deviation from a simple multiplicative model [37]. In the global network of genetic interactions, each gene can be characterized by its epistatic profile, i.e., a vector of epistatic scores with all other genes [38]. ...
Visualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistic insights into biological functions. Currently, it is possible to visualize high-throughput data on top of pre-defined network layouts, but they are not always adapted to a given data analysis task. A network layout based simultaneously on the network structure and the associated multidimensional data might be advantageous for data visualization and analysis in some cases.
We developed a Cytoscape app, which allows constructing biological network layouts based on the data from molecular profiles imported as values of node attributes. DeDaL is a Cytoscape 3 app, which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. The combination of all these functionalities facilitates the creation of insightful network layouts representing both structural network features and correlation patterns in multivariate data. We demonstrate the added value of applying DeDaL in several practical applications, including an example of a large protein-protein interaction network.
DeDaL is a convenient tool for applying data dimensionality reduction methods and for designing insightful data displays based on data-driven layouts of biological networks, built within Cytoscape environment. DeDaL is freely available for downloading at http://bioinfo-out.curie.fr/projects/dedal/ .
Background:
Previously, the chromosomal translocation t(12;15)(p13;q25) has been found to recurrently occur in both solid tumors and leukemias. This translocation leads to ETV6-NTRK3 (EN) gene fusions resulting in ectopic expression of the NTRK3 neurotropic tyrosine receptor kinase moiety as well as oligomerization through the donated ETV6-sterile alpha motif domain. As yet, no in vitro cell line model carrying this anomaly is available. Here we genetically characterized the acute promyelocytic leukemia (APL) cell line AP-1060 and, by doing so, revealed the presence of a t(12;15)(p13;q25). Subsequently, we evaluated its suitability as a model for this important clinical entity.
Methods:
Spectral karyotyping, fluorescence in situ hybridization (FISH), and genomic and transcriptomic microarray-based profiling were used to screen for the presence of EN fusions. qRT-PCR was used for quantitative expression analyses. Responses to AZ-23 (NTRK) and wortmannin (PI3K) inhibitors, as well as to arsenic trioxide (ATO), were assessed using colorimetric assays. An AZ-23 microarray screen was used to define the EN targetome, which was parsed bioinformatically. MAPK1 and MALAT1 activation were assayed using Western blotting and RNA-FISH, respectively, whereas an AML patient cohort was used to assess the clinical occurrence of MALAT1 activation.
Results:
An EN fusion was detected in AP1060 cells which, accordingly, turned out to be hypersensitive to AZ-23. We also found that AZ-23 can potentiate the effect of ATO and inhibit the phosphorylation of its canonical target MAPK1. The AZ-23 microarray screen highlighted a novel EN target, MALAT1, which also proved sensitive to wortmannin. Finally, we found that MALAT1 was massively up-regulated in a subset of AML patients.
Conclusions:
From our data we conclude that AP-1060 may serve as a first publicly available preclinical model for EN. In addition, we conclude that these EN-positive cells are sensitive to the NTRK inhibitor AZ-23 and that this inhibitor may potentiate the therapeutic efficacy of ATO. Our data also highlight a novel AML EN target, MALAT1, which was so far only conspicuous in solid tumors.
In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.
In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial "PCA and K-means decipher genome". The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics.
Epithelial-to-mesenchymal transition-like (EMT-like) is a critical process allowing initiation of metastases during tumour progression. Here, to investigate its role in intestinal cancer, we combine computational network-based and experimental approaches to create a mouse model with high metastatic potential. Construction and analysis of this network map depicting molecular mechanisms of EMT regulation based on the literature suggests that Notch activation and p53 deletion have a synergistic effect in activating EMT-like processes. To confirm this prediction, we generate transgenic mice by conditionally activating the Notch1 receptor and deleting p53 in the digestive epithelium (NICD/p53(-/-)). These mice develop metastatic tumours with high penetrance. Using GFP lineage tracing, we identify single malignant cells with mesenchymal features in primary and metastatic tumours in vivo. The development of such a model that recapitulates the cellular features observed in invasive human colorectal tumours is appealing for innovative drug discovery.
A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.
The Mitogen-Activated Protein Kinase (MAPK) network consists of tightly interconnected signalling pathways involved in diverse cellular processes, such as cell cycle, survival, apoptosis and differentiation. Although several studies reported the involvement of these signalling cascades in cancer deregulations, the precise mechanisms underlying their influence on the balance between cell proliferation and cell death (cell fate decision) in pathological circumstances remain elusive. Based on an extensive analysis of published data, we have built a comprehensive and generic reaction map for the MAPK signalling network, using CellDesigner software. In order to explore the MAPK responses to different stimuli and better understand their contributions to cell fate decision, we have considered the most crucial components and interactions and encoded them into a logical model, using the software GINsim. Our logical model analysis particularly focuses on urinary bladder cancer, where MAPK network deregulations have often been associated with specific phenotypes. To cope with the combinatorial explosion of the number of states, we have applied novel algorithms for model reduction and for the compression of state transition graphs, both implemented into the software GINsim. The results of systematic simulations for different signal combinations and network perturbations were found globally coherent with published data. In silico experiments further enabled us to delineate the roles of specific components, cross-talks and regulatory feedbacks in cell fate decision. Finally, tentative proliferative or anti-proliferative mechanisms can be connected with established bladder cancer deregulations, namely Epidermal Growth Factor Receptor (EGFR) over-expression and Fibroblast Growth Factor Receptor 3 (FGFR3) activating mutations.
Identification of genes that are synthetic lethal to p53 is an important strategy for anticancer therapy as p53 mutations have been reported to occur in more than half of all human cancer cases. Although genome-wide RNAi screening is an effective approach to finding synthetic lethal genes, it is costly and labor-intensive.
To illustrate this approach, we identified potentially druggable genes synthetically lethal for p53 using three microarray datasets for gene expression profiles of the NCI-60 cancer cell lines, one next-generation sequencing (RNA-Seq) dataset from the Cancer Genome Atlas (TCGA) project, and one gene expression data from the Cancer Cell Line Encyclopedia (CCLE) project. We selected the genes which encoded kinases and had significantly higher expression in the tumors with functional p53 mutations (somatic mutations) than in the tumors without functional p53 mutations as the candidates of druggable synthetic lethal genes for p53. We identified important regulatory networks and functional categories pertinent to these genes, and performed an extensive survey of literature to find experimental evidence that support the synthetic lethality relationships between the genes identified and p53. We also examined the drug sensitivity difference between NCI-60 cell lines with functional p53 mutations and NCI-60 cell lines without functional p53 mutations for the compounds that target the kinases encoded by the genes identified.
Our results indicated that some of the candidate genes we identified had been experimentally verified to be synthetic lethal for p53 and promising targets for anticancer therapy while some other genes were putative targets for development of cancer therapeutic agents.
Our study indicated that pre-screening of potential synthetic lethal genes using gene expression profiles is a promising approach for improving the efficiency of synthetic lethal RNAi screening.
Genetic interactions reveal insights into cellular function and can be used to identify drug targets. Here we construct a new model to predict negative genetic interactions in protein complexes by exploiting the evolutionary history of genes in parallel converging pathways in metabolism. We evaluate our model with protein complexes of Saccharomyces cerevisiae and show that the predicted protein pairs more frequently have a negative genetic interaction than random proteins from the same complex. Furthermore, we apply our model to human protein complexes to predict novel cancer drug targets, and identify 20 candidate targets with empirical support and 10 novel targets amenable to further experimental validation. Our study illustrates that negative genetic interactions can be predicted by systematically exploring genome evolution, and that this is useful to identify novel anti-cancer drug targets.
Background
Public repositories of biological pathways and networks have greatly expanded in recent years. Such databases contain many pathways that facilitate the analysis of high-throughput experimental work and the formulation of new biological hypotheses to be tested, a fundamental principle of the systems biology approach. However, large-scale molecular maps are not always easy to mine and interpret.
Results
We have developed BiNoM (Biological Network Manager), a Cytoscape plugin, which provides functions for the import-export of some standard systems biology file formats (import from CellDesigner, BioPAX Level 3 and CSML; export to SBML, CellDesigner and BioPAX Level 3), and a set of algorithms to analyze and reduce the complexity of biological networks. BiNoM can be used to import and analyze files created with the CellDesigner software. BiNoM provides a set of functions allowing to import BioPAX files, but also to search and edit their content. As such, BiNoM is able to efficiently manage large BioPAX files such as whole pathway databases (e.g. Reactome). BiNoM also implements a collection of powerful graph-based functions and algorithms such as path analysis, decomposition by involvement of an entity or cyclic decomposition, subnetworks clustering and decomposition of a large network in modules.
Conclusions
Here, we provide an in-depth overview of the BiNoM functions, and we also detail novel aspects such as the support of the BioPAX Level 3 format and the implementation of a new algorithm for the quantification of pathways for influence networks. At last, we illustrate some of the BiNoM functions on a detailed biological case study of a network representing the G1/S transition of the cell cycle, a crucial cellular process disturbed in most human tumors.
Tumor development is characterized by a compromised balance between cell
life and death decision mechanisms, which are tighly regulated in normal
cells. Understanding this process provides insights for developing new
treatments for fighting with cancer. We present a study of a
mathematical model describing cellular choice between survival and two
alternative cell death modalities: apoptosis and necrosis. The model is
implemented in discrete modeling formalism and allows to predict
probabilities of having a particular cellular phenotype in response to
engagement of cell death receptors. Using an original parameter
sensitivity analysis developed for discrete dynamic systems, we
determine the critical parameters affecting cellular fate decision
variables that appear to be critical in the cellular fate decision and
discuss how they are exploited by existing cancer therapies.
Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
Unlabelled:
Mathematical modeling is used as a Systems Biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and predict the effect of perturbations. This article presents an algorithm for modeling biological networks in a discrete framework with continuous time.
Background:
There exist two major types of mathematical modeling approaches: (1) quantitative modeling, representing various chemical species concentrations by real numbers, mainly based on differential equations and chemical kinetics formalism; (2) and qualitative modeling, representing chemical species concentrations or activities by a finite set of discrete values. Both approaches answer particular (and often different) biological questions. Qualitative modeling approach permits a simple and less detailed description of the biological systems, efficiently describes stable state identification but remains inconvenient in describing the transient kinetics leading to these states. In this context, time is represented by discrete steps. Quantitative modeling, on the other hand, can describe more accurately the dynamical behavior of biological processes as it follows the evolution of concentration or activities of chemical species as a function of time, but requires an important amount of information on the parameters difficult to find in the literature.
Results:
Here, we propose a modeling framework based on a qualitative approach that is intrinsically continuous in time. The algorithm presented in this article fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution of the biological process we wish to model, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) on the Boolean state space. This software, parallelized and optimized, computes the temporal evolution of probability distributions and estimates stationary distributions.
Conclusions:
Applications of the Boolean Kinetic Monte-Carlo are demonstrated for three qualitative models: a toy model, a published model of p53/Mdm2 interaction and a published model of the mammalian cell cycle. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations.
Despite decades of new discoveries in biomedical research, the overwhelming complexity of cells has been a significant barrier to a fundamental understanding of how cells work as a whole. As such, the holistic study of biochemical pathways requires computer modeling. Due to the complexity of cells, it is not feasible for one person or group to model the cell in its entirety.
The Cell Collective is a platform that allows the world-wide scientific community to create these models collectively. Its interface enables users to build and use models without specifying any mathematical equations or computer code - addressing one of the major hurdles with computational research. In addition, this platform allows scientists to simulate and analyze the models in real-time on the web, including the ability to simulate loss/gain of function and test what-if scenarios in real time.
The Cell Collective is a web-based platform that enables laboratory scientists from across the globe to collaboratively build large-scale models of various biological processes, and simulate/analyze them in real time. In this manuscript, we show examples of its application to a large-scale model of signal transduction.
Protein synthetic lethal genetic interactions are useful to define functional relationships between proteins and pathways. However, the molecular mechanism of synthetic lethal genetic interactions remains unclear.
In this study we used the clusters of short polypeptide sequences, which are typically shorter than the classically defined protein domains, to characterize the functionalities of proteins. We developed a framework to identify significant short polypeptide clusters from yeast protein sequences, and then used these short polypeptide clusters as features to predict yeast synthetic lethal genetic interactions. The short polypeptide clusters based approach provides much higher coverage for predicting yeast synthetic lethal genetic interactions. Evaluation using experimental data sets showed that the short polypeptide clusters based approach is superior to the previous protein domain based one.
We were able to achieve higher performance in yeast synthetic lethal genetic interactions prediction using short polypeptide clusters as features. Our study suggests that the short polypeptide cluster may help better understand the functionalities of proteins.
A genetic interaction network containing�1000 genes and�4000 interactions
was mapped by crossing mutations in 132 different query genes into a set of
�4700 viable gene yeast deletion mutants and scoring the double mutant
progeny for fitness defects.Network connectivity was predictive of function
because interactions often occurred among functionally related genes, and
similar patterns of interactions tended to identify components of the same
pathway.The genetic network exhibited dense local neighborhoods; therefore,
the position of a gene on a partially mapped network is predictive of other
genetic interactions.Because digenic interactions are common in yeast, similar
networks may underlie the complex genetics associated with inherited phenotypes
in other organisms.
An epistatic interaction between two genes occurs when the phenotypic impact of one gene depends on another gene, often exposing a functional association between them. Due to experimental scalability and to evolutionary significance, abundant work has been focused on studying how epistasis affects cellular growth rate, most notably in yeast. However, epistasis likely influences many different phenotypes, affecting our capacity to understand cellular functions, biochemical networks adaptation, and genetic diseases. Despite its broad significance, the extent and nature of epistasis relative to different phenotypes remain fundamentally unexplored. Here we use genome-scale metabolic network modeling to investigate the extent and properties of epistatic interactions relative to multiple phenotypes. Specifically, using an experimentally refined stoichiometric model for Saccharomyces cerevisiae, we computed a three-dimensional matrix of epistatic interactions between any two enzyme gene deletions, with respect to all metabolic flux phenotypes. We found that the total number of epistatic interactions between enzymes increases rapidly as phenotypes are added, plateauing at approximately 80 phenotypes, to an overall connectivity that is roughly 8-fold larger than the one observed relative to growth alone. Looking at interactions across all phenotypes, we found that gene pairs interact incoherently relative to different phenotypes, i.e. antagonistically relative to some phenotypes and synergistically relative to others. Specific deletion-deletion-phenotype triplets can be explained metabolically, suggesting a highly informative role of multi-phenotype epistasis in mapping cellular functions. Finally, we found that genes involved in many interactions across multiple phenotypes are more highly expressed, evolve slower, and tend to be associated with diseases, indicating that the importance of genes is hidden in their total phenotypic impact. Our predictions indicate a pervasiveness of nonlinear effects in how genetic perturbations affect multiple metabolic phenotypes. The approaches and results reported could influence future efforts in understanding metabolic diseases and the role of biochemical regulation in the cell.
Synthetic lethality occurs when the simultaneous perturbation of two genes results in cellular or organismal death. Synthetic lethality also occurs between genes and small molecules, and can be used to elucidate the mechanism of action of drugs. This area has recently attracted attention because of the prospect of a new generation of anti-cancer drugs. Based on studies ranging from yeast to human cells, this review provides an overview of the general principles that underlie synthetic lethality and relates them to its utility for identifying gene function, drug action and cancer therapy. It also identifies the latest strategies for the large-scale mapping of synthetic lethalities in human cells which bring us closer to the generation of comprehensive human genetic interaction maps.
We present several applications of non-linear data modeling, using principal manifolds and principal graphs constructed using the metaphor of elasticity (elastic principal graph approach). These approaches are generalizations of the Kohonen's self-organizing maps, a class of artificial neural networks. On several examples we show advantages of using non-linear objects for data approximation in comparison to the linear ones. We propose four numerical criteria for comparing linear and non-linear mappings of datasets into the spaces of lower dimension. The examples are taken from comparative political science, from analysis of high-throughput data in molecular biology, from analysis of dynamical systems.
Cytokines such as TNF and FASL can trigger death or survival depending on cell lines and cellular conditions. The mechanistic details of how a cell chooses among these cell fates are still unclear. The understanding of these processes is important since they are altered in many diseases, including cancer and AIDS. Using a discrete modelling formalism, we present a mathematical model of cell fate decision recapitulating and integrating the most consistent facts extracted from the literature. This model provides a generic high-level view of the interplays between NFkappaB pro-survival pathway, RIP1-dependent necrosis, and the apoptosis pathway in response to death receptor-mediated signals. Wild type simulations demonstrate robust segregation of cellular responses to receptor engagement. Model simulations recapitulate documented phenotypes of protein knockdowns and enable the prediction of the effects of novel knockdowns. In silico experiments simulate the outcomes following ligand removal at different stages, and suggest experimental approaches to further validate and specialise the model for particular cell types. We also propose a reduced conceptual model implementing the logic of the decision process. This analysis gives specific predictions regarding cross-talks between the three pathways, as well as the transient role of RIP1 protein in necrosis, and confirms the phenotypes of novel perturbations. Our wild type and mutant simulations provide novel insights to restore apoptosis in defective cells. The model analysis expands our understanding of how cell fate decision is made. Moreover, our current model can be used to assess contradictory or controversial data from the literature. Ultimately, it constitutes a valuable reasoning tool to delineate novel experiments.
A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions,
generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological
processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene
function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram
of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative
about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape
provides a key for interpretation of chemical-genetic interactions and drug target identification.
Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
Ever since the integration of Mendelian genetics into evolutionary biology in the early 20th century, evolutionary geneticists have for the most part treated genes and mutations as generic entities. However, recent observations indicate that all genes are not equal in the eyes of evolution. Evolutionarily relevant mutations tend to accumulate in hotspot genes and at specific positions within genes. Genetic evolution is constrained by gene function, the structure of genetic networks, and population biology. The genetic basis of evolution may be predictable to some extent, and further understanding of this predictability requires incorporation of the specific functions and characteristics of genes into evolutionary theory.
Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods.
By applying random walks on biological networks, we were able to predict synthetic lethal interactions at a true positive rate of 95 percent against a false positive rate of 10 percent in S. cerevisiae. Similarly, in C. elegans, we achieved a true positive rate of 95 against a false positive rate of 7 percent. Furthermore, we demonstrate that the inclusion of non-interacting gene pairs results in a considerable performance improvement.
We presented a method based on random walks that accurately captures aspects of network topology towards the goal of classifying potential genetic interactions as either synthetic lethal or non-interacting. Our method, which is generalizable to all types of biological networks, is likely to perform well with limited information, as estimated by holding out large portions of the synthetic lethal interactions and non-interactions.
Genetic interactions define overlapping functions and compensatory pathways. In particular, synthetic sick or lethal (SSL) genetic interactions are important for understanding how an organism tolerates random mutation, i.e., genetic robustness. Comprehensive identification of SSL relationships remains far from complete in any organism, because mapping these networks is highly labor intensive. The ability to predict SSL interactions, however, could efficiently guide further SSL discovery. Toward this end, we predicted pairs of SSL genes in Saccharomyces cerevisiae by using probabilistic decision trees to integrate multiple types of data, including localization, mRNA expression, physical interaction, protein function, and characteristics of network topology. Experimental evidence demonstrated the reliability of this strategy, which, when extended to human SSL interactions, may prove valuable in discovering drug targets for cancer therapy and in identifying genes responsible for multigenic diseases.
Epistatic interactions, manifested in the effects of mutations on the phenotypes caused by other mutations, may help uncover the functional organization of complex biological networks. Here, we studied system-level epistatic interactions by computing growth phenotypes of all single and double knockouts of 890 metabolic genes in Saccharomyces cerevisiae, using the framework of flux balance analysis. A new scale for epistasis identified a distinctive trimodal distribution of these epistatic effects, allowing gene pairs to be classified as buffering, aggravating or noninteracting. We found that the ensuing epistatic interaction network could be organized hierarchically into function-enriched modules that interact with each other 'monochromatically' (i.e., with purely aggravating or purely buffering epistatic links). This property extends the concept of epistasis from single genes to functional units and provides a new definition of biological modularity, which emphasizes interactions between, rather than within, functional modules. Our approach can be used to infer functional gene modules from purely phenotypic epistasis measurements.
We have generalized the derivation of genetic-interaction networks from quantitative phenotype data. Familiar and unfamiliar modes of genetic interaction were identified and defined. A network was derived from agar-invasion phenotypes of mutant yeast. Mutations showed specific modes of genetic interaction with specific biological processes. Mutations formed cliques of significant mutual information in their large-scale patterns of genetic interaction. These local and global interaction patterns reflect the effects of gene perturbations on biological processes and pathways.
To understand the behaviour of complex biological regulatory networks, a proper integration of molecular data into a full-fledge formal dynamical model is ultimately required. As most available data on regulatory interactions are qualitative, logical modelling offers an interesting framework to delineate the main dynamical properties of the underlying networks.
Transposing a generic model of the core network controlling the mammalian cell cycle into the logical framework, we compare different strategies to explore its dynamical properties. In particular, we assess the respective advantages and limits of synchronous versus asynchronous updating assumptions to delineate the asymptotical behaviour of regulatory networks. Furthermore, we propose several intermediate strategies to optimize the computation of asymptotical properties depending on available knowledge.
The mammalian cell cycle model is available in a dedicated XML format (GINML) on our website, along with our logical simulation software GINsim (http://gin.univ-mrs.fr/GINsim). Higher resolution state transitions graphs are also found on this web site (Model Repository page).
Sometimes mutations in two genes produce a phenotype that is surprising in light of each mutation's individual effects. This phenomenon, which defines genetic interaction, can reveal functional relationships between genes and pathways. For example, double mutants with surprisingly slow growth define synergistic interactions that can identify compensatory pathways or protein complexes. Recent studies have used four mathematically distinct definitions of genetic interaction (here termed Product, Additive, Log, and Min). Whether this choice holds practical consequences has not been clear, because the definitions yield identical results under some conditions. Here, we show that the choice among alternative definitions can have profound consequences. Although 52% of known synergistic genetic interactions in Saccharomyces cerevisiae were inferred according to the Min definition, we find that both Product and Log definitions (shown here to be practically equivalent) are better than Min for identifying functional relationships. Additionally, we show that the Additive and Log definitions, each commonly used in population genetics, lead to differing conclusions related to the selective advantages of sexual reproduction.
• epistasis
• fitness
• gene function
Cancer is a complex and heterogeneous disease that exhibits high levels of robustness against various therapeutic interventions. It is a constellation of diverse and evolving disorders that are manifested by the uncontrolled proliferation of cells that may eventually lead to fatal dysfunction of the host system. Although some of the cancer subtypes can be cured by early diagnosis and specific treatment, no effective treatment is yet established for a significant portion of cancer subtypes.
In industrial countries where the average life expectancy is high, cancer is one of the major causes of death. Any contribution to an in-depth understanding of cancer shall eventually lead to better care and treatment for patients. Due to the complex, heterogeneous, and evolving nature of cancer, it is essential for a system-oriented view to be adopted for an in-depth understanding. The question is how to achieve an in-depth yet realistic understanding of cancer dynamics. Although large-scale experiments are now being deployed, there are practical limitations of how much they do to convey the reality of cancer pathology and progression within the patient’s body. Computational approaches with system-oriented thinking may complement the limitations of an experimental approach. Computational studies not only provide us with new insights from large-scale experimental data, but also enable us to perceive what are the conceivable characteristics of cancer under certain assumptions. It is an engine of thoughts and proving grounds of various hypotheses on how cancer may behave as well as how molecular mechanisms work within anomalous conditions. It is not just computing that helps us fight against cancer, but a computational approach has to be combined with a proper theoretical framework that enables us to perceive “cancer” as complex dynamical and evolvable systems that entail a robust yet fragile nature. This recognition shifts our attention from the magic bullet approach of anti-cancer drugs to a more systematic control of cancer as complex dynamical phenomena. This leads to the view that a complex system has to be controlled by complex interventions. To understand such a system and design complex interventions, it is essential that we combine experimental and computational approaches. Thus, computational systems biology of cancer is an essential discipline for cancer biology and is expected to have major impacts for clinical decision-making.
This is the first book specifically focused on computational systems biology of cancer with a coherent and proper vision on how to tackle this formidable challenge.
Book web-site:http://www.cancer-systems-biology.net/
The most commonly used therapies for cancer involve delivering high doses of radiation or toxic chemicals to the patient that also cause substantial damage to normal tissue. To overcome this, researchers have recently resorted to a basic biological concept called 'synthetic lethality' (SL) that takes advantage of interactions between gene pairs. The identification of SL interactions is of considerable therapeutic interest because if a particular gene is SL with a tumor-causing mutation, then the targeting that gene carries therapeutic advantages. Mapping these interactions in the context of human cancer cells could hold the key to effective, targeted cancer treatments. In this review, we cover the recent advances that aim to identify these SL interactions using unbiased genetic screens.
Copyright © 2014 Elsevier Ltd. All rights reserved.
ViDaExpert is a tool for visualization and analysis of multidimensional vectorial data. ViDaExpert is able to work with data tables of "object-feature" type that might contain numerical feature values as well as textual labels for rows (objects) and columns (features). ViDaExpert implements several statistical methods such as standard and weighted Principal Component Analysis (PCA) and the method of elastic maps (non-linear version of PCA), Linear Discriminant Analysis (LDA), multilinear regression, K-Means clustering, a variant of decision tree construction algorithm. Equipped with several user-friendly dialogs for configuring data point representations (size, shape, color) and fast 3D viewer, ViDaExpert is a handy tool allowing to construct an interactive 3D-scene representing a table of data in multidimensional space and perform its quick and insightfull statistical analysis, from basic to advanced methods.
Elucidating the first principles of synthetic lethality in cancer, including biological context, will assist clinical translation.
The Biological Network Manager (BiNoM) is a software tool for the manipulation and analysis of biological networks. It facilitates the import and conversion of a set of well-established systems biology file formats. It also provides a large set of graph-based algorithms that allow users to analyze and extract relevant subnetworks from large molecular maps. It has been successfully used in several projects related to the analysis of large and complex biological data, or networks from databases. In this tutorial, we present a detailed and practical case study of how to use BiNoM to analyze biological networks.
Many important problems in cell biology require the consideration of dense nonlinear interactions between functional modules. The requirement of computer simulation for the understanding of cellular processes is now widely accepted, and a variety of modelling frameworks have been designed to meet this need. Here, we present a novel public release of the Gene Interaction Network simulation suite (GINsim), a software designed for the qualitative modelling and analysis of regulatory networks. The main functionalities of GINsim are illustrated through the analysis of a logical model for the core network controlling the fission yeast cell cycle. The last public release of GINsim (version 2.3), as well as development versions, can be downloaded from the dedicated website (http://gin.univ-mrs.fr/GINsim/), which further includes a model library, along with detailed tutorial and user manual.
Tumor development is characterized by a compromised balance between cell life and death decision mechanisms, which are tightly regulated in normal cells. Understanding this process provides insights for developing new treatments for fighting with cancer. We present a study of a mathematical model describing cellular choice between survival and two alternative cell death modalities: apoptosis and necrosis. The model is implemented in discrete modeling formalism and allows to predict probabilities of having a particular cellular phenotype in response to engagement of cell death receptors. Using an original parameter sensitivity analysis developed for discrete dynamic systems, we determine variables that appear to be critical in the cellular fate decision and discuss how they are exploited by existing cancer therapies.
Over the last few years, main effect genetic association analysis has proven to be a successful tool to unravel genetic risk
components to a variety of complex diseases. In the quest for disease susceptibility factors and the search for the ‘missing
heritability’, supplementary and complementary efforts have been undertaken. These include the inclusion of several genetic
inheritance assumptions in model development, the consideration of different sources of information, and the acknowledgement
of disease underlying pathways of networks. The search for epistasis or gene–gene interaction effects on traits of interest
is marked by an exponential growth, not only in terms of methodological development, but also in terms of practical applications,
translation of statistical epistasis to biological epistasis and integration of omics information sources. The current popularity
of the field, as well as its attraction to interdisciplinary teams, each making valuable contributions with sometimes rather
unique viewpoints, renders it impossible to give an exhaustive review of to-date available approaches for epistasis screening.
The purpose of this work is to give a perspective view on a selection of currently active analysis strategies and concerns
in the context of epistasis detection, and to provide an eye to the future of gene–gene interaction analysis.
Synthetic enhancement, in which one mutation exacerbates the severity of another, is a genetic interaction of increasing importance in biology. This review focuses on the logic of interpreting synthetic enhancement, and begins by comparing the phenomenon with genetic suppression.
Inhibition of protein synthesis by cycloheximide blocks subsequent division of a mammalian cell, but only if the cell is exposed to the drug before the "restriction point" (i.e. within the first several hours after birth). If exposed to cycloheximide after the restriction point, a cell proceeds with DNA synthesis, mitosis and cell division and halts in the next cell cycle. If cycloheximide is later removed from the culture medium, treated cells will return to the division cycle, showing a complex pattern of division times post-treatment, as first measured by Zetterberg and colleagues. We simulate these physiological responses of mammalian cells to transient inhibition of growth, using a set of nonlinear differential equations based on a realistic model of the molecular events underlying progression through the cell cycle. The model relies on our earlier work on the regulation of cyclin-dependent protein kinases during the cell division cycle of yeast. The yeast model is supplemented with equations describing the effects of retinoblastoma protein on cell growth and the synthesis of cyclins A and E, and with a primitive representation of the signaling pathway that controls synthesis of cyclin D.
Previously, we demonstrated the establishment of synthetic lethality screening in cultured somatic human cells, or mouse embryo fibroblasts (MEFs), for chemicals or mutant genes synergistically lethal with a mutated gene of interest. Here, we show in MEFs that the usage of RNA interference-based genetic suppressor elements encoding short hairpin RNAs (shRNAs) enables for genetic synthetic lethality screening at a frequency much higher than that achieved before with short truncated sense and antisense RNAs. These findings open up the possibility of using in mammalian cells genome-wide shRNA libraries for genetic synthetic lethality screening at the multi-gene level.
Two genes are synthetic lethal if mutation of either alone is compatible with viability but mutation of both leads to death. So, targeting a gene that is synthetic lethal to a cancer-relevant mutation should kill only cancer cells and spare normal cells. Synthetic lethality therefore provides a conceptual framework for the development of cancer-specific cytotoxic agents. This paradigm has not been exploited in the past because there were no robust methods for systematically identifying synthetic lethal genes. This is changing as a result of the increased availability of chemical and genetic tools for perturbing gene function in somatic cells.
Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and
analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available
at http://www.thebiogrid.org. BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full
or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts
of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction
attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.
To obtain a global view of functional interactions among genes in a metazoan genome, we computationally integrated interactome data, gene expression data, phenotype data, and functional annotation data from three model organisms-Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster-and predicted genome-wide genetic interactions in C. elegans. The resulting genetic interaction network (consisting of 18,183 interactions) provides a framework for system-level understanding of gene functions. We experimentally tested the predicted interactions for two human disease-related genes and identified 14 new modifiers.
Systematic mapping of genetic interactions for Caenorhabditis elegans genes involved in signaling pathways implicated in human disease reveals a network of 350 interactions. The topology of this network resembles that mapped previously in yeast, reinforcing the idea that similar networks may underlie the genetic basis of complex human disease.
The development and application of genetic tools and resources has enabled a partial genetic-interaction network for the yeast Saccharomyces cerevisiae to be compiled. Analysis of the network, which is ongoing, has already provided a clear picture of the nature and scale of the genetic interactions that robustly sustain biological systems, and how cellular buffering is achieved at the molecular level. Recent studies in yeast have begun to define general principles of genetic networks, and also pave the way for similar studies in metazoan model systems. A comparative understanding of genetic-interaction networks promises insights into some long-standing genetic problems, such as the nature of quantitative traits and the basis of complex inherited disease.
BiNoM (Biological Network Manager) is a new bioinformatics software that significantly facilitates the usage and the analysis
of biological networks in standard systems biology formats (SBML, SBGN, BioPAX). BiNoM implements a full-featured BioPAX editor
and a method of ‘interfaces’ for accessing BioPAX content. BiNoM is able to work with huge BioPAX files such as whole pathway
databases. In addition, BiNoM allows the analysis of networks created with CellDesigner software and their conversion into
BioPAX format. BiNoM comes as a library and as a Cytoscape plugin which adds a rich set of operations to Cytoscape such as
path and cycle analysis, clustering sub-networks, decomposition of network into modules, clipboard operations and others.
Availability: Last version of BiNoM distributed under the LGPL licence together with documentation, source code and API are available at
http://bioinfo.curie.fr/projects/binom
Contact: andrei.zinovyev{at}curie.fr
The correlations between relatives on the supposition of mendelian inheritance Method of elastic maps and its applications in data visualization and data modeling
- R Fisher
- A Gorban
- A Zinovyev
Fisher, R. (1918). The correlations between relatives on the
supposition of mendelian inheritance. Trans. Roy. Soc. Edinb.,
52, 399433.
REFERENCES
REFERENCES
16 Gorban, A. and Zinovyev, A. (2001). Method of elastic
maps and its applications in data visualization and data modeling. International Journal of Computing Anticipatory Systems, pages 353–369.
Computational Systems Biology of Cancer. Chapman & Hall
- E Barillot
- L Calzone
- P Hupe
- J.-P Vert
- A Zinovyev
Barillot, E., Calzone, L., Hupe, P., Vert, J.-P., and Zinovyev,
A. (2012). Computational Systems Biology of Cancer. Chapman & Hall, CRC Mathemtical and Computational Biology.
Travelling the world of gene-gene interactions
- K V Steen
Steen, K. V. (2012). Travelling the world of gene-gene interactions. Brief Bioinform, 13(1), 1-19.
- A H Y Tong
- G Lesage
- G D Bader
- H Ding
- H Xu
- X Xin
- J Young
- G F Berriz
- R L Brost
- M Chang
- Y Chen
- X Cheng
- G Chua
- H Friesen
- D S Goldberg
- J Haynes
- C Humphries
- G He
- S Hussein
- L Ke
- N Krogan
- Z Li
- J N Levinson
- H Lu
- P Ménard
- C Munyana
- A B Parsons
- O Ryan
- R Tonikian
- T Roberts
- A.-M Sdicu
- J Shapiro
- B Sheikh
- B Suter
- S L Wong
- L V Zhang
- H Zhu
- C G Burd
- S Munro
- C Sander
- J Rine
- J Greenblatt
- M Peter
- A Bretscher
- G Bell
- F P Roth
- G W Brown
- B Andrews
- H Bussey
- C Boone
Tong, A. H. Y., Lesage, G., Bader, G. D., Ding, H., Xu,
H., Xin, X., Young, J., Berriz, G. F., Brost, R. L., Chang, M.,
Chen, Y., Cheng, X., Chua, G., Friesen, H., Goldberg, D. S.,
Haynes, J., Humphries, C., He, G., Hussein, S., Ke, L., Krogan, N., Li, Z., Levinson, J. N., Lu, H., Ménard, P., Munyana,
C., Parsons, A. B., Ryan, O., Tonikian, R., Roberts, T., Sdicu,
A.-M., Shapiro, J., Sheikh, B., Suter, B., Wong, S. L., Zhang,
L. V., Zhu, H., Burd, C. G., Munro, S., Sander, C., Rine,
J., Greenblatt, J., Peter, M., Bretscher, A., Bell, G., Roth,
F. P., Brown, G. W., Andrews, B., Bussey, H., and Boone,
C. (2004). Global mapping of the yeast genetic interaction
network. Science, 303(5659), 808-813.