Navigating cancer network attractors for tumor-specific therapy

Creixell, Pau; Schoof, Erwin M.; Erler, Janine T.; Linding, Rune

doi:10.1038/nbt.2345

Cited by 136 publications

(123 citation statements)

References 79 publications

(93 reference statements)

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“…While a theoretical basis for such manipulation has been established in the case of deregulation of a single node (e.g. a single genetic mutation) [10], complex diseases are triggered by several co-existing gene mutations [9,14,15]. The algorithm presented here can be used to design preventive interventions for combinations of multiple dysfunctions of the network.…”

Section: Discussionmentioning

confidence: 99%

“…[10] and investigate the properties of interventions that prevent the cascading effect of knocking out two nodes in a network. The potential combinatorial effects of simultaneous damage to two nodes have been named genetic interactions in biological systems [9]. A specific example of cases where combined knockout of two genes has a stronger effect than the sum of the effects of the individual knockouts is synthetic lethality [39][40][41].…”

Section: Classification Of the Resilience Scenarios Of Double Node Damentioning

confidence: 99%

“…Complex diseases including diabetes and cancers can be modeled as network damage due to temporary or permanent node perturbation (e.g. constitutive activation of a protein arising from a genetic mutation) [2,9]. Thus the topics of network repair and network control have drawn significant attention in the scientific community [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Recent research suggests that complex diseases such as cancer often involve multiple gene mutations and the "one disease, one target, one drug" approach may not be effective to battle these diseases [9,14,15]. Thus it is worthwhile to use the network paradigm to explore the combinatorial effect of multiple gene mutations, and to * gzy105@psu.edu † rza1@psu.edu design control measures to prevent these effects.…”

Section: Introductionmentioning

confidence: 99%

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Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

2016

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Complex diseases can be modeled as damage to intra-cellular networks that results in abnormal cell behaviors. Network-based dynamic models such as Boolean models have been employed to model a variety of biological systems including those corresponding to disease. Previous work designed compensatory interactions to stabilize an attractor of a Boolean network after single node damage. We generalize this method to a multi-node damage scenario and to the simultaneous stabilization of multiple steady state attractors. We classify the emergent situations, with a special focus on combinatorial effects, and characterize each class through simulation. We explore how the structural and functional properties of the network affect its resilience and its possible repair scenarios. We demonstrate the method ′ s applicability to two intra-cellular network models relevant to cancer. This work has implications in designing prevention strategies for complex disease.

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Section: Discussionmentioning

confidence: 99%

Section: Classification Of the Resilience Scenarios Of Double Node Damentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

2016

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“…The focus on activity states of a regulatory network rather than specific mutations is motivated by recent empirical studies showing that there is high heterogeneity of mutations in sequenced tumors for nonhereditary cancers (Creixell, Schoof, Erler, and Linding, 2012). The importance of context and reversible dynamics of cell states is further supported by experimental interventions showing that normal cells can turn cancerous when placed next to neoplastic tissues and that cancer cells can be normalized if transplanted from tumors to a location next to normal stroma (Lang, Shi, and Chin, 2013).…”

Section: Cancer As Dynamic Attractor Statesmentioning

confidence: 99%

Network analyses in systems biology: new strategies for dealing with biological complexity

et al. 2017

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Abstract:The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity.

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Experimental and Computational Tools for Analysis of Signaling Networks in Primary Cells

Schoof

Linding

2014

CP in Immunology

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Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis, or differentiation. Protein phosphorylation events play a major role in this process and are often involved in fundamental biological and cellular processes such as protein-protein interactions, enzyme activity, and immune responses. Determining which kinases phosphorylate specific phospho sites poses a challenge; this information is critical when trying to elucidate key proteins involved in specific cellular responses. Here, methods to generate high-quality quantitative phosphorylation data from cell lysates originating from primary cells, and how to analyze the generated data to construct quantitative signaling network models, are presented. These models can subsequently be used to guide follow-up in vitro/in vivo validation studies.

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Navigating cancer network attractors for tumor-specific therapy

Cited by 136 publications

References 79 publications

Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

Compensatory interactions to stabilize multiple steady states or mitigate the effects of multiple deregulations in biological networks

Network analyses in systems biology: new strategies for dealing with biological complexity

Experimental and Computational Tools for Analysis of Signaling Networks in Primary Cells

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