They come from the cover of of a new book, “Computational Methods in Cell Biology,” co-edited by chemical engineering professor Anand Asthagiri. Okay, so it may not be beach reading, but I assure you it covers a really interesting topic.
“Computational biology has been around for decades,” said Asthagiri in an interview yesterday. But only recently have we begun to realize the full potential of its utility. And by realize I don’t mean like we woke up and a light bulb turned on, I mean realize in the sense that it’s actually starting to happen.
Which is why Asthagiri thought it was so important to devote a volume in one of cell biology’s leading technical series to the topic. He approached the editors with his idea to develop a computation handbook for experimentalists in 2009 and it finally reached the light of day last month.
“It will be a very good resource especially now,” said Asthagiri. “It’s hard to train in cell biology without being at least aware of how computation can be used as a tool even if you’re not going to use it yourself at least to know what it’s capable of.”
The reason comes down to the emerging field of “systems biology.” Network science, which I so love to talk about on here, isn’t limited to just social networks and mobility patterns. It’s starting to look like the “reductionist” approach to science is no longer useful in many areas. Cell biology is no exception.
You can’t look at a single protein and say it has a single effect on the rest of the body and that if you suppress it with this or that drug it will work the same way for everyone. The reason is because the molecular, cellular and multi-cellular systems that keep us alive (and effectively are us) are deeply interconnected. Change this protein and effect that protein. Mess with this tissue, notice changes in that tissue.
Okay great, so where does computation come into all of this? Well, if you want to look at a system, even the system of a single cell, you’re talking about a whole lot of data. You can look at protein levels, DNA sequencing data, imaging data you name it. Each of these presents a single technical question with thousands of data points alone. Then if you try to integrate them with each other, as is often necessary when you take a systems approach….you see where this is going.
Without computation you can’t have systems biology. And without systems biology we can’t answer the kinds of questions that are now arising around treating disease and maintaining health.
Asthagiri and his co-editor, Adam Arkin, chose to organize the book into three sections: molecular, cellular and multicellular. Then within each of those sections the look at a variety of biological problems that can be addressed computationally. Each chapter tackles a different question, giving both a high level and more in-depth discussion of the computational tools involved. The authors of the chapters are leaders in their various fields. Some are experimentalists. Some are computationalists. Some are both (Asthagiri is both).
Computational and systems biology often involves a lot of collaboration. Because experimentalists and computationalists are not always one in the same. Already, just producing the book has spurred a new collaboration for Asthagiri, who is now working with Chapter 13’s author, James Glazier, who developed a software package called Compucell 3D, which allows for the dynamic modeling of cells and groups of cells.
So, like I said, not beach reading but ridiculously cool.