Bridging scales in cancer progression: Mapping genotype to phenotype using neural networks
Philip Gerlee, Eunjung Kim, Alexander R.A. Anderson

TL;DR
This review discusses how neural networks can model the complex, multi-scale processes of cancer progression, linking genotype, phenotype, and environment to better understand tumor heterogeneity and evolution.
Contribution
It introduces neural network models that connect biological scales in cancer, focusing on heterogeneity, micro-environment effects, and genotype-phenotype mapping.
Findings
Neural networks can simulate heterogeneity in cancer evolution.
Models reveal how micro-environment influences tumor dynamics.
Genotype to phenotype mapping under drug perturbations is feasible with neural networks.
Abstract
In this review we summarize our recent efforts in trying to understand the role of heterogeneity in cancer progression by using neural networks to characterise different aspects of the mapping from a cancer cells genotype and environment to its phenotype. Our central premise is that cancer is an evolving system subject to mutation and selection, and the primary conduit for these processes to occur is the cancer cell whose behaviour is regulated on multiple biological scales. The selection pressure is mainly driven by the microenvironment that the tumour is growing in and this acts directly upon the cell phenotype. In turn, the phenotype is driven by the intracellular pathways that are regulated by the genotype. Integrating all of these processes is a massive undertaking and requires bridging many biological scales (i.e. genotype, pathway, phenotype and environment) that we will only…
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Taxonomy
TopicsCancer Genomics and Diagnostics · Bioinformatics and Genomic Networks · Mathematical Biology Tumor Growth
