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Is there such a thing as too much data? If we don’t have the tools to use it, then the answer is yes. In fields like genetics, neuroscience, and cancer biology, we’ve built tools that have enabled us to acquire mountains of data. But this sophistication has also left us with the challenge of using it all effectively. It's a problem that cell biologist Jasmin Fisher is trying to solve at the Microsoft Research Lab in Cambridge.
“Everyone now appreciates that we have huge amounts of information - but how do we read it, how do we understand what it all means?” Fisher tells WIRED. “You must have some serious means of machine power.”
As associate professor of systems biology at the University of Cambridge, and senior researcher at Cambridge’s Microsoft Research Lab, Fisher is mastering machine power to solve one of our biggest chronic health threats: cancer.
Her research relies on the vast amounts of available data on cell biology, generated over the last several decades and plugged into computers to produce models that mimic how cancer cells behave. She calls her research ‘executable biology’, a term she coined: it’s based on a presumption that in order to do everything a cell does - grow, proliferate, die - it ‘executes’ a programme of steps to get there.
“The idea behind executable biology is to treat those behaviours as a set of algorithms. So we’re writing computer programmes that will mimic their biological behaviours,” she says. In this way, she can reverse engineer the processes and trace the steps cells took to become what they are. “We do this with the mindset that if we understand the programmes they’re executing, we’ll also know how to fix them when they’re not operating correctly” - such as when cells proliferate, and form tumours.
This has led Fisher to explore problems like how best to target cancer cells with drugs. “We can intervene with a programme [in a model] and manipulate it - mimicking the effect of drugs to figure out where we want to target the cells, in order to make them behave in a desired rather than undesired way,” she says. In 2015, her lab helped build a model that showed how the processes behind abnormal blood production can give rise to cancers like leukaemia and lymphoma. The model provided a framework for doing rapid, simulated experiments that helped identify drugs targets. “You can simulate situations that you haven’t yet tested experimentally,” Fisher explains.
Through thousands of rapid-fire simulations like these, researchers can reveal how cells would behave in different scenarios. The more data that’s factored in, the higher-resolution the models become, allowing better and quicker predictions about cell behaviour. It could also direct researchers towards more promising outcomes, by getting through a lot of the experimental trial and error that can slow down scientific progress in the lab. These are just some of the reasons why modelling can trump traditional, lab-based experiments. “It really saves a lot of time, money, animals, and resources. You can do it in the order of seconds or minutes, and get insights into what it is that you really want to test experimentally,” Fisher says.
So, should we be moving toward a future where cloud computing replaces traditional experiments? In Fisher’s opinion, no. After all, her computational work depends on data produced in the lab. “Everything we do can’t replace experimental biology; it just complements it in a very smart and essential way,” she says. “But I think it’s not just a nice ‘added value’ either - it’s absolutely essential, because of the complexity of the puzzle.”
For personalised medicine, in which treatments are tailored to individual patients, this approach holds special promise; that’s true of cancer especially, because of the huge variability of the disease.
Acute myeloid leukaemia, a disease that shows high levels of drug resistance, is one example. In recent research carried out with pharmaceutical company AstraZeneca, Fisher was able to explore tailored treatment models for the disease. It proved the approach can be used to investigate and create "specialised treatments for different patients with the same disease”. It's the first steps towards truly personalised, effective cancer treatments.
The executable approach applies to other types of cancer too, like glioblastoma, which causes highly malignant tumours in the brain. “You can really tailor models to be specific to the particular genetics of a patient,” Fisher says. “Once you have such a model, you can simulate the effects of radiation, or chemotherapy, and then see how to shrink the tumour on a personalised level.” This will aid clinicians in making more educated guesses about how to treat patients - and much more rapidly than possible in a lab, with a sliver of tumour tissue on a petri dish. “It’s based on all the knowledge that we’ve gathered [through experiments], but it allows us to get this 50,000-foot view that we don’t get any other way.”
The key to moving the technology in this direction, Fisher believes, is to make it accessible to the people who need it most, like biologists and clinicians. “There’s a lot of emphasis - and this is one of the major challenges - to really build these tools so that this approach can be used as a mainstream technique.” To this end, Fisher and colleagues from the University of Leicester and Cambridge have developed the Bio Model Analyzer in partnership with AstraZeneca, a tool initially devised to explore the mechanisms of drug resistance in people suffering from acute myeloid leukaemia.
This year, they made the software open-source by launching it on GitHub. It’s designed to be user-friendly, so even those who lack a computational background can easily use it to feed vast amounts of data into the models to map out cellular processes. “We want this to become a mainstream technique in every lab,” Fisher says.
She hopes it will be part of a cultural shift occurring in the biological sciences, as people come to grips with the expanding role that big data and cloud computing can play in research. “There are cultural leaps that everyone needs to take, and it’s not something that happens overnight. But I think everyone appreciates that there’s a need for it,” she says. “These are challenging times, but they’re also the most interesting times. I just feel that we've reached a point where we have so much information to play with, which is super exciting.”
Want to know more? Jasmin Fisher will be speaking at this year's WIRED Health conference in London. Join hundreds of healthcare, pharmaceutical and technology influencers and leaders at the fourth annual event on March 9 at 30 Euston Square. Buy tickets and learn more here.
This article was originally published by WIRED UK
