Thanks, but no thanks

I am posting from the q-bio Summer School, where we are enjoying many discussions about modeling. Several lecturers have advised the junior modelers attending the school, who are mostly graduate students and postdocs, to find an experimental collaborator. I appreciate the advice and the benefits of having an experimental collaborator, but I am usually quite irked by the reasons stated for seeking out opportunities to collaborate with an experimentalist. One reason I've heard many times is that modelers need an experimentalist to explain the biology to them and to help them read papers critically. It certainly could be useful to have a more experienced researcher aid in formulating a model, but that person might as well be a modeler familiar with the relevant biology. I don't subscribe to the idea that modelers need a collaborator to evaluate the soundness of a paper. To suggest so seems insulting to me. Modelers do need to consult experts from time to time to understand the nuances of an unfamiliar experimental technique, for example, but so do experimentalists. I am probably more annoyed by the popular sentiment that a collaborator is essential for getting predictions tested. If I were an experimentalist, I might be insulted by this idea. It's unrealistic to think that experimentalists are lacking for ideas about which experiment to do next. If your prediction is only appealing to your experimental collaborator, then maybe it's not such an interesting prediction? Modelers should be more willing to report their predictions and let the scientific community follow up however they may, partly because it's unlikely that your collaborator is going to be the most qualified experimentalist to test each and every prediction you will ever make. I think the real reason to collaborate with an experimentalist is shared goals and interests and complementary expertise. Finding such a colleague is wonderful, but it shouldn't be forced, and the absence of a collaborator shouldn't be an impediment to progress. If you have a good prediction, you should report it, and if you want to model a system, you should pursue that. Eventually, you will know the system as well as the experimentalists studying it, if not better. After all, it's your role as a modeler to integrate data and insights, to elucidate the logical consequences of accepted understanding and plausible assumptions, and to suggest compelling experiments. Finally, I want to speak to the notion that modelers should do their own experiments. I think that's a good idea if you want to be an experimentalist. If you want to be a modeler, be a modeler.

The alternate routes of allergic responses

claimtoken-5326210a079e4
As spring approaches for the northern hemisphere (or as a cat approaches from across the room), allergy sufferers might wonder about how their symptoms originate. In some ways, they're in luck. The molecules involved in allergic reactions have been studied for decades, and to some extent, we've developed a cohesive picture of how these molecules work together. However, the immune system is always full of surprises. Here are some newly-discovered and seemingly fundamental roles for proteins that we knew existed, but that rarely made appearances in review papers.

Background: The signals before the sneezes

In an article published in Science this February, Rivera and co-workers investigated how mast cells, which play a central role in allergic responses, can distinguish between different antigens. Antigens (also known as allergens) are molecules that bind to antibody-receptor complexes on the mast cell's surface. Antigen binding can initiate a process that leads to release of substances that induce inflammation and the symptoms of allergies. The two different antigens used in this study differ in their affinity, meaning how tightly they bind to receptors.

A typical view of signal initiation in mast cells via the high-affinity receptor for IgE, also known as FcεRI. The upper part of the image represents the space outside of the cell, and the bottom part represents the inside of the cell. Antibody-FcεRI (receptor) complexes are clustered by binding to an antigen. The kinase Lyn can then phosphorylate the receptor, meaning that phosphate groups are attached to multiple parts of the receptor. The phosphorylated receptor can bind another kinase, Syk, which goes on to phosphorylate multiple targets, including Lat.

Why does binding affinity matter? It has been proposed that antigens that bind more tightly, and stay in contact with receptors for a longer period of time, allow signaling to progress further and induce stronger cellular responses. One response that can be measured is overall receptor phosphorylation (see image), one of the earliest steps in signaling. The low-affinity antigen does indeed induce less receptor phosphorylation than an equal dose of the high-affinity antigen. However, if the amount of low-affinity antigen is 100x higher, total receptor phosphorylation is roughly equal. Which raises the question...

Are all responses affected in the same way?

The answer is no (which others have also found). One of the most important downstream players in this system is the adaptor protein Lat, which is phosphorylated to recruit an array of other signaling proteins. Lat undergoes less phosphorylation in response to the low-affinity antigen than the high-affinity one, even when receptor phosphorylation is equal. Surprisingly, the related but less well-studied protein Lat2 undergoes more phosphorylation in response to the low-affinity antigen. Lat2 phosphorylation depends, directly or indirectly, on a kinase called Fgr. Fgr's close relatives, Lyn and Fyn, are well-known for their roles in initiating mast cell signaling, but Fgr has largely gone under the radar. 

A possible clue about the origins of these differences is that even when total receptor phosphorylation (the total phosphorylation of multiple sites) is equalized, the low-affinity antigen causes more phosphorylation of at least one specific receptor site. So although total phosphorylation is the same, the contributions of individual sites may be different.

Finally, the authors considered how the low- and high-affinity antigens influence the messages that the mast cell sends to the rest of the immune system. The two antigens caused mast cells to release different types of signaling molecules (chemokines vs. cytokines), which induced different types of immune cells to arrive at the site of inflammation. So it seems that the Fgr/Lat2 pathway elucidated in this paper enables responses to low-affinity antigens, but these responses are qualitatively different from those induced by high-affinity antigens.

What we can learn:

• The idea of higher affinity -> more signaling -> stronger responses can explain some aspects of signaling, but is too simplistic to explain how specific responses are enhanced for low-affinity antigens.
• Lat2 and Fgr may play important roles that are distinct from their more famous protein relatives, Lat and Lyn.
• Several blanks are yet to be filled. Does Fgr act on Lat2 directly? How does the phosphorylation pattern of individual receptor sites differ with antigen affinity (although, that's likely to be experimentally challenging)? Although this system has been studied for a long time, there's evidently still a lot to learn about how quantitative differences between antigens lead to qualitatively different cellular behaviors.

References:

Suzuki, R., Leach, S., Liu, W., Ralston, E., Scheffel, J., Zhang, W., Lowell, C., & Rivera, J. (2014). Molecular Editing of Cellular Responses by the High-Affinity Receptor for IgE Science, 343 (6174), 1021-1025 DOI: 10.1126/science.1246976

McKeithan TW. Kinetic proofreading in T-cell receptor signal transduction. Proc Natl Acad Sci USA. 92:5042-6. (1995)

Liu ZJ, Haleem-Smith H, Chen H, Metzger H. Unexpected signals in a system subject to kinetic proofreading. Proc Natl Acad Sci USA 98:7289-94. (2001)

Do modelers have low self esteem?

When was the last time an experimental biologist experienced a manuscript rejection because the work in question didn't include modeling? As a modeler working in biology, I tend to be hesitant about trying to report work that doesn't include new data (from a collaborator), because it doesn't usually go well. Modeling without experimentation tends to be held in low regard, especially among modelers, which is a tragic irony. If physicists of the early 20th century had the same attitude as many of today's modelers and experimental biologists, "Zur Elektrodynamik bewegter Körper" would not have been published without its author first doing experiments to confirm his ideas or him finding an experimental collaborator to generate confirmatory data. I don't think that modelers should take a favorable view of every modeling study they come across but I wonder if we need to be more supportive of each other and allow more room for independence from collaborations with experimentalists. If a modeling study is based on reasonable assumptions and performed with care and it produces at least one non-obvious testable prediction, why should it not be reported immediately? It seems that some of us might be concerned that such reports will be ignored or that such reports are too untrustworthy, given all the complexities and ambiguities. It's true that models need to be tested, but it seems unlikely that someone able to build and analyze a model will also be the best person to test the model or to have a circle of friends that includes this special person. Indeed, I think the requirement to publish with data has led some modelers to produce predictions that are, let's say, "obvious," because this is the type of prediction can be confirmed easily. Let's be rigorous, but to a reasonable standard. Let's also be bold. Many experimental results turn out to be misinterpreted, or plain wrong. It's OK for models to be wrong too. Biological systems are complicated. We need models to guide our study of these systems. Most of the work being done in biology today is being performed without models. Until experimentalists start chiding each other for failing to leverage the powerful reasoning aids that models are, it makes little sense for modelers to criticize each other for work that doesn't include generation of new data.