About this Research Topic
Cancer, a paradigmatic example of a complex disease, is the second leading cause of death worldwide. Mechanisms and potential causes underlying the appearance and progression of cancer learned from single-scale studies have led to the development of personalized treatments, which in turn have decreased mortality rates in several types of cancer. However,
because it is not a single, isolated disease, but rather a heterogeneous set of multi-scale alterations, current research requires computational and systems biology approaches that deal with complex systems. A multi-omics approach for instance, aims at developing multi-scale mathematical modelling able to integrate the dynamics of biological perturbations. Or the tissue/organ-specific path that could create mechanistic models of multicellular systems. On the other hand, low-cost high throughput technologies have made publicly available massive cohorts of -omics and clinical data stored in publicly available databases. This immeasurable amount of information allows harnessing data science approaches for the analysis, integration, and mining of multi-omics data. Multi-disciplinary groups focus on creating models and provide theoretical frameworks that describe cancer transformation, cancer evolution, single cell genomics and transcriptomics, cancer driver mutations and mutational processes, development of target-specific treatments leading to a prediction of drug responses. Notwithstanding, several challenges that remain to be undertaken such as mechanisms of metastasis, resistance to treatment, intra-tumoral heterogeneity, molecular, cellular, and metabolic changes during progression stages, epigenetic modifications, to mention a few.
Cancer, understood as a complex system in which several layers of information interact in time and space has given place to a non-linear adaptive disease. Cancer systems biology incorporates the concept of multi-scale level of description, which consequently provides more information than the simple sum of single-level approaches. This multi-level framework may integrate the genetic, epigenetic, transcriptomic, metabolic, cellular, tissular, organismal and epidemiological layers. The cross-disciplinary interaction of these levels of description have contributed to develop more efficient models that improve the predictive capacity and ultimately help clinicians and medical scientists in the treatment and therapies.
The aim of this Research Topic is to discuss and explore state-of-the-art research focused on the ever-growing field of cancer systems biology.
Subjects to be covered in this special topic include, but are not limited to:
- Regulatory Networks
Structure and dynamics of networks in cancer
Signalling pathways and functional analyses
Single and multi-omics network approaches
Network medicine in cancer
-Mathematical Modelling
Modelling of tumor micro and macroenvironment
Dynamical Systems applied to cancer research
Algorithms and models for cancer progression
Multi-scale modelling
Inference of cancer tissue-of-origin and metastasis
-Computational Methods
Machine learning and AI in cancer research
Multi-omics-related molecular classification
Cancer bioinformatics, Data mining & Automated handling of large-scale datasets
-Translational Bioinformatics
Drug analysis and repurposing
Functional and algorithmic methods for biomarker validation
Computational methods for drug analysis and repurposing
Proteomics and post-translational modifications in cancer
Precision Therapeutics in Cancers
because it is not a single, isolated disease, but rather a heterogeneous set of multi-scale alterations, current research requires computational and systems biology approaches that deal with complex systems. A multi-omics approach for instance, aims at developing multi-scale mathematical modelling able to integrate the dynamics of biological perturbations. Or the tissue/organ-specific path that could create mechanistic models of multicellular systems. On the other hand, low-cost high throughput technologies have made publicly available massive cohorts of -omics and clinical data stored in publicly available databases. This immeasurable amount of information allows harnessing data science approaches for the analysis, integration, and mining of multi-omics data. Multi-disciplinary groups focus on creating models and provide theoretical frameworks that describe cancer transformation, cancer evolution, single cell genomics and transcriptomics, cancer driver mutations and mutational processes, development of target-specific treatments leading to a prediction of drug responses. Notwithstanding, several challenges that remain to be undertaken such as mechanisms of metastasis, resistance to treatment, intra-tumoral heterogeneity, molecular, cellular, and metabolic changes during progression stages, epigenetic modifications, to mention a few.
Cancer, understood as a complex system in which several layers of information interact in time and space has given place to a non-linear adaptive disease. Cancer systems biology incorporates the concept of multi-scale level of description, which consequently provides more information than the simple sum of single-level approaches. This multi-level framework may integrate the genetic, epigenetic, transcriptomic, metabolic, cellular, tissular, organismal and epidemiological layers. The cross-disciplinary interaction of these levels of description have contributed to develop more efficient models that improve the predictive capacity and ultimately help clinicians and medical scientists in the treatment and therapies.
The aim of this Research Topic is to discuss and explore state-of-the-art research focused on the ever-growing field of cancer systems biology.
Subjects to be covered in this special topic include, but are not limited to:
- Regulatory Networks
Structure and dynamics of networks in cancer
Signalling pathways and functional analyses
Single and multi-omics network approaches
Network medicine in cancer
-Mathematical Modelling
Modelling of tumor micro and macroenvironment
Dynamical Systems applied to cancer research
Algorithms and models for cancer progression
Multi-scale modelling
Inference of cancer tissue-of-origin and metastasis
-Computational Methods
Machine learning and AI in cancer research
Multi-omics-related molecular classification
Cancer bioinformatics, Data mining & Automated handling of large-scale datasets
-Translational Bioinformatics
Drug analysis and repurposing
Functional and algorithmic methods for biomarker validation
Computational methods for drug analysis and repurposing
Proteomics and post-translational modifications in cancer
Precision Therapeutics in Cancers
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