The decade of genomic revolution following the complete determination of the human DNA sequence, has produced significant medical advances, and yet again, revealed how complicated human biology is, and how much more remains to be understood. Biology is an extraordinary complicated puzzle; we may know some of its pieces but have no clue how they are assembled to orchestrate the symphony of life, which renders the comprehension and analysis of living systems a major challenge.
Computer science can play a major role in helping solve this puzzle. Recent efforts to create executable models of complex biological phenomena – an approach we call Executable Biology – entail great promise for new scientific discoveries, shading new light on the puzzle of life. At the same time, this new wave of the future forces computer science to stretch far and beyond, and in ways never considered before, in order to deal with the enormous complexity observed in biology.
Complex human diseases, such as cancer, arise from the interaction between many different genes and the environment and are the main causes of death in Europe. In contrast to the successful identification of genes underlying rare monogenic diseases, studying the genetic basis of common complex diseases has been more challenging.
Evidence is mounting to suggest that the genetic background of the patient has a profound impact on a wide variety of complex disease in humans, but so far, the genetic mechanisms are mostly unknown. The nematode worm Caenorhabditis elegans is an important model for the identification of genes underlying complex diseases in humans. Indeed some key genetic processes in C. elegans have been implicated in human cancers and the simplicity of the C. elegans worm allows easy and detailed study of those processes.
My work at Microsoft Research Cambridge focuses on the usage of computer-based techniques originally developed for engineering computerized systems, to model and analyze the crosstalk between cancer signalling pathways during C. elegnas development. We work in a consortium with five other research groups from the UK, Netherlands and Switzerland, (EU FP7 consortium called PANACEA) and we aim to gain a better understanding of the mechanisms underlying complex human disease, in particular cancer. In this joint effort, we analyze large experimental data set through executable modeling of cell differentiation and cell death (apoptosis), which are fundamental in cancer development. Our modeling work has already provided several new biological insights, and we hope that this line of work will further help generate a broader description of disease states, and pave the way for novel drug targets and therapies for human cancers.