Systems biology reveals biology of systems

Bertolaso, Marta; Giuliani, Alessandro; Gara, Laura De

doi:10.1002/cplx.20353

Cited by 8 publications

(7 citation statements)

References 28 publications

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“…First, how can data produced at the molecular level be correlated so that there is a biological meaning for higher organizational levels? Second, how can the conditioning of the epistemic premises in the very process of data collection, selection, and modeling be decided [131].…”

Section: Limits Of the Pragmatic Approachmentioning

confidence: 99%

Is Cancer Reversible? Rethinking Carcinogenesis Models—A New Epistemological Tool

2023

Self Cite

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A growing number of studies shows that it is possible to induce a phenotypic transformation of cancer cells from malignant to benign. This process is currently known as “tumor reversion”. However, the concept of reversibility hardly fits the current cancer models, according to which gene mutations are considered the primary cause of cancer. Indeed, if gene mutations are causative carcinogenic factors, and if gene mutations are irreversible, how long should cancer be considered as an irreversible process? In fact, there is some evidence that intrinsic plasticity of cancerous cells may be therapeutically exploited to promote a phenotypic reprogramming, both in vitro and in vivo. Not only are studies on tumor reversion highlighting a new, exciting research approach, but they are also pushing science to look for new epistemological tools capable of better modeling cancer.

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Section: Limits Of the Pragmatic Approachmentioning

confidence: 99%

Is Cancer Reversible? Rethinking Carcinogenesis Models—A New Epistemological Tool

2023

Self Cite

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“…Though this approach has led to significant improvement in understanding of human disease, translation to clinical impact has not been fast or frequent. Increasingly gaps and paradoxes arising from this assumption (Sonnenschein and Soto 2008;Bertolaso et al, 2011;Baker 2012;Bizzarri and Cucina 2014;Bertolaso, 2017) has led to acknowledgement that biological complexity has been overlooked (Nurse 2008) resulting in the rise of "systems biology", a term that describes the quantitative analysis of the dynamic interactions among several components of a biological system with the aim to understand the behavior of the system as a whole.…”

Section: A Systems Approachmentioning

confidence: 99%

How 3D Printing Is Reshaping Translational Research

Sigston

2021

Front. Bioeng. Biotechnol.

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“Translational Research” has traditionally been defined as taking basic scientific findings and developing new diagnostic tools, drugs, devices and treatment options for patients, that are translated into practice, reach the people and populations for whom they are intended and are implemented correctly. The implication is of a unidirectional flow from “the bench to bedside”. The rapidly emergent field of additive manufacturing (3D printing) is contributing to a major shift in translational medical research. This includes the concept of bidirectional or reverse translation, early collaboration between clinicians, bio-engineers and basic scientists, and an increasingly entrepreneurial mindset. This coincides with, and is strongly complemented by, the rise of systems biology. The rapid pace at which this type of translational research can occur brings a variety of potential pitfalls and ethical concerns. Regulation surrounding implantable medical devices is struggling to keep up. 3D printing has opened the way for personalization which can make clinical outcomes hard to assess and risks putting the individual before the community. In some instances, novelty and hype has led to loss of transparency of outcomes with dire consequence. Collaboration with commercial partners has potential for conflict of interest. Nevertheless, 3D printing has dramatically changed the landscape of translational research. With early recognition and management of the potential risks, the benefits of reshaping the approach to translational research are enormous. This impact will extend into many other areas of biomedical research, re-establishing that science is more than a body of research. It is a way of thinking.

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“…The high throughput techniques have generated a large amount of protein-protein interaction (PPI) data, gene expression data, and protein structure data, which enable scholars to find protein complexes based on the topological properties of PPI networks and structural information of proteins [7]. Bader and Hogue proposed MCODE [8] method to detect protein complexes based on the proteins' connectivity and density in PPI networks.…”

Section: Introductionmentioning

confidence: 99%

Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC

Zhao

Lei

2017

Complexity

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Protein complexes play a critical role in understanding the biological processes and the functions of cellular mechanisms. Most existing protein complex detection algorithms cannot reflect dynamics of protein complexes. In this paper, a novel algorithm named Improved Cuckoo Search Clustering (ICSC) algorithm is proposed to detect protein complexes in weighted dynamic proteinprotein interaction (PPI) networks. First, we constructed weighted dynamic PPI networks and detected protein complex cores in each dynamic subnetwork. Then, ICSC algorithm was used to cluster the protein attachments to the cores. The experimental results on both DIP dataset and Krogan dataset demonstrated that ICSC algorithm is more effective in identifying protein complexes than other competing methods.

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Systems biology reveals biology of systems

Cited by 8 publications

References 28 publications

Is Cancer Reversible? Rethinking Carcinogenesis Models—A New Epistemological Tool

Is Cancer Reversible? Rethinking Carcinogenesis Models—A New Epistemological Tool

How 3D Printing Is Reshaping Translational Research

Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC

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