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Data-driven Optimizations for Model Checking of Multi-valued Regulatory Networks
Abstract
The model checking method has been long since established as an important tool for modelling and reverse engineering of biological systems. However, due to a high complexity of both the method and the biological systems, this approach often requires a vast amount of computational resources. In this article we show that by reducing the expressivity of the method we can gain performance while still being able to use all biologically relevant data. We utilize this approach to conduct a study of mutations in the EGFR signalling, motivated by a paper from Klinger et al. (2013). Here we aim at constructing approximated models of multiple cell-lines from sizeable sets of experimental data. Due to cancerous mutations in each cell line, there is a high degree of parameter uncertainty and the study would not be practically tractable without the performance optimizations described here.
Supplementary resource (1)
... These effects cause changes in the regulatory network leading to an insensitivity of the component from its regulators, e.g., constantly active receptors. We aim to identify these changes in the regulation of a component in our approach, which we were able to confirm in previous work (Streck et al., 2016). ...
... As a result, we receive one or more specific model pools that need to be analyzed. For this aim, different kinds of analysis tools can be employed depending on the aim and the size of the resulting model pool such as statistical analysis (Thobe et al., 2014;Streck et al., 2016) or optimization (Terfve et al., 2012;Videla et al., 2017). Here, we want to present an analysis approach that allows a closer look at classes of models as well as single models. ...
... Tools like caspo list running times of approximately 56 mins for models of size 45 generating a model pool with 384 models and thus can still handle medium sized models (Videla et al., 2017). The software Tremppi was shown to be able to handle a model pool of size 259,200 and perform model-checking of 40 data sets within 151-177 min depending on parameter settings on a similar workstation (Streck et al., 2016). In general, the kind of models that are feasible for this approach are a trade-off between number of components and number of uncertain edges, the latter of which affects the size of the model pool. ...
Systems biology studies the structure and dynamics of biological systems using mathematical approaches. Bottom-up approaches create models from prior knowledge but usually cannot cope with uncertainty, whereas top-down approaches infer models directly from data using statistical methods but mostly neglect valuable known information from former studies. Here, we want to present a workflow that includes prior knowledge while allowing for uncertainty in the modeling process. We build not one but all possible models that arise from the uncertainty using logical modeling and subsequently filter for those models in agreement with data in a top-down manner. This approach enables us to investigate new and more complex biological research questions, however, the encoding in such a framework is often not obvious and thus not easily accessible for researcher from life sciences. To mitigate this problem, we formulate a pipeline with specific templates to address some research questions common in signaling network analysis. To illustrate the potential of this approach, we applied the pipeline to growth factor signaling processes in two renal cancer cell lines. These two cell lines originate from similar tissue, but surprisingly showed a very different behavior toward the cancer drug Sorafenib. Thus our aim was to explore differences between these cell lines regarding three sources of uncertainty in one analysis: possible targets of Sorafenib, crosstalk between involved pathways, and the effect of a mutation in mammalian target of Rapamycin (mTOR) in one of the cell lines. We were able to show that the model pools from the cell lines are disjoint, thus the discrepancies in behavior originate from differences in the cellular wiring. Also the mutation in mTOR is not affecting its activity in the pathway. The results on Sorafenib, while not fully clarifying the mechanisms involved, illustrate the potential of this analysis for generating new hypotheses.
... The first application of the toolbox is published work that was done in cooperation with Adam Streck [92,93]. In the paper, the focus was less on the workflow, but more on testing and comparing the performance of an improved version of Tremppi on a large data set [91]. ...
... This section presents a case study published with Adam Streck and Heike Siebert as conference and journal papers [92,93]. In these papers, Tremppi and its implemented methods to effectively study biological systems were demonstrated. ...
... But, it is not clear how scalable their proposed method is and whether different state transition update schemes such as synchronous and asynchronous are accommodated in their framework. There are already various tools developed for logical analysis of biological networks, such as Caspo (Guziolowski et al., 2013), TREMPPI (Streck et al., 2016), GINsim (Chaouiya et al., 2003), and Bio Model Analyzer (BMA) (Benque et al., 2012). Caspo employs ASP in order to parameterize the regulatory networks. ...
The in silico study and reverse engineering of regulatory networks has gained in recognition as an insightful tool for the qualitative study of biological mechanisms that underlie a broad range of complex illness. In the creation of reliable network models, the integration of prior mechanistic knowledge with experimentally observed behavior is hampered by the disparate nature and widespread sparsity of such measurements. The former challenges conventional regression-based parameter fitting while the latter leads to large sets of highly variable network models that are equally compliant with the data. In this paper, we propose a bounded Constraint Satisfaction (CS) based model checking framework for parameter set identification that readily accommodates partial records and the exponential complexity of this problem. We introduce specific criteria to describe the biological plausibility of competing multi-valued regulatory networks that satisfy all the constraints and formulate model identification as a multi-objective optimization problem. Optimization is directed at maximizing structural parsimony of the regulatory network by mitigating excessive control action selectivity while also favoring increased state transition efficiency and robustness of the network's dynamic response. The framework's scalability, computational time and validity is demonstrated on several well-established and well-studied biological networks.
In the face of incomplete data on a system of interest, constraint-based Boolean modeling still allows for elucidating system characteristics by analyzing sets of models consistent with the available information. In this setting, methods not depending on consideration of every single model in the set are necessary for efficient analysis. Drawing from ideas developed in qualitative differential equation theory, we present an approach to analyze sets of monotonic Boolean models consistent with given signed interactions between systems components. We show that for each such model constraints on its behavior can be derived from a universally constructed state transition graph essentially capturing possible sign changes of the derivative. Reachability results of the modeled system, e.g., concerning trap or no-return sets, can then be derived without enumerating and analyzing all models in the set. The close correspondence of the graph to similar objects for differential equations furthermore opens up ways to relate Boolean and continuous models.
Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however , is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space.
Gene expression measurements represent the most important source of biological data used to unveil the interaction and functionality of genes. In this regard, several data mining and machine learning algorithms have been proposed that require, in a number of cases, some kind of data discretization to perform the inference. Selection of an appropriate discretization process has a major impact on the design and outcome of the inference algorithms, as there are a number of relevant issues that need to be considered. This study presents a revision of the current state-of-the-art discretization techniques, together with the key subjects that need to be considered when designing or selecting a discretization approach for gene expression data.
Multi-valued logical models can be used to describe biological networks on a
high level of abstraction based on the network structure and logical parameters
capturing regulatory effects. Interestingly, the dynamics of two distinct
models need not necessarily be different, which might hint at either only
non-functional characteristics distinguishing the models or at different
possible implementations for the same behaviour. Here, we study the conditions
allowing for such effects by analysing classes of dynamically equivalent models
and both structurally maximal and minimal representatives of such classes.
Finally, we present an efficient algorithm that constructs a minimal
representative of the respective class of a given multi-valued model.
The epidermal growth factor receptor (EGFR) signaling network is activated in most solid tumors, and small-molecule drugs targeting this network are increasingly available. However, often only specific combinations of inhibitors are effective. Therefore, the prediction of potent combinatorial treatments is a major challenge in targeted cancer therapy. In this study, we demonstrate how a model-based evaluation of signaling data can assist in finding the most suitable treatment combination. We generated a perturbation data set by monitoring the response of RAS/PI3K signaling to combined stimulations and inhibitions in a panel of colorectal cancer cell lines, which we analyzed using mathematical models. We detected that a negative feedback involving EGFR mediates strong cross talk from ERK to AKT. Consequently, when inhibiting MAPK, AKT activity is increased in an EGFR-dependent manner. Using the model, we predict that in contrast to single inhibition, combined inactivation of MEK and EGFR could inactivate both endpoints of RAS, ERK and AKT. We further could demonstrate that this combination blocked cell growth in BRAF- as well as KRAS-mutated tumor cells, which we confirmed using a xenograft model.
The hierarchy of properties as overviewed by Manna and Pnueli [18] relates language, topology, ω-automata, and linear temporal logic classifications of properties. We provide new characterisations of this hierarchy in
terms of automata with Büchi, co-Büchi, and Streett acceptance condition and in terms of SLTLi\Sigma^\mathit{LTL}_i and PLTLi\Pi^\mathit{LTL}_i hierarchies. Afterwards, we analyse the complexity of the model checking problem for particular classes of the hierarchy
and thanks to the new characterisations we identify those linear time temporal properties for which the model checking problem
can be solved more efficiently than in the general case.
Insulin receptor substrates (IRS) serine phosphorylation is a time-controlled physiological feedback mechanism in insulin signaling that is hijacked by metabolic and inflammatory stresses to promote insulin resistance. Kinases, including IKKbeta, JNK, ERK, mTOR, and S6K, activated by the inducers of insulin resistance induce uncontrolled IRS serine phosphorylation. Studies with genetically modified mice reveal that these kinases integrate signals from metabolic and inflammatory stresses in adipose tissue, liver, and hypothalamus leading to peripheral and central insulin resistance. Moreover, IKKbeta/NF-kappaB and JNK1 pathways in myeloid cells represent a core mechanism involved in inflammation linked to obesity. These kinases are thus potential drug targets against insulin resistance and the targeting of the IKKbeta/NF-kappaB or the JNK pathway may evolve into future diabetes medication.
Although increasing evidence supports a link between epidermal growth factor receptor (EGFR) signaling and resistance to apoptosis, the mechanism by which the EGFR signaling pathway inhibits apoptosis is not well understood. In this study, we found that epidermal growth factor (EGF) stimulation increased the level of expression of the inhibitor of apoptosis protein survivin in breast cancer cells but not in normal mammary epithelial cells. We further demonstrated that activation of survivin gene expression is mediated by oxygen-independent hypoxia-inducible factor (HIF)-1alpha up-regulation in EGF-treated cancer cells. EGFR signaling activated the phosphoinositide 3-kinase/AKT pathway, subsequently increasing the level of HIF-1alpha under normoxic conditions. HIF-1alpha then activated survivin gene transcription through direct binding to the survivin promoter. Furthermore, we found that overexpression of HIF-1alpha small interfering RNA blocks EGF-induced survivin gene up-regulation and increases apoptosis induced by the chemotherapy drug docetaxel. However, transfection of a plasmid expressing HIF-1alpha gene activates survivin gene expression and reduces the apoptotic response. Our results demonstrate a novel pathway for EGFR signaling-mediated apoptosis resistance in human cancer cells. Although the role of HIF-1alpha in regulating cell survival under hypoxic conditions has been studied extensively, our results show that normoxic breast cancer cells utilize cross-talk between EGFR signals and HIF-1alpha to up-regulate the anti-apoptotic survivin gene, providing a strong rationale for the targeting of HIF-1alpha as a therapeutic approach for both hypoxic and normoxic tumor cells. Understanding key molecular events in EGFR signaling-induced apoptosis resistance should provide new information for the development of novel therapeutic agents targeting EGFR, HIF-1alpha, and/or survivin.
ion. ACM Transactions on Programming Languages and Systems, 16(5):1512--1542, September 1994. Bibliography 401 [Che80] B. F. Chellas. Modal Logic -- an Introduction. Cambridge University Press, 1980. [Dam96] D. R. Dams. Abstract Interpretation and Partition Refinement for Model Checking. PhD thesis, Institute for Programming research and Algorithmics. Eindhoven University of Technology, July 1996. [Dij76] E. W. Dijkstra. A Discipline of Programming. Prentice Hall, 1976. [DP96] R. Davies and F. Pfenning. A Modal Analysis of Staged Computation. In 23rd Annual ACM Symposium on Principles of Programming Languages. ACM Press, January 1996. [EN94] R. Elmasri and S. B. Navathe. Fundamentals of Database Systems. Benjamin/Cummings, 1994. [FHMV95] Ronald Fagin, Joseph Y. Halpern, Yoram Moses, and Moshe Y. Vardi. Reasoning about Knowledge. MIT Press, Cambridge, 1995. [Fit93] M. Fitting. Basic modal logic. In D. Gabbay, C. Hogger, and J. Robinson, editors, Handbook of Logic in Artificial In...
We provide the first classification of different types of RandomBoolean Networks (RBNs). We study the differences of RBNs depending on the degree of synchronicity and determinism of their updating scheme. For doing so, we first define three new types of RBNs. We note some similarities and differences between different types of RBNs with the aid of a public software laboratory we developed. Particularly, we find that the point attractors are independent of the updating scheme, and that RBNs are more different depending on their determinism or non-determinism rather than depending on their synchronicity or asynchronicity. We also show a way of mapping non-synchronous deterministic RBNs into synchronous RBNs. Our results are important for justifying the use of specific types of RBNs for modelling natural phenomena.
Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space.
cell biology, microbiology and biomedical science to understand the key concepts to expanding their work. Chapters organized around broader themes of Organelles and Organisms, Systems Properties of Biological Processes, Cellular Networks, and Systems Biology and Disease discuss the development of concepts, the current applications, and the future prospects. Emphasis is placed on concepts and insights into the multi-disciplinary nature of the field as well as the importance of systems biology in human biological research. Technology, being an extremely important aspect of scientific progress overall, and in the creation of new fields in particular, is discussed in 'boxes' within each chapter to relate to appropriate topics. Emphasizes the interdisciplinary nature of systems biology with contributions from leaders in a variety of disciplines Includes the latest research developments in human and animal models to assist with translational research Presents biological and computational aspects of the science side-by-side to facilitate collaboration between computational and biological researchers.
The hierarchy of properties as overviewed by Manna and Pnueli [18] relates language, topology, ω-automata, and linear temporal logic classifications of properties. We provide new characterisations of this hierarchy in terms of automata with Büchi, co-Büchi, and Streett acceptance condition and in terms of ΣiLTL and ΠiLTL hierarchies. Afterwards, we analyse the complexity of the model checking problem for particular classes of the hierarchy and thanks to the new characterisations we identify those linear time temporal properties for which the model checking problem can be solved more efficiently than in the general case.
Building models with a high degree of specificity, e.g. for particular cell lines, is becoming an important tool in the advancement towards personalised medicine. Constraint-based modelling approaches allow for utilizing general system knowledge to generate a set of possible models that can be further filtered with more specific data. Here, we exploit such an approach in a Boolean modelling framework to investigate EGFR signalling for different cancer cell lines, motivated by a study from Klinger et al. [8]. To optimize performance of the underlying model checking procedure, we present a number of constraint encodings tailored to describing common data types and experimental set-ups. This results in a significant increase in the performance of the approach.
Methods for model integration have become increasingly popular for understanding of the interplay between biological processes. In this work, we introduce an approach for coupling models taking uncertainties concerning the crosstalk into account. Using constraintbased modeling and formal verification techniques, a pool of possible integrated models is generated in agreement with previously validated behavior of the isolated models as well as additional experimental observations. Correlation- and causality-based analysis allows us to uncover the importance of particular crosstalk connections for specific functionalities leading to new biological insights and starting points for experimental design. We illustrate our approach studying crosstalk between the MAPK and mTor signaling pathways.
The development of drug resistance by cancer cells is recognized as a major cause for drug failure and disease progression. The PI3K/AKT/mTOR pathway is aberrantly stimulated in many cancer cells and thus it has emerged as a target for therapy. However, mTORC1 and S6K also mediate potent negative feedback loops that attenuate signaling via insulin/insulin growth factor receptor and other tyrosine kinase receptors. Suppression of these feedback loops causes overactivation of upstream pathways, including PI3K, AKT, and ERK that potentially oppose the antiproliferative effects of mTOR inhibitors and lead to drug resistance. A corollary of this concept is that release of negative feedback loops and consequent compensatory overactivation of promitogenic pathways in response to signal inhibitors can circumvent the mitogenic block imposed by targeting only one pathway. Consequently, the elucidation of the negative feedback loops that regulate the outputs of signaling networks has emerged as an area of fundamental importance for the rational design of effective anticancer combinations of inhibitors. Here, we review pathways that undergo compensatory overactivation in response to inhibitors that suppress feedback inhibition of upstream signaling and underscore the importance of unintended pathway activation in the development of drug resistance to clinically relevant inhibitors of mTOR, AKT, PI3K, or PI3K/mTOR. Mol Cancer Ther; 13(11); 1-12. ©2014 AACR.
Model checking together with other formal methods and techniques is being adapted for applications to biological systems. We present a selection of approaches used for modeling biological systems and formalizing their interesting properties in temporal logics. We also give a brief account of high performance model checking techniques and add a few case studies that demonstrate the use of model checking in computational systems biology. The primary aim is to give a reference for further reading.
Proto-organisms probably were randomly aggregated nets of chemical reactions. The hypothesis that contemporary organisms are also randomly constructed molecular automata is examined by modeling the gene as a binary (on-off) device and studying the behavior of large, randomly constructed nets of these binary "genes". The results suggest that, if each "gene" is directly affected by two or three other "genes", then such random nets: behave with great order and stability; undergo behavior cycles whose length predicts cell replication time as a function of the number of genes per cell; possess different modes of behavior whose number per net predicts roughly the number of cell types in an organism as a function of its number of genes; and under the stimulus of noise are capable of differentiating directly from any mode of behavior to at most a few other modes of behavior. Cellular differentation is modeled as a Markov chain among the modes of behavior of a genetic net. The possibility of a general theory of metabolic behavior is suggested.
Mathematical modeling of biological processes provides deep insights into complex cellular systems. While quantitative and continuous models such as differential equations have been widely used, their use is obstructed in systems wherein the knowledge of mechanistic details and kinetic parameters is scarce. On the other hand, a wealth of molecular level qualitative data on individual components and interactions can be obtained from the experimental literature and high-throughput technologies, making qualitative approaches such as Boolean network modeling extremely useful. In this paper, we build on our research to provide a methodology overview of Boolean modeling in systems biology, including Boolean dynamic modeling of cellular networks, attractor analysis of Boolean dynamic models, as well as inferring biological regulatory mechanisms from high-throughput data using Boolean models. We finally demonstrate how Boolean models can be applied to perform the structural analysis of cellular networks. This overview aims to acquaint life science researchers with the basic steps of Boolean modeling and its applications in several areas of systems biology.
This paper is concerned with the analysis of labeled Thomas networks using discrete time series. It focuses on refining the given edge labels and on assessing the data quality. The results are aimed at being exploitable for experimental design and include the prediction of new activatory or inhibitory effects of given interactions and yet unobserved oscillations of specific components in between specific sampling intervals. On the formal side, we generalize the concept of edge labels and introduce a discrete time series interpretation. This interpretation features two original concepts: (1) Incomplete measurements are admissible, and (2) it allows qualitative assumptions about the changes in gene expression by means of monotonicity. On the computational side, we provide a Python script, erda.py, that automates the suggested workflow by model checking and constraint satisfaction. We illustrate the workflow by investigating the yeast network IRMA.
Two papers published in Science Signaling reveal extensive crosstalk between Wnt/β-catenin and mitogen-activated protein kinase (MAPK) signaling in cancer. Although both studies describe previously unknown links between these two signaling pathways, the relationship between Wnt/β-catenin and MAPK signaling depends on the specific cellular context. Indeed, in melanoma, hyperactivated MAPK signaling down-regulates the Wnt/β-catenin signal transduction cascade, thereby establishing a negative crosstalk between the two signaling pathways. In contrast, in colorectal cancer, stimulation of the Wnt/β-catenin pathway leads to activation of the MAPK pathway through Ras stabilization, representing an example of positive crosstalk. Moreover, activation of Wnt/β-catenin signaling has context-dependent functions that trigger opposing effects on tumor growth. In melanoma, aberrant activation of Wnt/β-catenin signaling may have anti-oncogenic functions by promoting programmed cell death; by contrast, in the intestine, Wnt/β-catenin signaling drives malignant transformation. Thus, there is no single correct way to target the Wnt/β-catenin pathway for all cancers.
The aim of this paper is to provide a compact answer to the questions:why treat complex biological systems in logical terms?how can one do it conveniently?Our initial description (Thomas, R. J. theor. Biol. 1973, 42, 563) is what we now call the “naive” logical description. After recalling the essential elements of this asynchronous description, the present paper introduces-the use of logical variables with more than two values-the notion of logical parameters-the logical identification of all steady states of the differential description-the notion of “characteristic” state of feedback loops-a compact matricial presentationThis is an essentially methodological paper. More extended developments including concrete biological examples will be found elsewhere (Thomas & D'Ari, 1990).
Please don't be shy about sending even vague pointers to people who may have complete or partial resolutions of the problems mentioned in any of the open questions columns that have appeared as earlier complexity theory columns. Though I don't give a ...
In response to stress, p53 initiates the transcriptional regulation of selected target genes and various cellular responses, including cell cycle arrest, apoptosis and senescence. Recent studies revealed two additional functions of p53 in the regulation of IGF-1/AKT/mTOR pathways and energy metabolism, contributing to p53's role as a tumor suppressor. Oncogenic processes give rise to metabolic pathways focused upon the use of aerobic glycolysis (the Warburg effect) and the pentose shunt, providing higher levels of reducing activities. p53 shuts down these pathways and refocuses cells to utilize mitochondrial oxidative phosphorylation, thereby maximizing efficient ATP production and minimizing the synthesis of substrates for cell division. The use of these alternative metabolic pathways is an integral part of both normal and oncogenic phenotypes.
Although stochastic population models have proved to be a powerful tool in the study of process generating mechanisms across a wide range of disciplines, all too often the associated mathematical development involves nonlinear mathematics, which immediately raises difficult and challenging analytic problems that need to be solved if useful progress is to be made. One approximation that is often employed to estimate the moments of a stochastic process is moment closure. This approximation essentially truncates the moment equations of the stochastic process. A general expression for the marginal- and joint-moment equations for a large class of stochastic population models is presented. The generalisation of the moment equations allows this approximation to be applied easily to a wide range of models. Software is available from http://pysbml.googlecode.com/ to implement the techniques presented here.
Proto-organisms probably were randomly aggregated nets of chemical reactions. The hypothesis that contemporary organisms are also randomly constructed molecular automata is examined by modeling the gene as a binary (on-off) device and studying the behavior of large, randomly constructed nets of these binary “genes”. The results suggest that, if each “gene” is directly affected by two or three other “genes”, then such random nets: behave with great order and stability; undergo behavior cycles whose length predicts cell replication time as a function of the number of genes per cell; possess different modes of behavior whose number per net predicts roughly the number of cell types in an organism as a function of its number of genes; and under the stimulus of noise are capable of differentiating directly from any mode of behavior to at most a few other modes of behavior. Cellular differentation is modeled as a Markov chain among the modes of behavior of a genetic net. The possibility of a general theory of metabolic behavior is suggested.
In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.
Signaling pathways in mammalian cells are assembled and regulated by a finely controlled network of protein-protein and protein-phospholipid interactions, mediated by dedicated signaling domains and their cognate binding motifs. The domain-based modular architecture of signaling proteins may have facilitated the evolution of complex biological systems, and can be exploited experimentally to generate synthetic signaling pathways and artificial mechanisms of autoregulation. Pathogenic proteins, such as those encoded by bacteria and viruses, frequently form ectopic signaling complexes to respecify cellular behavior. In a similar fashion, proteins expressed as a consequence of oncogenic fusions, mutations or amplifications can elicit ectopic protein-protein interactions that re-wire signaling pathways, in a fashion that promotes malignancy. Compounds that directly or indirectly reverse these aberrant interactions offer new possibilities for therapy in cancer.
EGFR signalling network model and data
- A Streck
A. Streck. EGFR signalling network model and data. http://
dibimath.github.io/BioSystems_2015/, 2015. Accessed: 2015-11-30.
Oncogenic re-wiring of cellular signaling pathways Cross-talk between epidermal growth factor receptor and hypoxiainducible factor-1α signal pathways increases resistance to apoptosis by up-regulating survivin gene expression
- T Pawson
- N Warner Peng
- P Karna
- Z Cao
- B.-H Jiang
- M Zhou
- L Yang
T. Pawson and N. Warner. Oncogenic re-wiring of cellular signaling
pathways. Oncogene, 26(9):1268–1275, 2007.
[17] X.-H. Peng, P. Karna, Z. Cao, B.-H. Jiang, M. Zhou, and L. Yang.
Cross-talk between epidermal growth factor receptor and hypoxiainducible factor-1α signal pathways increases resistance to apoptosis by up-regulating survivin gene expression. Journal of Biological
Chemistry, 281(36):25903–25914, 2006.
Contributions to the Analysis of Qualitative Models of Regulatory Networks
- H Klarner
H. Klarner. Contributions to the Analysis of Qualitative Models of
Regulatory Networks. PhD thesis, Freie Universität Berlin, Germany,
2015.
TREMPPI source repository
- A Streck
A. Streck. TREMPPI source repository. https://github.com/
xstreck1/TREMPPI/, 2015. Accessed: 2015-06-18.