Attractor landscape analysis of colorectal tumorigenesis and its reversion

Cho, Sung-Hwan; Park, Sang-Min; Lee, Ho-Sung; Lee, Hwang-Yeol; Cho, Kwang-Hyun

doi:10.1186/s12918-016-0341-9

Cited by 39 publications

(41 citation statements)

References 75 publications

(91 reference statements)

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“…A variety of network models of cancer cells has been previously established to investigate such dynamic features of the network from a systems biological perspective. However, previous studies have mainly focused on qualitative assessments of cancer such as changes in input-output relationships in cancer cells or comparison of major attractors between normal and cancer cells [19, 40]. For instance, a recent study employed a network modeling approach to identify control targets for anti-cancer treatment by qualitatively examining the changes of basin size between major attractors, such as proliferation and apoptosis [40].…”

Section: Discussionmentioning

confidence: 99%

“…However, previous studies have mainly focused on qualitative assessments of cancer such as changes in input-output relationships in cancer cells or comparison of major attractors between normal and cancer cells [19, 40]. For instance, a recent study employed a network modeling approach to identify control targets for anti-cancer treatment by qualitatively examining the changes of basin size between major attractors, such as proliferation and apoptosis [40]. However, these system-based studies still have fundamental limitations in systematic control for cancer reversion because there is no quantitative evaluation of cancer status.…”

Section: Discussionmentioning

confidence: 99%

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Quantitative evaluation and reversion analysis of the attractor landscapes of an intracellular regulatory network for colorectal cancer

et al. 2017

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Background: Cancer reversion, converting the phenotypes of a cancer cell into those of a normal cell, has been sporadically observed throughout history. However, no systematic analysis has been attempted so far. Results: To investigate this from a systems biological perspective, we have constructed a logical network model of colorectal tumorigenesis by integrating key regulatory molecules and their interactions from previous experimental data. We identified molecular targets that can reverse cancerous cellular states to a normal state by systematically perturbing each molecular activity in the network and evaluating the resulting changes of the attractor landscape with respect to uncontrolled proliferation, EMT, and stemness. Intriguingly, many of the identified targets were well in accord with previous studies. We further revealed that the identified targets constitute stable network motifs that contribute to enhancing the robustness of attractors in cancerous cellular states against diverse regulatory signals.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Quantitative evaluation and reversion analysis of the attractor landscapes of an intracellular regulatory network for colorectal cancer

et al. 2017

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“…Regarding cancer research, in the last years it has found valuable support in a wide range of modeling and simulation approaches, which cover a wide spectrum ranging from mathematical models -e.g., continuous models [2][3][4] and stochastic models [5][6][7][8] to computational models -e.g., Monte Carlo method and cellular automata [9][10][11], Boolean networks [12], Petri nets [13], artificial neural networks [14][15][16] and expert systems [17]. These approaches have allowed the in silico experimentation in cancer at the cellular, system and patient level.…”

Section: Bodymentioning

confidence: 99%

An In Silico Cell Signaling-Based Approach for Exploring the Activities Involved in Pre-Metastasis and Metastasis

González-Pérez

Cárdenas-García

2017

Genomics Comput Biol

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SUMMARYIn order to understand, identify and explore the activities involved in metastasis, as well as possible control points, in this work we model and simulate the sequential steps that a cell must follow from its transformation to metastasis, using the Cellulat bioinformatics tool, an in silico experimentation environment that complements and guides in vitro experimentation concerning intra and intercellular signaling networks. KEYWORDSMetastasis; Cancer cells; In silico experiments; Cellulat bioinformatics tool. BODYMetastasis is the clinical term for the process by which tumor cells leave a primary cancer tumor to transfer to different organs. The metastasis is considered to be the true killer in cancer patients, since usually when this process is triggered, it inevitably leads to death. To prevent the spreading of cancer, the most important is to avoid the metastasis of the primary tumor, as well as to find markers that allow early identification of the presence of a primary tumor [1]. Features that promote metastasis -i.e., sustained proliferation, replicative immortality, and evasion of growth suppression -allow cancer cells to grow uncontrollably in a tumor large enough to invade neighboring tissues. The ability of cells to resist cell death and prevent their destruction caused by the immune system response, allow them to survive on their way to metastasis.The intrinsic complexity of biological systems and phenomena -such as protein-protein interaction, protein-ligand docking and protein folding, just to mention a few examples -has required the development of a wide range of computational tools dedicated to the modeling and simulation of them, so that these computational approaches -also known as computer simulation or in silico experimentation -can complement, corroborate and enrich both the advances in theoretical and experimental research in the study of such systems. Regarding cancer research, in the last years it has found valuable support in a wide range of modeling and simulation approaches, which cover a wide spectrum ranging from mathematical models -e.g., continuous models [2][3][4] [17]. These approaches have allowed the in silico experimentation in cancer at the cellular, system and patient level.In order to understand, identify and explore the activities involved in metastasis, as well as possible control points, in this work we model and simulate the sequential steps that a cell must follow from its transformation to metastasis.We simulate and explore the complex interaction patterns of signaling pathways involved in pre-metastasis and metastasis using the Cellulat bioinformatics tool (http://bioinformatics.cua.uam.mx/node/10) [18,19], a computational simulation tool developed by us and inspired by Biochemical Tuple Spaces for Self-Organizing Coordination model (BTSSOC) [20]. The main idea behind the Cellulat bioinformatics tool is to provide an in silico experimentation environment that complements and guides in vitro experimentation concerning intra and intercellular signaling networks.C...

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“…The purpose of the study we propose here is not to provide a comprehensive 60 molecular description of the response but to verify that the existence and functionality of the 61 suggested feedback loops around the signalling pathway in which BRAF is involved [21] may be a 62 first hint towards these differences. For a more thorough study of these cancers, we refer to other 63 works [4,24,25].…”

Section: Introductionmentioning

confidence: 99%

Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers

Béal

Pantolini

Noël

et al. 2020

Preprint

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The study of response to cancer treatments has benefited greatly from the contribution of different omics data but their interpretation is sometimes difficult. Some mathematical models based on prior biological knowledge of signalling pathways, facilitate this interpretation but often require fitting of their parameters using perturbation data. We propose a more qualitative mechanistic approach, based on logical formalism and on the sole mapping and interpretation of omics data, and able to recover differences in sensitivity to gene inhibition without model training. This approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal cancers who experience significant differences in sensitivity despite similar omics profiles.We first gather information from literature and build a logical model summarizing the regulatory network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by automatically assessing that it qualitatively reproduces response or resistance behaviours identified in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to validate the model's ability to explain differences in sensitivity. This generic model is transformed into personalized cell line-specific logical models by integrating the omics information of the cell lines as constraints of the model. The use of mutations alone allows personalized models to correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and CRISPR targeting, and even better with the joint use of mutations and RNA, supporting multi-omics mechanistic models. A comparison of these untrained models with learning approaches highlights similarities in interpretation and complementarity depending on the size of the datasets.This parsimonious pipeline, which can easily be extended to other biological questions, makes it possible to explore the mechanistic causes of the response to treatment, on an individualized basis. Author summaryWe constructed a logical model to study, from a dynamical perspective, the differences between melanomas and colorectal cancers that share the same BRAF mutations but exhibit different sensitivities to anti-BRAF treatments. The model was built from the literature and completed from May 30, 2020 1/24 existing pathway databases. The model encompasses the key proteins of the MAPK pathway and was made specific to each cancer cell line (100 melanoma and colorectal cell lines from public database) using available omics data, including mutations and RNAseq data. It can simulate the effect of drugs and show high correlation with experimental results. Moreover, the structure of the network confirms both the importance of the reactivation of the MAPK pathway through CRAF and the involvement of PI3K/AKT pathway in the mechanisms of resistance to BRAF inhibition. The study shows that, because of the low number of samples, the ...

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Attractor landscape analysis of colorectal tumorigenesis and its reversion

Cited by 39 publications

References 75 publications

Quantitative evaluation and reversion analysis of the attractor landscapes of an intracellular regulatory network for colorectal cancer

Quantitative evaluation and reversion analysis of the attractor landscapes of an intracellular regulatory network for colorectal cancer

An In Silico Cell Signaling-Based Approach for Exploring the Activities Involved in Pre-Metastasis and Metastasis

Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers

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