Arturo Araujo, PhD
Cancer Scientist. Plastic Artist. Film Lover.
I am Lecturer in Computer Science at the University of Roehampton, London and honorary researcher at University College London (UCL). My vision is to examine systems related to natural life to elucidate its first principles and understand the emergent dynamics through the use of computational and biological models.
I have a PhD in Modelling of Biological Complexity from UCL, 5 years postdoctoral experience at the Integrated Mathematical Oncology (IMO) department at H. Lee Moffitt Cancer Center, and 3 years experience in AI industry. I model cancer at the interphase between computer science, biology and the clinic. I have been awarded for this research a fellowship usually reserved for experimental biologists from the Department of Defense of the United States, and the first Braintree Research Fellowship to develop novel AI techniques to tackle the disease. My long-term goal is to contribute to the development of the fundamental laws of biology that will allow for the much needed exploration, understanding and optimal treatment of cancer.
Here's the latest on my current research:
Araujo, A., Cook, L.M., Frieling, J.S., Tan, W., Copland, J.A., Kohli, M., Gupta, S., Dhillon, J., Pow-Sang, J., Lynch, C.C. and Basanta, D. (2021). Quantification and optimization of standard-of-care therapy to delay the emergence of resistant bone metastatic prostate cancer. Cancers, 13(4), p.677.
One of the biggest challenges we face in cancer research is that collecting biological data is not enough. We need to elucidate which are the right methods to analyse and utilise the data. There is an abundance of data in many scales, from molecular and cellular to epidemiological; but without the right tools, such as multi-scale models, most of it remains under-utilised. Development of preventive and more effective treatments is hindered by gaps in our understanding cancer initiation and evolution, as well as the limitations of in vitro and in vivo experimental techniques. We have bridged this gap in our knowledge by constructing cell-based models of healthy organs, incorporating multi-scale biological data at the cellular (via agent-based modelling) and molecular level (via a gene regulatory network), generating solely through their interactions the emergent properties of homeostasis, self-regulation and healing. Using these in silico experimental environments, in which the effects of oncogenic mutations can be investigated and analysed with unique granularity, we investigate tumour initiation through genetic mutations, and shed light on the possible evolutionary trajectories during its growth. I envision the creation of patient-specific models as a clinical aid for outcome forecasting.
Understanding Colorectal Cancer Initiation
Intestinal glands in the small intestine and colon, or intestine crypts, are an important example of tissue homeostasis regulated by the extracellular environment. The crypts are invaginated structures made of a layer of cells that help absorb nutrients from passing food. However, they are continuously worn away by this process and are being continually renovated by Stem Cells at the bottom of the crypt. These Stem Cells divide to replace worn cells and may even displace other stem cells so that at a given time the whole crypt becomes monoclonal- a descendant of one single Stem Cell. Colorectal Cancer, the second leading cause of cancer-related death in Europe and North America, is thought to start with a mutation of one Stem Cell at the base of the intestinal crypt; which then expands within the crypt until the crypt is composed of monoclonal cells. The time to monoclonality therefore offers a key metric for the successful establishment of mutations; however, the biggest biological contributor to this feature is highly debated. To tackle this, we have abstracted key biological features and modelled them in a bottom-up Agent-Based Model that allowed us to study the biological first principles that rule the fixation of mutations, offering key spatial and temporal understanding of this process. Our results show that the number of basal Stem Cells have a direct influence on the fixations of mutations and suggesting a lesser role for extracellular influences, while proposing the existence of a threshold to the contribution of cell side displacement. Our results have been published as:
Modelling Therapies for Prostate to Bone Metastasis
Bone metastasis is common in prostate cancer progression. In bone, prostate cancer cells derive factors necessary for progression by manipulating bone forming osteoblasts and bone resorbing osteoclasts, resulting in areas of excessive osteogenesis and osteolysis, respectively. Transforming growth factor beta (TGFBeta) is a key factor in the progression of bone metastases. Therapeutic inhibition of TGFBeta however, presents a dilemma since it can have differential effects on various cell types in the tumor-bone microenvironment. In our current study, we have utilised an integrated approach using mathematical and in vivo models to test the impact of TGFBeta inhibition on prostate to bone metastases as a possible therapy. For this, we developed an agent-based mathematical model where the interactions between key cell types and their role on the evolutionary dynamics of the tumour can be studied. Our work has been published in:
Aneuploidy in Cancer
Many cancers are aneuploid (have chromosomal aberrations). However, the precise role that chromosomal instability plays in the development of cancer and in the response of tumours to treatment is still hotly debated. To explore this question from a theoretical standpoint we have developed an agent-based model of tissue homeostasis in which to test the likely effects of whole chromosome mis-segregation during cancer development. In stochastic simulations, chromosome mis-segregation events at cell division lead to the generation of a diverse population of aneuploid clones that over time exhibit hyperplastic growth. Significantly, the course of cancer evolution depends on genetic linkage, as the structure of chromosomes lost or gained through mis-segregation events and the level of genetic instability function in tandem to determine the trajectory of cancer evolution. As a result, simulated cancers differ in their level of genetic stability and in their growth rates. We used this system to investigate the consequences of these differences in tumour heterogeneity for anti-cancer therapies based on surgery and anti-mitotic drugs that selectively target proliferating cells. Our model highlights the difficulties of predicting the outcome of a given anti-cancer treatment, even in cases in which it is possible to determine the genotype of the entire set of cells within the developing tumour. This work was published in :
Arturo in the Media
I aim to use all of my interdisciplinary training and viewpoints to successfully bridge the gap between theory, experiments and the clinic with integrated computational models in tight collaboration with biologists and clinicians. Together with Dr. Leah Cook, we successfully modelled Prostate Bone Metastasis at the interphase between mathematics, biology and the clinic. The cutting-edge computational techniques I developed have been highlighted in the media at:
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I have the ability to think abstractly. This is my main feature It is easy for me to see the grey areas and the blurred lines. I spend most of my time thinking through stuff. I love to talk about culture, life stories and sincere issues. I strive to see the other persons point-of-view on any situation.
I have rarely been at complete peace with myself, but I have often feel happiest and most fulfilled when making models to help others understand their problems, be it scientific, artistic, social or personal.
My passions are coffee, red wine and watching films. Lots of films. I also build lightsabers.