Deciphering and targeting cancer metabolism using executable models
Lead Research Organisation:
University of Cambridge
Department Name: MRC Cancer Unit
Abstract
The progression of cancer is driven by mutations and errors in the cell that promote the "hallmarks of cancer". These are a set of distinct behaviours that cells in tumours possess, enabling the survival and growth of the cancer. A longstanding observation in cancer cells is the Warburg effect; the switch away from oxidative phosphorylation to anaerobic glycolysis. Recent evidence has shown that metabolic reprogramming- large scale modification of how the cells process energy- is achieved by mutations in oncogenes including KRAS. This enables cancer cell survival in the tumour. Furthermore mutations in individual metabolic enzymes, such as fumarate hydratase, can affect a transition from epithelial to a mesenchymal morphology. Both the links between individual metabolites and cell behaviour, and the role of the metabolic network in cancer development however remains unclear. Knowledge of both of these are necessary to interpret metabolic changes in cancer and to identify new drug targets that are robust to network effects.
Computational modelling offers the opportunity to formalise the relationships between elements in the network and to address this issue. However, conventional approaches based on ordinary differential equations (ODEs) and flux balance analysis (FBA) are not suitable. ODE models require precise physico-chemical parameters that are not available for human metabolism. FBA does not have this requirement but is not capable of modelling the accumulation of metabolites that can occur in response to mutation.
I propose to use an "executable" modelling approach to construct models of the metabolic network and link them to cellular behaviour. Executable modelling approaches do not require detailed physical parameters, and can model accumulation events. They have the further advantage that they are amenable to a class of model analysis known as formal verification. Here mathematical proofs are used to offer guarantees of cell properties that are encoded in formal logic. These guarantees can apply over all possible states of the system and so offer a uniquely powerful way to test model correctness. This is particularly useful for highly robust systems such as the metabolic network, but also in taking account of rare events. This latter issue is particularly important when considering how rare events determine the development of cancer. Model building will be guided by the use of machine learning approaches (t-SNE, decision forests) to address publicly available expression data and metabolomic datasets shared with us by AstraZeneca. Finally, these models will be used to make novel predictions about the metabolism can change cell phenotype. The development of these models will allow us to understand how metabolic networks drive cancer progression and help us identify novel oncogenes and drug targets.
Computational modelling offers the opportunity to formalise the relationships between elements in the network and to address this issue. However, conventional approaches based on ordinary differential equations (ODEs) and flux balance analysis (FBA) are not suitable. ODE models require precise physico-chemical parameters that are not available for human metabolism. FBA does not have this requirement but is not capable of modelling the accumulation of metabolites that can occur in response to mutation.
I propose to use an "executable" modelling approach to construct models of the metabolic network and link them to cellular behaviour. Executable modelling approaches do not require detailed physical parameters, and can model accumulation events. They have the further advantage that they are amenable to a class of model analysis known as formal verification. Here mathematical proofs are used to offer guarantees of cell properties that are encoded in formal logic. These guarantees can apply over all possible states of the system and so offer a uniquely powerful way to test model correctness. This is particularly useful for highly robust systems such as the metabolic network, but also in taking account of rare events. This latter issue is particularly important when considering how rare events determine the development of cancer. Model building will be guided by the use of machine learning approaches (t-SNE, decision forests) to address publicly available expression data and metabolomic datasets shared with us by AstraZeneca. Finally, these models will be used to make novel predictions about the metabolism can change cell phenotype. The development of these models will allow us to understand how metabolic networks drive cancer progression and help us identify novel oncogenes and drug targets.
Technical Summary
The progression of cancer is associated with extensive metabolic reprogramming. A key feature of cancer is the Warburg effect, where cells switch from relying on oxidative phosphorylation to anaerobic glycolysis. Similarly, KRAS and BRAF mutations drive widescale reprogramming of metabolism, whilst the mutation of individual metabolic enzymes such as fumarate hydratase can promote cancer progression.
Computational models of cancer metabolism could elucidate the links between metabolism and cellular phenotype. Here we propose the construction of an executable model of cancer metabolism, guided by the use of machine learning applied to high throughput expression and metabolomic datasets and the existing literature on cancer metabolism. Executable models do not require the detailed physical parameterisation necessary for continuous models, such as those based on ordinary differential equations. They also allow more complex model behaviours to be described than flux balance analysis, including metabolite accumulation. Finally, they are amenable to analyses based on formal verification, where mathematical proofs are used to offer powerful guarantees of model behaviour by effectively searching all possible states of the model. The models and the new predictions that arise from the model construction will be validated through experiments performed with the Frezza group.
Computational models of cancer metabolism could elucidate the links between metabolism and cellular phenotype. Here we propose the construction of an executable model of cancer metabolism, guided by the use of machine learning applied to high throughput expression and metabolomic datasets and the existing literature on cancer metabolism. Executable models do not require the detailed physical parameterisation necessary for continuous models, such as those based on ordinary differential equations. They also allow more complex model behaviours to be described than flux balance analysis, including metabolite accumulation. Finally, they are amenable to analyses based on formal verification, where mathematical proofs are used to offer powerful guarantees of model behaviour by effectively searching all possible states of the model. The models and the new predictions that arise from the model construction will be validated through experiments performed with the Frezza group.
Planned Impact
This project will lead to demonstrable contributions within academia, industrial research, and wider society. Complex models of metabolism will be constructed using existing publicly available and privately shared high throughput datasets analysed using machine learning approaches, alongside the literature on cancer metabolism. These models will be tested using formal verification based analyses. The work will give rise to valuable insights into how metabolism can determine the cancer phenotype, and will be published in high impact papers and through conferences.
Within the academic community, the interdisciplinary nature of the work will support biologists working in metabolism, and computer scientists working on formal verification approaches applied to large models. The models that are generated will encompass large numbers of observations that have been made and as such can be used to test and validate experimental approaches before they are taken into the laboratory. Similarly, model building in the life sciences has generated new problems for existing approaches and algorithms, requiring the development of new techniques for analysis. As such, the construction of new, complex models will support algorithm developments, at a minimum by extending the test sets with large, realistic models, or by requiring new research.
Through working with high throughput datasets, supplied in part by AstraZeneca, we will be able to apply our models to relevant data on the metabolic behaviours of different cell lines. This will give new insights into a large body of experimental data covering multiple cell lines under constant conditions. This in turn will be used to inform drug development within the company, and will lead to future collaborations based around the research.
In addition to impact from the direct results of the project, the work will have additional impacts important to the wider UK. One post-doctoral research associate will be trained over the course of the fellowship in the building and analysis of executable models of biological processes, and the development of formal specifications describing biological processes. Current models of metabolism have been used in outreach activities (including demonstrations for school parties) and will be used to demonstrate to the wider public how computational methods aid scientific understanding of cancer and wider biology. This includes the development of worksheets for A-level students currently under discussion with the OCR.
Within the academic community, the interdisciplinary nature of the work will support biologists working in metabolism, and computer scientists working on formal verification approaches applied to large models. The models that are generated will encompass large numbers of observations that have been made and as such can be used to test and validate experimental approaches before they are taken into the laboratory. Similarly, model building in the life sciences has generated new problems for existing approaches and algorithms, requiring the development of new techniques for analysis. As such, the construction of new, complex models will support algorithm developments, at a minimum by extending the test sets with large, realistic models, or by requiring new research.
Through working with high throughput datasets, supplied in part by AstraZeneca, we will be able to apply our models to relevant data on the metabolic behaviours of different cell lines. This will give new insights into a large body of experimental data covering multiple cell lines under constant conditions. This in turn will be used to inform drug development within the company, and will lead to future collaborations based around the research.
In addition to impact from the direct results of the project, the work will have additional impacts important to the wider UK. One post-doctoral research associate will be trained over the course of the fellowship in the building and analysis of executable models of biological processes, and the development of formal specifications describing biological processes. Current models of metabolism have been used in outreach activities (including demonstrations for school parties) and will be used to demonstrate to the wider public how computational methods aid scientific understanding of cancer and wider biology. This includes the development of worksheets for A-level students currently under discussion with the OCR.
Publications
Abby E
(2023)
Notch1 mutations drive clonal expansion in normal esophageal epithelium but impair tumor growth.
in Nature genetics
Clarke M
(2019)
Automated Reasoning for Systems Biology and Medicine
Colom B
(2020)
Spatial competition shapes the dynamic mutational landscape of normal esophageal epithelium.
in Nature genetics
Colom B
(2021)
Mutant clones in normal epithelium outcompete and eliminate emerging tumours.
in Nature
Fowler JC
(2021)
Selection of Oncogenic Mutant Clones in Normal Human Skin Varies with Body Site.
in Cancer discovery
Hall B
(2021)
Data integration in logic-based models of biological mechanisms
in Current Opinion in Systems Biology
Hall MWJ
(2023)
Mutations observed in somatic evolution reveal underlying gene mechanisms.
in Communications biology
Kostiou V
(2021)
Simulations reveal that different responses to cell crowding determine the expansion of p53 and Notch mutant clones in squamous epithelia.
in Journal of the Royal Society, Interface
Description | A-level worksheet development with OCR |
Geographic Reach | National |
Policy Influence Type | Influenced training of practitioners or researchers |
Impact | Computational science plays a major role in all branches of the natural sciences. Whilst mathematical models have long played a role in hard sciences, driving uptake of computational techniques, this has spread significantly over the last two decades to include the life sciences. This shift in the sciences reflects a wider change in society as computers and computational thinking become more embedded in all areas of life. Whilst there have been substantive shifts in recent years through efforts such as "Computing at schools" to encourage computer science in early years education, this has been predominantly limited to that discipline. Despite its value being recognised, and the requirement of examination boards for modelling approaches, "computational science" has been slower to be adopted due to lack of appropriate software and developed activites. Over the past QQ we have developed a body of work aimed at A-level students to introduce cancer modelling, based on research at MRC CU. At the heart of this work is the use of the open source BioModelAnalyzer tool (http://biomodelanalyzer.org/), a graphical tool for network model construction and analysis. We have presented demonstrations at the Big Biology day, the Cambridge Science Festival, and the MRC Festival of Science. Through connections made with individual teachers during the MRC Festival, we were approached by Richard Tateson (and later Andri Achilleos) of OCR and worked with OCR to develop A-level worksheets. These were first released in 2018 through OCR forums for teachers, and with a blog post made in early 2019 https://www.ocr.org.uk/blog/getting-started-with-computational-biology-in-cancer-research/. Over the past year this has been evaluated by both the Hall group with A-level work experience students and in local schools and separately by OCR, and as part of this work, the Hall group took part in the new schools hub at the Royal Society Exhibition (Summer 2019), demonstrating the tool for students and sharing worksheets with teachers. In response to the detailed evaluation results and broader feedback from OCR, the worksheets are currently undergoing rewriting in preparation for assessment for inclusion in the curriculum. If approved at the "PAG10" standard, these will be suitable for adoption by up to 60,000 A-level biology students a year in the UK. |
URL | https://www.ocr.org.uk/blog/getting-started-with-computational-biology-in-cancer-research/ |
Description | Research Fellows Enhanced Research Expenses 2021 |
Amount | £168,282 (GBP) |
Funding ID | RF\ERE\210188 |
Organisation | The Royal Society |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 12/2021 |
End | 03/2023 |
Description | University Research Fellowships Renewals 2019 |
Amount | £475,798 (GBP) |
Funding ID | URF\R\191013 |
Organisation | The Royal Society |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 04/2020 |
End | 03/2023 |
Description | Executable modelling of cancer metabolic networks |
Organisation | AstraZeneca |
Department | Research and Development AstraZeneca |
Country | United Kingdom |
Sector | Private |
PI Contribution | Developing network models and analysing large datasets of metabolic changes in cancer |
Collaborator Contribution | Shared datasets, expertise, and training of members of my lab. |
Impact | This is a multidisciplinary project, involving mathematical modelling, data science, and experimental techniques in cancer metabolism |
Start Year | 2018 |
Description | Executable modelling of cancer metabolic networks |
Organisation | University of Cambridge |
Department | MRC Cancer Unit |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Developing network models and analysing large datasets of metabolic changes in cancer |
Collaborator Contribution | Shared datasets, expertise, and training of members of my lab. |
Impact | This is a multidisciplinary project, involving mathematical modelling, data science, and experimental techniques in cancer metabolism |
Start Year | 2018 |
Description | Metabolic maps of cancer |
Organisation | AstraZeneca |
Country | United Kingdom |
Sector | Private |
PI Contribution | Analysis of cell line metabolomics data |
Collaborator Contribution | Generation of metabolomics data |
Impact | None at present |
Start Year | 2018 |
Description | Modelling stromal function in tumour draining lymph nodes |
Organisation | Medical Research Council (MRC) |
Department | MRC Cancer Unit |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | This a collaboration between computational biologists and wet lab experimentalists. Experiments yielding large amounts of data were performed by the Shields Lab and analysed by us. This data was then used as a basis for modelling systems, where the predictions were functionally verified by members of the Shields lab. |
Collaborator Contribution | This a collaboration between computational biologists and wet lab experimentalists. Experiments yielding large amounts of data were performed by the Shields Lab and analysed by us. This data was then used as a basis for modelling systems, where the predictions were functionally verified by members of the Shields lab. |
Impact | Riedel et al Nature Immunology 2016. This is a multidisciplinary collaboration crossing wet lab and in silica approaches |
Start Year | 2015 |
Title | BioModelAnalyzer |
Description | Software for the construction and analysis of executable models |
Type Of Technology | Software |
Year Produced | 2019 |
Open Source License? | Yes |
Impact | Added features that enable model and motif construction through the introduction of selection and copy tools |
URL | http://biomodelanalyzer.org/ |
Title | BioModelAnalyzer |
Description | Software for the development and analysis of executable models |
Type Of Technology | Software |
Year Produced | 2020 |
Open Source License? | Yes |
Impact | Design refinements and unification of model and motif library, easing the use and reuse of models |
URL | http://biomodelanalyzer.org/ |
Title | BioModelAnalyzer (2021 |
Description | BioModelAnalyzre is a tool for the construction and analysis of executable models of biological systems |
Type Of Technology | Software |
Year Produced | 2021 |
Open Source License? | Yes |
Impact | Taught at workshop, masters courses, and used heavily in my research |
URL | https://biomodelanalyzer.org/ |
Title | CaSQ |
Description | Software tool for converting formats for describing network maps to models automatically. I added support for BioModelAnalyzer export. |
Type Of Technology | Software |
Year Produced | 2021 |
Open Source License? | Yes |
Impact | Will be adopted in the near future by students from collaborators groups. |
URL | https://github.com/soli/casq |
Title | OmnipathR |
Description | Tool for the extraction of networks from the omnipath database of gene interactions. I added BMA motif export support. |
Type Of Technology | Software |
Year Produced | 2019 |
Open Source License? | Yes |
Impact | Aids adoption in the community |
URL | https://bioconductor.org/packages/OmnipathR |
Description | School practical session (Perse) |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Schools |
Results and Impact | Around 20 A-level students attended a short talk and a practical session run by myself using the BioModelAnalyzer. Students enjoyed the practical and gave extensive feedback which will be included in future updates. |
Year(s) Of Engagement Activity | 2019 |
URL | https://www.instagram.com/p/B3uJGNJpToD/ |