Cancer systems biology: a network modeling perspective

Kreeger, Pamela K.; Lauffenburger, Douglas A.

doi:10.1093/carcin/bgp261

Cited by 336 publications

(254 citation statements)

References 87 publications

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“…While these problems have been identified in conventional node biomarker studies, their solution requires biomarkers that integrate network information and even dynamical information, such as (dynamical) edge biomarkers. Such biomarkers have been extensively proposed and investigated in recent years [4,33,34], and are described in the next sections.…”

Section: Node Biomarkers For Classification and Prediction Without Nementioning

confidence: 99%

Edge biomarkers for classification and prediction of phenotypes

Zeng

Zhang

et al. 2014

Sci. China Life Sci.

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In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.biomarker, edge biomarker, dynamical network biomarker, classification, prediction, phenotype, disease diagnosis, disease prognosis Citation:

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Section: Node Biomarkers For Classification and Prediction Without Nementioning

confidence: 99%

Edge biomarkers for classification and prediction of phenotypes

Zeng

Zhang

et al. 2014

Sci. China Life Sci.

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“…Although there are numerous examples of systems biology projects that address biomedical questions, it cannot be denied that most of the efforts are easily labeled as "basic research, " often being pathway centered with a focus on the subcellular level (2,3). Furthermore, we explore the challenges and limitations of mechanistic modeling in systems medicine in the following sections.…”

Section: Reviewmentioning

confidence: 99%

The road from systems biology to systems medicine

et al. 2013

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Review nature publishing group as research institutions prepare roadmaps for "systems medicine, " we ask how this differs from applications of systems biology approaches in medicine and what we (should) have learned from about one decade of funding in systems biology. after surveying the area, we conclude that systems medicine is the logical next step and necessary extension of systems biology, and we focus on clinically relevant applications. We specifically discuss three related notions. First, more interdisciplinary collaborations are needed to face the challenges of integrating basic research and clinical practice: integration, analysis, and interpretation of clinical and nonclinical data for diagnosis, prognosis, and therapy require advanced statistical, computational, and mathematical tools. second, strategies are required to (i) develop and maintain computational platforms for the integration of clinical and nonclinical data, (ii) further develop technologies for quantitative and time-resolved tracking of changes in gene expression, cell signaling, and metabolism in relation to environmental and lifestyle influences, and (iii) develop methodologies for mathematical and statistical analyses of integrated data sets and multilevel models. Third, interdisciplinary collaborations represent a major challenge and are difficult to implement. For an efficient and successful initiation of interdisciplinary systems medicine programs, we argue that epistemological, ontological, and sociological aspects require attention. Lessons From systems BioLogyHere, we discuss the developments that can or should take us from systems biology to systems medicine. Although it is easy to agree that medicine should embark on that journey, it remains unclear which route should be taken. Our own experience and analysis of developments in the field of systems biology has taught us several lessons of which the following are of central importance and which shall be the focus of our discussion: (i) many diseases have their origin in cellular malfunction, requiring a deep understanding of the mechanisms underlying cell functions; (ii) the emergence of diseases is a nonlinear dynamical phenomenon, requiring quantitative time-resolved monitoring of key biological parameters at the molecular, cellular, and physiological levels; (iii) advances in measurement technologies can generate large-scale, multilevel but also heterogeneous datasets, requiring not only new computational platforms to manage data but most importantly, requiring new ways of thinking, including the application and development of methodologies from the mathematical sciences; and (iv) to address clinical questions with statistical, mathematical, computational, molecular, and cell-biological methodologies requires strategic efforts to motivate and sustain cross-disciplinary collaborations.Applying systems approaches in a clinical setting, practical, i.e., formal/legal and computational issues of data collection and sharing are the most immediate challenge and potential threat to...

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“…L'intégration de ces données biologiques grâce à des méthodes bio-informatiques permet de cerner la fonction de nouveaux gènes, de nouveaux modèles animaux pour des maladies humaines, des composants de voies de signalisation, des cibles thé-rapeutiques, des agents pharmacologiques, et de prédire les effets de ces agents [16][17][18]. Ces approches ont été utilisées avec succès dans différents cas de cancer et ont confirmé le rôle des kinases/phosphatases et des GTPases au sein des mécanismes d'intégration des voies de signalisation favorisant la tumorigenèse et l'acquisition de potentiel métastatique [19,36]. Ce type d'approche commence à être appliqué aux maladies cognitives et comportementales telles que la schizophrénie, la DI et l'autisme [20,21].…”

Section: Revuesunclassified

Soigner la déficience intellectuelle : la recherche d’équilibre

Harel

Jenna

2011

Med Sci (Paris)

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Le siège de la cognitionLa déficience intellectuelle (DI) se caractérise par un déficit cognitif associé à un quotient intellectuel inférieur à 70. En Occident, 2 à 3 % de la population souffrent de diverses formes de DI. Quand ces formes sont associées à d'autres symptômes cliniques, on parle alors de formes syndromiques de DI. Si la DI se limite à ces deux caractéristiques (déficit cognitif et QI infé-rieur à 70), on parle, dans ce cas, de formes non syndromiques ou non spécifiques de DI. Ces différentes formes de DI peuvent avoir des origines génétiques ou environnementales. À l'échelle macro-anatomique, une DI peut être associée à une altération de la structure du cortex cérébral et de l'hippocampe, ou à une microcéphalie [1]. En revanche, certaines formes de DI, par exemple celles qui sont associées aux syndromes de Down, du X fragile, de Rett ou encore de Rubinstein-Taybi, ainsi que certaines formes non syndromiques de DI, se caractérisent par une altération de la densité et de la morphologie des épines dendritiques ( Figure 1A) [1]. Les épines dendritiques sont des bourgeonnements des dendrites formant les domaines post-synaptiques > Certaines formes de déficience intellectuelle résultent d'une réduction de la capacité des neurones, en l'occurrence ceux de l'hippocampe, à moduler l'efficacité de leur transmission synaptique en réponse à certaines stimulations. Elles proviennent donc d'une altération de la fonction de certaines protéines dans les voies de signalisation qui régulent ces mécanismes dans les synapses. Afin d'augmenter les capacités cognitives des enfants présentant des déficiences intellectuelles attribuables à ces phénomènes, des travaux de recherche très prometteurs sont actuellement en cours. Ils ont pour objectif de caractériser la machinerie de signalisation dans ces états pathologiques et de sélectionner des moyens thérapeutiques susceptibles de restaurer l'état physiologique normal. < qui permettent la transduction des signaux provenant des neurones pré-synaptiques. Ces structures se caractérisent par leur nature très plastique, leur morphologie et leur nombre étant modulés par l'activité neuronale. Le fait que certaines formes de DI proviendraient d'une altération de la communication neuronale et non d'une malformation structurelle du cerveau ouvre des perspectives thérapeutiques captivantes. Il semble en effet plus aisé de moduler une activité neuronale dysfonctionnelle grâce à un traitement pharmacologique administré au cours de l'enfance d'un individu que d'envisager une réparation structurelle de certaines parties de son cerveau. La plasticité synaptique et la cognitionDes changements morphologiques des épines dendritiques sont corrélés aux différents états d'efficacité de la transmission synaptique, ce que l'on désigne par le terme de plasticité synaptique (PS). La PS dépend de processus moléculaires permettant le maintien dans les épines dendritiques d'une optimisation de l'efficacité de transmission synaptique, que l'on appelle aussi la potentialisation à long terme (PLT), ou d...

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Cancer systems biology: a network modeling perspective

Cited by 336 publications

References 87 publications

Edge biomarkers for classification and prediction of phenotypes

Edge biomarkers for classification and prediction of phenotypes

The road from systems biology to systems medicine

Soigner la déficience intellectuelle : la recherche d’équilibre

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