A systems biology view of cancer

In order to understand how a cancer cell is functionally different from a normal cell it is necessary to assess the complex network of pathways involving gene regulation, signaling, and cell metabolism, and the alterations in its dynamics caused by the several different types of mutations leading to malignancy. Since the network is typically complex, with multiple connections between pathways and important feedback loops, it is crucial to represent it in the form of a computational model that can be used for a rigorous analysis. This is the approach of systems biology, made possible by new -omics data generation technologies. The goal of this review is to illustrate this approach and its utility for our understanding of cancer. After a discussion of recent progress using a network-centric approach, three case studies related to diagnostics, therapy, and drug development are presented in detail. They focus on breast cancer, B-cell lymphomas, and colorectal cancer. The discussion is centered on key mathematical and computational tools common to a systems biology approach.     

A survey on methods for modeling and analyzing integrated biological networks

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics" technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.     

Beyond the Oncogene Paradigm: Understanding Complexity in Cancerogenesis

In the past decades, an enormous amount of precious information has been collected about molecular and genetic characteristics of cancer. This knowledge is mainly based on a reductionistic approach, meanwhile cancer is widely recognized to be a 'system biology disease'. The behavior of complex physiological processes cannot be understood simply by knowing how the parts work in isolation. There is not solely a matter how to integrate all available knowledge in such a way that we can still deal with complexity, but we must be aware that a deeply transformation of the currently accepted oncologic paradigm is urgently needed. We have to think in terms of biological networks: understanding of complex functions may in fact be impossible without taking into consideration influences (rules and constraints) outside of the genome. Systems Biology involves connecting experimental unsupervised multivariate data to mathematical and computational approach than can simulate biologic systems for hypothesis testing or that can account for what it is not known from high-throughput data sets. Metabolomics could establish the requested link between genotype and phenotype, providing informations that ensure an integrated understanding of pathogenic mechanisms and metabolic phenotypes and provide a screening tool for new targeted drug.     

Systems biology of virus-host signaling network interactions

Viruses have evolved to manipulate the host cell machinery for virus propagation, in part by interfering with the host cellular signaling network. Molecular studies of individual pathways have uncovered many viral host-protein targets; however, it is difficult to predict how viral perturbations will affect the signaling network as a whole. Systems biology approaches rely on multivariate, context-dependent measurements and computational analysis to elucidate how viral infection alters host cell signaling at a network level. Here we describe recent advances in systems analyses of signaling networks in both viral and non-viral biological contexts. These approaches have the potential to uncover virus- mediated changes to host signaling networks, suggest new therapeutic strategies, and assess how cell-to-cell variability affects host responses to infection. We argue that systems approaches will both improve understanding of how individual virus-host protein interactions fit into the progression of viral pathogenesis and help to identify novel therapeutic targets.     

Modeling cell signaling networks

Cell signaling pathways interact with one another to form networks in mammalian systems. Such networks are complex in their organization and exhibit emergent properties such as bistability and ultrasensitivity. Analysis of signaling networks requires a combination of experimental and theoretical approaches including the development and analysis of models. This review focuses on theoretical approaches to understanding cell signaling networks. Using heterotrimeric G protein pathways an example, we demonstrate how interactions between two pathways can result in a network that contains a positive feedback loop and function as a switch. Different mathematical approaches that are currently used to model signaling networks are described, and future challenges including the need for databases as well as enhanced computing environments are discussed.     

Core signaling pathways of survival/death in autophagy-related cancer networks

Autophagy (macroautophagy), an evolutionarily conserved lysosomal degradation process, is implicated in a wide variety of pathological processes including cancer. Autophagy plays the Janus role in regulating several survival or death signaling pathways that may decide the fate of cancer cell. Accumulating evidence has revealed the core molecular machinery of autophagy in tumor initiation and progression; however, the intricate relationships between autophagy and cancer are still in its infancy. In this review, we summarize several key survival/death pathways such as mTOR subnetwork, Beclin 1 interactome, and p53 signaling that may play the crucial roles for the regulation of the autophagy-related cancer networks. Therefore, a better understanding of the relationships between autophagy and cancer may ultimately allow cancer biologists and clinicians to harness core autophagic pathways for the discovery of potential novel drug targets.     

Meeting report: Signal transduction meets systems biology

ABSTRACT: In the 21st century, systems-wide analyses of biological processes are getting more and more realistic. Especially for the in depth analysis of signal transduction pathways and networks, various approaches of systems biology are now successfully used. The EU FP7 large integrated project SYBILLA (Systems Biology of T-cell Activation in Health and Disease) coordinates such an endeavor. By using a combination of experimental data sets and computational modelling, the consortium strives for gaining a detailed and mechanistic understanding of signal transduction processes that govern T-cell activation. In order to foster the interaction between systems biologists and experimentally working groups, SYBILLA co-organized the 15th meeting "Signal Transduction: Receptors, Mediators and Genes" together with the Signal Transduction Society (STS). Thus, the annual STS conference, held from November 7 to 9, 2011 in Weimar, Germany, provided an interdisciplinary forum for research on signal transduction with a major focus on systems biology addressing signalling events in T-cells. Here we report on a selection of ongoing projects of SYBILLA and how they were discussed at this interdisciplinary conference.     

Translating Biomaterial Properties to Intracellular Signaling

Bioactive materials present important micro-environmental cues that induce specific intracellular signaling responses which ultimately determine cell behavior. For example, vascular endothelial cells on a normal vessel wall resist inflammation and thrombosis, but the same cells seeded on an artificial vascular graft or stent do not. What makes these cells behave so differently when they are adhered to different materials? Intracellular signaling from integrins and other cell-surface receptors is an important part of the answer, but these signaling responses constitute a highly-branched, interconnected network of molecules. In order to perform rational design of biomaterials, one must understand how altering the properties of the material (micro-environment) causes changes in cell behavior, and this in turn requires understanding the complex signaling response. Systems biology and mathematical modeling aid analysis of the connectivity of this network. This review summarizes applicable systems biology and mathematical modeling techniques including ordinary differential equations-based models, principal component analysis, and Bayesian networks. Next covered is biomaterials research which studies the intracellular signaling responses generated by variation of biomaterial properties. Finally, the review details ways in which modeling has been or could be applied to better understand the link between biomaterial properties and intracellular signaling.     

Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology

We present a Systems Biology Toolbox for the widely used general purpose mathematical software MATLAB. The toolbox offers systems biologists an open and extensible environment, in which to explore ideas, prototype and share new algorithms, and build applications for the analysis and simulation of biological and biochemical systems. Additionally it is well suited for educational purposes. The toolbox supports the Systems Biology Markup Language (SBML) by providing an interface for import and export of SBML models. In this way the toolbox connects nicely to other SBML-enabled modelling packages. Models are represented in an internal model format and can be described either by entering ordinary differential equations or, more intuitively, by entering biochemical reaction equations. The toolbox contains a large number of analysis methods, such as deterministic and stochastic simulation, parameter estimation, network identification, parameter sensitivity analysis and bifurcation analysis.     

The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: putting systems biology to work

Between December 8–10, 2016, the International Conference on Intelligent Biology and Medicine (ICIBM 2016) was held in Houston, Texas, USA. The conference included eight scientific sessions, four tutorials, one poster session, four highlighted talks and four keynotes that covered topics in 3D genome structure analysis and visualization, next generation sequencing analysis, computational drug discovery, medical informatics, cancer genomics and systems biology. Systems biology has been a main theme in ICIBM 2016, with exciting advances were presented in many areas of systems biology. Here, we selected seven high quality papers to be published in BMC Systems Biology.     

Using views of Systems Biology Cloud: application for model building

A large and growing network (“cloud”) of interlinked terms and records of items of Systems Biology knowledge is available from the web. These items include pathways, reactions, substances, literature references, organisms, and anatomy, all described in different data sets. Here, we discuss how the knowledge from the cloud can be molded into representations (views) useful for data visualization and modeling. We discuss methods to create and use various views relevant for visualization, modeling, and model annotations, while hiding irrelevant details without unacceptable loss or distortion. We show that views are compatible with understanding substances and processes as sets of microscopic compounds and events respectively, which allows the representation of specializations and generalizations as subsets and supersets respectively. We explain how these methods can be implemented based on the bridging ontology Systems Biological Pathway Exchange (SBPAX) in the Systems Biology Linker (SyBiL) we have developed.     

Feedback dynamics and cell function: Why systems biology is called Systems Biology

A new paradigm, like Systems Biology, should challenge the way research has been conducted previously. This Opinion article aims to present Systems Biology, not as the application of engineering principles to biology but as a merger of systems- and control theory with molecular- and cell biology. In our view, the central dogma of Systems Biology is that it is system dynamics that gives rise to the functioning and function of cells. The concepts of feedback regulation and control of pathways and the coordination of cell function are emphasized as an important area of Systems Biology research. The hurdles and risks for this area are discussed from the perspective of dynamic pathway modelling. Most of all, the aim of this article is to promote mathematical modelling and simulation as a part of molecular- and cell biology. Systems Biology is a success if it is widely accepted that there is nothing more practical than a good theory.     

Systems biology towards life in silico: mathematics of the control of living cells

Systems Biology is the science that aims to understand how biological function absent from macromolecules in isolation, arises when they are components of their system. Dedicated to the memory of Reinhart Heinrich, this paper discusses the origin and evolution of the new part of systems biology that relates to metabolic and signal-transduction pathways and extends mathematical biology so as to address postgenomic experimental reality. Various approaches to modeling the dynamics generated by metabolic and signal-transduction pathways are compared. The silicon cell approach aims to describe the intracellular network of interest precisely, by numerically integrating the precise rate equations that characterize the ways macromolecules’ interact with each other. The non-equilibrium thermodynamic or ‘lin–log’ approach approximates the enzyme rate equations in terms of linear functions of the logarithms of the concentrations. Biochemical Systems Analysis approximates in terms of power laws. Importantly all these approaches link system behavior to molecular interaction properties. The latter two do this less precisely but enable analytical solutions. By limiting the questions asked, to optimal flux patterns, or to control of fluxes and concentrations around the (patho)physiological state, Flux Balance Analysis and Metabolic/Hierarchical Control Analysis again enable analytical solutions. Both the silicon cell approach and Metabolic/Hierarchical Control Analysis are able to highlight where and how system function derives from molecular interactions. The latter approach has also discovered a set of fundamental principles underlying the control of biological systems. The new law that relates concentration control to control by time is illustrated for an important signal transduction pathway, i.e. nuclear hormone receptor signaling such as relevant to bone formation. It is envisaged that there is much more Mathematical Biology to be discovered in the area between molecules and Life.     

从系统生物学到配伍西药:中药现代化的一个核心战略

国际范围内系统生物学的兴起和发展在为中药现代化发展提供机遇的同时,更容易为西药的创新研发提供方便的手段,因为西药在分子水平上的研究基础比中药要强大得多。系统生物学对分子网络包括基因网络的深层把握将自动把西药的设计引向配伍的道路,而配伍西药可能就是中药现代化的一个终极目标。所以说中药现代化其实面临着潜在的巨大危机。如何化不利形势为有利形势,需要中药现代化的决策部门在战略战术上提出及时的应对方案。需要从国家利益的角度吸纳不同领域的研究力量并在分子水平上极大地拓展相关研究,不失时机地开展与西药配伍潜势相结合的具体研究。     

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin’s antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.     

Signal integration during development: mechanisms of EGFR and Notch pathway function and cross-talk

Metazoan development relies on a highly regulated network of interactions between conserved signal transduction pathways to coordinate all aspects of cell fate specification, differentiation, and growth. In this review, we discuss the intricate interplay between the epidermal growth factor receptor (EGFR; Drosophila EGFR/DER) and the Notch signaling pathways as a paradigm for signal integration during development. First, we describe the current state of understanding of the molecular architecture of the EGFR and Notch signaling pathways that has resulted from synergistic studies in vertebrate, invertebrate, and cultured cell model systems. Then, focusing specifically on the Drosophila eye, we discuss how cooperative, sequential, and antagonistic relationships between these pathways mediate the spatially and temporally regulated processes that generate this sensory organ. The common themes underlying the coordination of the EGFR and Notch pathways appear to be broadly conserved and should, therefore, be directly applicable to elucidating mechanisms of information integration and signaling specificity in vertebrate systems.     

Oncogenes are to lose control on signaling following mutation

Rationalized cancer therapy aims at blocking overactive signaling pathways in cancer cells using kinase inhibitors. Essential for its success is the identification of suitable drug targets. Several recent reports have shown that by using control analysis, one can determine which component of a pathway is in control of its output. However, it has not been analyzed how a mutation in an oncogene affects the extent to which the various components are important. Are the same proteins still important after an oncogene has been activated? In the present study, we show that, upon mutation, oncogenes such as mutant kinases tend to lose part of their control on signaling. On the other hand, some of the nonmutated genes may become more important, when compared to the situation before the mutation. This may imply that, perhaps paradoxically, signaling proteins encoded by nonmutated genes should make better drug targets against cancer.