Computational Biology and Systems Biology

Emmanuelle Manck
Thanks to the contributions of high-throughput sequencing and mass spectrometry, oncology research is generating considerable volumes of data describing tumors. The quantity and precision of the information collected raise new conceptual and methodological questions in biology that Institut Curie researchers are trying to answer. To do so, they design and use sophisticated mathematical models requiring high performance computing capabilities.
image team Chavrier

A myoepithelium/basement membrane transmigration assay. A primary in situ tumor xenograft of cells expressing MT1-MMPmCherry (red) has been digested to tumor organoids, composed of…

Whether systems biology is focused on genetics and transcriptomics, intra- and intercellular networks and communications, or tumors and micro-environments, bioinformatics has proven necessary to this growing field. Institut Curie Research Center counts mathematicians and data scientists alongside its physicists and biologists; together, they collaborate to develop computational models and methods for treating patients. They also call upon artificial intelligence and machine learning, for which the Research Center has the necessary technical resources (5 petabytes of stored data and 4000 kHz of computing power) and scientific expertise (in large-scale data analysis and modeling biological processes).

In combining mathematics and experimentation, systems biology and computational biology reveal the mechanisms of how cancer works in order to more effectively fight it. For example, Institut Curie Research Center developed a model to processs 400 million measurements of tumor and cell line samples carrying mutations linked to Ewing's sarcoma. It helped decipher the tumorigenesis of this pediatric cancer.

Because these methods can approach and associate at different scales, the scope at which they can intervene continues to expand. At the cellular level, they allow for the simultaneous processing of cell expression data from different cells or the processing of cell positioning in a tumor sample. At the organ or whole body level, imaging techniques give a macroscopic view of the tumor. At the patient level, they facilitate a holistic perspective on cancer, integrating quality of life data with chance-of-survival data to help evaluate the benefits of a particular treatment pathway. At the population level, they can inform epidemiological studies and improve cancer prevention.

Computational biology and systems biology currently face a major challenge: integrating millions of data of all sizes and from all sources, from molecular-level biochemistry to whole-body images, into a coherent whole

says Emmanuel Barillot, director of the Cancer and Genome: Bioinformatics, Biostatistics, and Epidemiology of Complex Systems unit (U900) and head of the Computational Systems Biology of Cancer research team.