 My name is Christine Mumry, I'm Professor of Developmental Biology at Leiden University Medical Centre. IPSCs are very useful for modelling disease and development because we can derive them from patients and we can turn the IPSCs into any cell type we like. So if the patient has a genetic disease, the mutation in the DNA, the differentiated cells from the IPS cells have the same mutation. What we hope is that they have the same phenotype, that means the same sort of symptoms as the patient has. And in that way we can screen for drugs that correct the symptoms in the cell and then hope that the same drug will work in the patient. So among the key findings are how, to what extent the IPS cells or adult stem cells can actually be made to be high throughput and a larger scale. And what I actually mean is it will be very expensive for an individual to have their own IPSCs or adult stem cells derived. And what we'd really like to see is that we can make thousands and thousands of IPS cells or adult stem cells from different individuals. So a kind of population study. That's also what pharmaceutical companies want to know. They're not really interested in testing, let's say, thousands of drugs on a single individual but they want to know how many, many individuals would react to the same drug at different doses. So that's a sort of population study and that's what they're hoping we will be able to develop. And we'll need robotics to do that. So we're going to need robots to make from thousands of patients, IPS lines or adult stem cells and then do the screening course. Academia is rather good at making different stem cell lines and looking for disease phenotypes and they also have access to patient material with the proper informed consent. But what academics are not very good at doing is making the assays we need to examine the cells very robust. And what I mean by that is that we can do an experiment three times as we are often required to by scientific publications. What happens if you do those assays for many years at an end using different reagents and different cell lines? And that's what industry does very well, interpreting the results properly, making the right software to analyze the data, putting the data together and actually ensuring that for many years on end they can repeat the assays with different drugs and different systems. So upscaling and validating is what we need industry to help us with. There are a number of challenges to the fields from you could say very trivial to actually quite major. So if I would go to the trivial ones in some sort of way, it's finding the right sort of personnel. So to do this type of work you need to be highly educated. You really need to understand what you're doing. You need the right training and it's quite difficult for academia at least to attract the right sort of young people into the field that they will stay. Many of them choose to go to different aspects of industry. They go to science writing, they go to work for ministries and what we need them to do is actually to stay for a bit longer at the bench and do the experiments in a very skilled way. In the next generation art we also need new leaders to bring the field forward and to develop new technologies and implement them. The more serious technical issues are related to how do we actually model aging. So from adult stem cells or IPS cells we can get let's say young adult differentiated cells. But we can't actually mimic aging. So if a disease is only evident when you're 60, 70 or 80 we can't get cells that are mimicking that sort of age. We also can't get cells that mimic diseases of aging. Let's say it's quite hard to do them for dementia or loss of vitality of cells. So aside from regular maturation we're going to need to have new methods to look at the let's say the challenges of life. What do we get from smoking, from sunlight, from drinking. How do we mimic those environmental factors that predispose to disease.