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Our lab develops state-of-the-art computational models and analysis techniques to study cancer evolution and mechanisms of drug resistance to identify better personalised treatments for cancer patients.
Our research is focused on understanding how cancers evolve through the identification of molecular mechanisms that underpin cell-fate decision programs during both normal development and disease. We have developed an innovative approach called Executable Biology, which is a toolset to simulate and analyse biological mechanisms as if they were computer programs. This approach enables us to use powerful methods developed in computer science to prove properties of these programs and simulations to gain better understanding of the dynamic complexity of evolving biological processes such as cancer. This approach has been shown to be highly effective in developing deeper insights into the molecular mechanisms of cell fate decisions and in the discovery of novel combination therapies for cancer.
Our goal is to determine the mechanistic programs by which oncogenic signalling pathways regulate the onset, progression, maintenance and (when blocked) regression of cancers. We do this by computational modelling of oncogenic signalling networks and how they are linked to cell fate decisions (such as proliferation and cell death). In this way, we can understand how cellular decisions are made and how aberrations in those decisions drive the pathology of cancer.
Our lab develops state-of-the-art computational models and analysis techniques to study cancer evolution and mechanisms of drug resistance to identify better personalised treatments for cancer patients.
Our research is focused on understanding how cancers evolve through the identification of molecular mechanisms that underpin cell-fate decision programs during both normal development and disease. We have developed an innovative approach called Executable Biology, which is a toolset to simulate and analyse biological mechanisms as if they were computer programs. This approach enables us to use powerful methods developed in computer science to prove properties of these programs and simulations to gain better understanding of the dynamic complexity of evolving biological processes such as cancer. This approach has been shown to be highly effective in developing deeper insights into the molecular mechanisms of cell fate decisions and in the discovery of novel combination therapies for cancer.
Our goal is to determine the mechanistic programs by which oncogenic signalling pathways regulate the onset, progression, maintenance and (when blocked) regression of cancers. We do this by computational modelling of oncogenic signalling networks and how they are linked to cell fate decisions (such as proliferation and cell death). In this way, we can understand how cellular decisions are made and how aberrations in those decisions drive the pathology of cancer.
研究兴趣
论文共 101 篇作者统计合作学者相似作者
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Dana Silverbush, Shaun Grosskurth,Dennis Wang, Francoise Powell, Berthold Gottgens,Jonathan Dry,Jasmin Fisher
crossref(2023)
Genome Medicineno. 1 (2023): 1-24
Dana Silverbush, Shaun Grosskurth,Dennis Wang, Francoise Powell, Berthold Gottgens,Jonathan Dry,Jasmin Fisher
crossref(2023)
Dana Silverbush, Shaun Grosskurth,Dennis Wang, Francoise Powell, Berthold Gottgens,Jonathan Dry,Jasmin Fisher
crossref(2023)
Rowan Howell,James Davies,Matthew A. Clarke,Anna Appios, Inês Mesquita,Yashoda Jayal, Ben Ringham-Terry,Isabel Boned Del Rio,Jasmin Fisher,Clare L. Bennett
Science Advancesno. 15 (2023): eadd1992-eadd1992
Rowan Howell,Matthew A. Clarke,Ann-Kathrin Reuschl, Tianyi Chen, Sean Abbott-Imboden,Mervyn Singer,David M. Lowe,Clare L. Bennett,Benjamin Chain,Clare Jolly,Jasmin Fisher
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