What can modeling do for you?

In the blog so far, we've talked often about computational models - how they're made, what can go wrong, and what they could be like in the future. But what exactly are they - and why should anyone (especially biologists) care?

A model is a representation, or imitation, of a system that is difficult to examine directly. Biologists already use models all the time. For example, we'd like to understand biological process in humans, but since most of us can't experiment on humans, model organisms and cell lines are used instead. "Model" also refers a working hypothesis about how a system functions, which is often presented as a cartoon diagram.

A cartoon model for how the Shc1 adaptor protein acts at different stages of signaling.

Zheng et al., Nature 2013

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Like a cartoon model, a computational model is created based on what a person knows, or on what they hypothesize. The difference is that instead of drawing a picture, which tends to be vague and qualitative, they make concrete and quantitative statements about how molecules interact. They then use this information to create a set of equations or a computer program, which is used to simulate system behavior. In modeling of chemical kinetics, the goal of simulation is often to see how certain outputs (like protein concentration) change over time, or under different conditions. So, like a model organism, a computational model can be used to make new discoveries with potential relevance to real-world questions.

Here are some of the reasons why I think biologists can get excited about what models can offer:

1. A roadmap for pursuing experiments. Why do we do experiments? Often, it's to test a hypothesis. The more complicated the hypothesis, the greater number of experiments one could try, and the more involved each experiment might be. At the same time, even the most diligent of us want to optimize, and do minimum work for maximum information. Models can potentially help identify which tests would be most meaningful for supporting or disproving a hypothesis. 
2. A way to make sense of complicated or conflicting data. Sometimes it turns out that two seemingly contradictory ideas are actually compatible if you think about the quantitative details, which is exactly what models are good for. 
3. Consolidating and testing knowledge about a system. A typical experimental study provides information about one or a few interactions. Models can help us put together multiple pieces of information, like assembling a jigsaw puzzle, to form a more complete picture. Furthermore, by simulating such a model and comparing it to experimental data, interesting discrepancies can sometimes be identified. In other words, we can see whether we have enough puzzle pieces, or if we need to find more through additional experiments. 

At the same time, we need to remember that models won't magically provide the answers to everything. A model that simply recapitulates your expectations could be appealing; however, a model's real worth is in its ability to generate non-obvious, testable predictions. If you're going to start modeling or are thinking about starting a modeling collaboration, try to first learn about about what models can and can't do.

So what does the word "model" mean to you? And what do you think they can be used for?

Extreme writing

Out of the blue one day, Pieter Swart stopped by my office, and for some reason, the conversation turned to extreme programming, a practice that Pieter and his colleagues used in their development of NetworkX. One aspect of extreme programming is programming in pairs, or pair programming. Two programmers sit at one workstation. One, the driver, types. The other, the observer, reviews what is typed. Because of Pieter's enthusiasm, I tried it, but for writing, not programming. It turns out that pair writing works very well, at least for me with certain writing partners. If you've ever had writer's block, extreme writing will cure it. If you're the observer, you're off the hook - you just need to give your attention to what's being typed. If you're the driver, a pause will usually lead immediately to a discussion with the observer and a quick return to steady progress, or the observer will just deliver a coup de grace and take over the keyboard. Changing roles occurs frequently. If you haven't tried pair writing, give it a try. It helps to work with a large monitor in a comfortable but isolated and confined environment (to limit the possibilities of escape), where loud conversation will not disturb anyone.

How to make yourself understood across field boundaries

It seems like everyone these days is excited about "interdisciplinary science", which is much like regular science but with a longer list of affiliations. Working together means talking together, which includes making presentations that appeal to people in different fields. Is there anything to keep in mind beyond generic advice about giving a talk (develop an outline, make eye contact, don't mumble, etc)?

Here are two pitfalls that I've noticed when people speak for "interdisciplinary" groups:

• Tunnel vision. A speaker ignores the diverse backgrounds of audience members and assumes they all share his or her knowledge and interests. As a result, the speaker doesn't provide enough basic information for audience members to understand the talk, or to appreciate why the talk matters. 
• Self-effacement. A speaker goes too far in catering to an audience and loses their own point of view as a result. I once heard a talk from a bioinformatics researcher. They seemed to think their audience contained only chemists and, furthermore, that no one would want to learn anything about biology. As a result, the speaker tried to avoid touching any biological details. The result? A deluge of vagueness and abstraction. 

What can we do to avoid these extremes so that speakers and audiences can meet in the middle? 

1. Scope out the audience beforehand. Learn about your potential listeners. Think about what they're likely to know or not know, what they might feel strongly about, and why your talk is relevant to them. They might have more in common with you than you think. People are usually eager to latch onto something that connects to their interests, even tangentially. It's usually a good thing, but beware... 
2. Stay in the driver's seat.  A handful of times, "I know about what the speaker is talking about" can devolve into "I need to prove that I know more about it than she does, especially since I consider myself more of a specialist".  As a speaker, it's your job to address questions/comments thoughtfully. However, if someone tries to derail you - and you'll know it when you see it! - it's also your job to stay on track and remind them that you're the one giving a talk, which is different from a one-on-one meeting. 
3. Make good use of pictures and examples, which can help make ideas more concrete. A well-made diagram will make descriptions/equations/algorithms more approachable, especially to someone new to the subject. 
4. Appeal to shared problem-solving tendencies. If you're talking to scientists - or to humans, generally* - it's likely that even if you have different backgrounds, you share an instinct for solving problems. Try to give your audience the basic information needed to answer a question. Then give them a chance to work out an answer before showing them your results. No need to demand a verbal response (which can get awkward), but you want their brains to work while they listen. It'll keep their attention and make your research process more relatable. 
*If you're talking to non-humans, I would love to hear about that.
5. Have some faith in your listeners. I've come across many blanket statements, like that biologists always quail at the sight of equations, or that one must never utter gene names in the presence of a physicist. Although these statements may allude to general preferences, we need to remember that people aren't defined by what subject their degree is in and, related to point #4, people like to learn. Try to gently and non-patronizingly lead people out from their comfort zone. If you can show them that something they thought was incomprehensible is actually not so bad, you'll help them feel smarter rather than dumber, which is a big step towards bringing together people with diverse backgrounds. 

What are your thoughts? Have you had any interesting experiences when presenting your work to others?  

The alternate routes of allergic responses

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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)

The Center for Nonlinear Studies

In 2007, the q-bio Conference was inaugurated through the initiative of the Los Alamos Center for Nonlinear Studies, which is also known as CNLS. A few days ago a nicely produced YouTube video about CNLS became available, and the Center's sponsorship of the conference is mentioned. The video might be of interest to past or future attendees of the q-bio Conference who want to know more about CNLS, which supports a postdoc training program in the area of quantitative biology. The current director of CNLS is Bob Ecke, who is featured in the video. Bob was instrumental in obtaining the funding needed to launch and then sustain the conference series, as well as the affiliated q-bio Summer School. Bob is retiring soon and 2014 may be the last year that we will see him at the conference, where he usually welcomes attendees to New Mexico and says a few words about CNLS. If you happen to see him, or even if you don't, and you appreciate the conference and summer school, it would be nice to let him know. I expect that he would appreciate hearing about the impact of CNLS on q-bio scientists and their research.