Cancer reversion, a renewed challenge in systems biology

Cho, Kwang-Hyun; Lee, Soobeom; Kim, Dongsan; Shin, Dongkwan; Joo, Jae Il; Park, Sang-Min

doi:10.1016/j.coisb.2017.01.005

Cited by 18 publications

(13 citation statements)

References 72 publications

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“…Identifying control targets is important for cell fate control toward a desired cellular state. For instance, we can consider a state conversion from a malignant cancerous state to a benign normal state, which is called cancer reversion (Cho et al, 2016(Cho et al, , 2017Choi et al, 2020;Lee et al, 2020). We also showed that our method is robust to noisy data through the example of MAPK33 network.…”

Section: Discussionmentioning

confidence: 82%

Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion

2020

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The molecular regulatory network (MRN) within a cell determines cellular states and transitions between them. Thus, modeling of MRNs is crucial, but this usually requires extensive analysis of time-series measurements, which is extremely difficult to obtain from biological experiments. However, single-cell measurement data such as single-cell RNA-sequencing databases have recently provided a new insight into resolving this problem by ordering thousands of cells in pseudo-time according to their differential gene expressions. Neural network modeling can be employed by using temporal data as learning data. In contrast, Boolean network modeling of MRNs has a growing interest, as it is a parameter-free logical modeling and thereby robust to noisy data while still capturing essential dynamics of biological networks. In this study, we propose a Boolean feedforward neural network (FFN) modeling by combining neural network and Boolean network modeling approach to reconstruct a practical and useful MRN model from large temporal data. Furthermore, analyzing the reconstructed MRN model can enable us to identify control targets for potential cellular state conversion. Here, we show the usefulness of Boolean FFN modeling by demonstrating its applicability through a toy model and biological networks.

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Section: Discussionmentioning

confidence: 82%

Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion

2020

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“…The most common updating schemes are the synchronous and the stochastic asynchronous updating schemes, and several others exist. A number of review articles offer insights into the construction and use of DDNMs ∗26 , 27 , 28 , 29 , 30 , 31 , ∗32 .…”

Section: Discrete Dynamic Network Models In Biologymentioning

confidence: 99%

Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer

Zañudo

Steinway

Albert

2018

Current Opinion in Systems Biology

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Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.

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“…Cancer is a complex disease that still causes fear in the world (Cho et al, 2017). Breast cancer (BC) is the leading cause of death among Women in the world, and this calls for more studies on chemoprevention and chemotherapy (Donepudi, Kondapalli, Amos, & Venkanteshan, 2014).…”

Section: Introductionmentioning

confidence: 99%

Citrinin against breast cancer: A cytogenotoxicological study

Filho

Andrade

Lima

et al. 2020

Phytotherapy Research

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Breast cancer is one of the most lethal types of cancer and a leading cause of mortality among Women worldwide. Citrinin (CIT), a polyketide extracted from the fungus Penicillium citrinum, exhibits a wide range of biological activities such as antibacterial, antifungal, and cytotoxic effects. The aim of the current study was to evaluate the antitumoral effects of CIT against 7,12‐dimethylbenzanthracene (DMBA)‐induced mammary carcinoma in Swiss mice For this, CIT, DMBA and the standard cyclophosphamide (CPA) induced behavioral changes in experimental animals, and these changes were screened by using the rota rod and open field tests. Additionally, hematological, biochemical, immuno‐histochemical, and histopathological analyses were carried out. Results suggest that CIT did not alter behavioral, hematological, and biochemical parameters in mice. DMBA induced invasive mammary carcinoma and showed genotoxic effects in the breasts, bone marrow, lymphocytes, and hepatic cells. It also caused mutagenic effects in the formation of micronuclei, bridges, shoots, and binucleate cells in bone marrow and liver. CIT and CPA genotoxic effects were observed after 3 weeks of therapy, where CIT exhibited a repair capacity and induced significant apoptotic damage in mouse lymphocytes. In conclusion, CIT showed antitumoral effects in Swiss mice, possibly through induction of apoptosis.

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Cancer reversion, a renewed challenge in systems biology

Cited by 18 publications

References 72 publications

Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion

Boolean Feedforward Neural Network Modeling of Molecular Regulatory Networks for Cellular State Conversion

Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer

Citrinin against breast cancer: A cytogenotoxicological study

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