UNDER CONSTRUCTION: My personal website
A difficult question to answer, in my opinion. The main idea of Systems Biology (SysBio) is in the name - considering the whole system, not just its constituent parts, since the system is greater than the sum of its parts. An example of this thinking is to examine not just how one protein regulates the expression of a gene, but instead study how all the proteins in the cell regulate the expression of all the genes in the cell. At the same time, however, SysBio recognises that to understand the system, it is helpful to understand the component parts in a measureable and quantitative way, so a part of SysBio looks at the quantitative modelling of processes fundamental to biological systems.
‘Data! Data! Data! I cannot make bricks without clay’ - Sherlock Holmes, The Adventure of the Copper Beeches. Large and complex systems output data, which we can measure at increasingly large volumes and detail. To understand these systems as SysBio wishes to requires dealing with this data, which is often measured in terms of terabytes, in a structured and quantitative manner. So a large part of SysBio is the handling and quantitative analysis of big data. The insights drawn from analyses form hypotheses and models, which are ideally taken from the computer back into the laboratory to be experimentally validated, although this is not always possible. Data generated from experiments then heads back to the computers, to be analysed once more and geenrate more hypotheses and better models.
‘What I cannot create, I do not understand’ - Richard Feynmann. Systems biology and synthetic biology go hand-in-hand. To me, the end goal of SysBio is to build an understanding of whole systems complete enough that those systems can be replicated in simulations or in reality - the latter of which is the realm of synthetic biology. Ideally, this is done through a quantitative understanding of the systems’ elementary building blocks. Once we think we understand the systems, we can test and develop our understanding by recreating the systems. Indeed, Professor Jonathan Karr at the Icahn School of Medicine has already built a complete simulation of an entire cell, so that we can watch every detail of how it lives. Efforts are being made to simulate how cancers operate at multiple levels of their biology. These require an understanding of how cells and cellular processes work at a large-scale, systems level, as well as a fundamental understanding of the components that make them up, both of which systems biology seeks to provide.
Finally, SysBio is inherently disciplinary. We recruit chemistry and physics to help us understand the physical processes that underpin the building blocks of the biology we study. We use mathematics to analyse our datasets and build models that describe our understanding of the complex systems we study. We use computation for our analysis, data handling and modelling. The biological systems we study range from molecular biology to ecology to social interactions and networks.
For sure, there are some things that will be disputed in what I have said above, and some things I have missed. But that is my current opinion on what SysBio is.
I had come to Cambridge hoping to study both computer science and cell biology, but scheduling conflicts between the courses prevented this. As an alternative, I developed my coding skills beyond what I had self-learnt in high school by sourcing and cleaning unstructured data with a fintech start-up. Although I had interest in both computation and cell biology, it was only through a practical on modelling metabolism in my second year at Cambridge that I started to understand how computing approaches could be used to understand cell biology processes. I became interested particularly in the flow and integration of information along cellular networks.
Now, using systems-based and data-driven approaches, I am interested in understanding how intracellular and intercellular networks receive, process and integrate information from internal or external signals, and how these networks get hacked in cancer. I am particularly interested in how inherited factors that increase the likelihood of cancer, particularly pancreatic cancer, perturb cellular networks to prime them for disease, and how therapies, both conventional and alternative, can pre-empt and combat disease development. My research interests are driven by personal motivations - multiple relatives, including my mother, have developed pancreatic cancer, and now I will work for them and for the countless others who were, are or will be afflicted by one of the most deadly types of cancer.