Editor of Open Access Government, Jonathan Miles, spoke to Juan Meza at the National Science Foundation about the launch of four new centres to bring mathematical perspectives to the biological search for the Rules of Life
The National Science Foundation (NSF), in partnership with the Simons Foundation, in May 2018, launched four new centres to bring mathematical perspectives to the biological search for the Rules of Life. Collectively, the centres will produce a new generation of scientists equipped to tackle questions that cannot be answered today.
The NSF-Simons Research Centers for Mathematics of Complex Biological Systems aim to explore how information encoded in DNA results in complex organisms with diverse forms, functions and behaviours when it is manipulated by changing environments across multiple time scales.
The NSF-Simons Centers will enable scientists to understand with unprecedented clarity, how genes translate into so many diverse phenotypes. This endeavour will help the NSF address one of its 10 Big Ideas, Understanding the Rules of Life: Predicting Phenotype, which explains how the Rules of Life are expressed across many scales of structure and time.
In this question and answer interview with Juan Meza at the National Science Foundation (NSF), we learn that such knowledge may lead to a predictive framework for understanding the pathways that lead from the DNA within a cell to the myriad expressions of an organism in its environment. We also discover that maths is more than just proofs or calculations but is a really powerful tool that helps us study nature.
Thank you for taking the time to speak to me today. First, tell us how the National Science Foundation (NSF), in a partnership, has launched four new centres to bring mathematical perspectives to the biological search for the Rules of Life?
The partnership came about through a lot of discussions, both internally and externally. We had a workshop here at the NSF and we followed that up with discussions with other parts of the organisation. Throughout these discussions, we realised that we had reached a tipping point at which the combination of mathematics, data and biology could have a big impact on our understanding of biological processes. Thus, we came up with a couple of goals, the first of which was to enable innovation and collaborative research at the intersection of mathematics and three different areas of biology: molecular, cellular and organismal.
A secondary goal was to establish new connections between the two disciplines and to promote the interdisciplinary education and workforce training that we felt was needed to proceed in the field.
Thank you for that very interesting answer. What kind of knowledge will the centres explore and what could this lead to, for example, a predictive framework for understanding the pathways that lead from the DNA within a cell?
The main focus is to explore the interface between mathematics and biology. All the centres are going to be involved in trying to develop a better understanding of these thought processes because they are very complex and involve the entire spectrum going from the genotype to the phenotype. But each centre is going to have a goal or a specific set of goals that are all slightly different
For example, The NSF-Simons Center for Multiscale Cell Fate Research at the University of California, Irvine, will look at what determines cell fate – the differentiation of cells into specific biological cell types. The best way to describe it is to say, “well, how do individual cells develop into specific types of tissue?” So you start with a single cell and then after a while, you have a liver cell, a heart cell or a muscle cell – how does that happen? We know that these cells receive signals from other cells and that will tip them off and let them know that they should be doing certain things. But how, when and where these signals occur is still something of a mystery.
Many thanks for this insight. Now, I am very intrigued by your comment that “many people think of maths as proofs or calculations, but it’s so much more” and wondered if you could develop this thought when it comes to applying mathematics to the complex processes that underline biology?
I sometimes get a laugh out of this because I think that many of us develop a maths phobia and what we remember is that in this subject you need proofs. For example, think back to when you took Geometry and wanted to show that one triangle was the same as another one. That was a sort of proof that we had to come up with. Or sometimes we think in terms of algebra, so, many of us dreaded the old question of, “how do you solve this quadratic equation?” We’re given these formulas and calculations – that’s what we think of when we think of maths. But I like to think of it as something completely different in that maths’ power really comes in because we can develop models and use them for both describing and predicting things. What that does, is that it allows us to really understand complex processes.
We state the problem in terms of mathematics – that’s the first step but then that allows you to bring in literally hundreds or maybe even thousands of years of knowledge to bear on the problem. And it allows us today to provide accurate weather forecasts. It allows us to provide links between environmental and genetic factors and certain diseases. Each of us uses maths every day whenever we make a phone call or make a purchase online – using number theory to encrypt your messages back and forth.
Now in biology, it’s slightly different in the sense that there are a lot of processes and mechanisms that are very complex, but they lend themselves easily to mathematical modelling. In the example I just gave with the cell fate, you could model how a signal is sent from one cell to another. And then you want to see how these signals are interconnected with a network of cells. You might think of it in the same way you might model electricity by moving it through the power grid. Essentially, it is the same kind of mechanism.
Here, the applications are really quite different. But the mathematics is quite similar so that that gives us the power of the maths to be able to apply it to many different applications, some of which are very complex.
Tell us how the centres will build research capacity at the interface between mathematics and biology through cross-disciplinary training of students and postdoctoral associates, for example.
I’m really excited about the training of a new generation of researchers and the young folks who could come in and work well in both worlds – in biology and mathematics. I think it’s too easy to spend all of our time in one small part of our research but I predict the future major advances are going to occur at the interface of the scientific disciplines, for example, in maths and biology. That’s why I think it’s important to train the next generation of researchers because we want them to be able to work in multiple disciplines and to be able to talk across these different disciplines. That’s where the core of the new ideas will be coming from.
As an example of the four centres, tell us about the aims of the NSF-Simons Southeast Center for Mathematics and Biology and how they will address how genetic information impacts phenotypic traits.
We want to develop an interdisciplinary collaborative team to model these complex biosystems. This centre is actually a little different than the other ones. What they propose is to have seven different projects and each of them will have a pairing so there will be one biosystem experimentalist working side by side with one mathematician. And each team will look at a particular problem in the genotype to phenotype landscape. Just to give you one example, one of the things we’re going to look at is why that phenotype is consistent despite variations in both genotype and the environment. In other words, why is biology so robust? But we have a genotype, and depending on the environment, it will do a certain thing.
You can look at the example of the development of a stem cell to a muscle cell – the environment is not the same each time this happens yet we always get the same result at the end. Somehow biology and life have managed to develop a system that despite many differences in the environment, we still end up with a fairly robust system that tends to give you the same result after many instances.
How will the NSF-Simons Centers enable scientists to understand, in unprecedented clarity, how genes translate into so many diverse phenotypes?
I’d like to point out that there have been some incredible advances in technology during the last 10 or so years. And it has completely changed how we address biology problems. If you take a look at, for example, gene sequencing or new imaging technologies that led to having access to unprecedented amounts of data, much of which is very high-quality data. We’re able to get sequencing on single cells at this point and we have incredible detail on imaging. So that sort of points out and there is a lot of information here but how do we take advantage of this wealth of data?
What we believe is that by combining the data with the mathematical models, such as the ones that I mentioned earlier, it’s going to allow us to develop predictive models. These models will have been validated with real data, so what that allows us to do is to then be able to predict certain things and then we can go back and check them. The models will help us understand where we might look for more detail. That will allow us to go back and do more experiments and fill in the gaps and that means a more accurate and more predictive model. It is a cycle, in fact, that gives us more and better models and higher-quality predictive models.
Is there anything you would like to add?
Well, first of all, thank you for the opportunity to talk with you. I think it is wonderful to share our exciting results but I would like to add that we’re only at the beginning of an incredible and really important journey. Nobody knows how this is going to play out but I think the potential for revolutionary advances in biology is tremendous, especially when it’s coupled with mathematics and all of the data that we have.
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