Computational Cancer Biology

Research focus and mission

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.

Iterative cycle between experimental lab work and computational modelling.
PNAS 116(44): 22399-22408, 2019

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.

Executable Cancer Models. Nat Rev Can 20(5): 343-354, 2020.

Our lab

After more than a decade at Microsoft Research Cambridge and the Department of Biochemistry at the University of Cambridge, our lab moved in 2019 to the UCL Cancer Institute to strengthen our collaborations with the outstanding talent of medical and clinical oncologists and cancer biologists within the institute.

Our multidisciplinary lab hosts students, postdocs and visiting researchers from a diverse set of backgrounds (e.g., biology, physics, computer science, software engineering, medicine) all working together as a team. We collaborate closely with experimental cancer biologists and clinicians to build and analyse our computational models and validate their predictions experimentally. When appropriate, we aim to translate our findings into clinical trials to improve cancer patients’ outcome.

If you are interested in joining our lab, please get in touch.

Fisher Lab Group Members

Selected publications

1. Howell R, Davies J, Clarke MA, Appios A … Fisher J, Bennett CL. Localized immune surveillance of primary melanoma in the skin deciphered through executable modeling. Sci Adv. 2023 Apr 14;9(15): eadd1992.
2. Howell R, Clarke MA, Reuschl AK, Chen T … Fisher J. Executable network of SARS-CoV-2-host interaction predicts drug combination treatments. NPJ Digit Med. 2022 Feb 14;5(1):18.
3. Clarke MA, Fisher J. Executable cancer models: successes and challenges. Nat Rev Cancer. 2020 Jun;20(6): 343-354.
4. Kreuzaler P, Clarke MA, Brown EJ … Fisher J. Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modeling. Proc Natl Acad Sci U S A. 2019 Oct 29;116(44): 22399-22408.
5. Yu MK, Ma J, Fisher J, et al. Visible Machine Learning for Biomedicine. Cell. 2018 Jun 14;173(7): 1562-1565.
6. Silverbush D, Grosskurth S, Wang D … Fisher J. Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia. Cancer Res. 2017 Feb 15;77(4): 827-838.
7. Chuang R, Hall BA, Benque D … Fisher J. Drug target optimization in chronic myeloid leukemia using innovative computational platform. Sci Rep. 2015 Feb 3;5:8190.
8. Moignard V, Woodhouse S, Haghverdi L … Fisher J, Göttgens B. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat Biotechnol. 2015 Mar;33(3): 269-276.
9. Nusser-Stein S, Beyer A, Rimann I … Fisher J. Cell-cycle regulation of NOTCH signaling during C. elegans vulval development. Mol Syst Biol. 2012;8: 618.
10. Fisher J, Henzinger TA. Executable cell biology. Nat Biotechnol. 2007 Nov;25(11): 1239-49.

Computational Tools

Bio Model Analyzer

We developed Bio Model Analyzer (BMA), a biological modelling tool that illustrates signalling pathways and determines cellular stabilization. The tool represents a merging of perspectives from systems biology, formal methods, human computer interaction and design. At one level, BMA is a sketching tool that enables users to draw out a biological system of interest (e.g. a genetic regulatory network) by dragging and dropping cells, their contents (DNA, proteins, etc.), extracellular components and relationships onto a simple canvas. At another level, Bio Model Analyzer’s analysis proves stabilization of biological systems, based upon formal methods that were developed for the specification and verification of properties in concurrent software systems.

Single Cell Network Synthesis Tool

We developed the Single Cell Network Synthesis tool (SCNS), a tool for the reconstruction and analysis of executable models from single-cell gene expression data, which supports easy deployment of computation to the cloud for performance and control via a web-based graphical interface. SCNS can be used for understanding differentiation, developmental, or reprogramming journeys.

MAGELLAN

We developed MAGELLAN, a tool for the construction of executable network models from prior knowledge. These models integrate topology drawn from pathway databases with interaction logic inferred using machine learning and comparison to a specification of known experimental results. The resulting network models can be further analysed, in a mechanistically transparent manner, using our BioModelAnalyzer tool. For more information see preprint MAGELLAN: Automated Generation of Interpretable Computational Models for Biological Reasoning Clarke et al. 2025