In a laboratory setting, researchers, postdocs, and graduate students can find themselves alone and lacking confidence in the face of some common challenges. Those difficulties are often lumped together as an inherent part of pursuing a research career, but we think they could be divided into two types—challenges that are hard in a good way or in a bad way.
Good-hard challenges include rigorous tasks that lead to scientific discovery, and can be surmounted with discipline and focus, while bad-hard challenges are those that are extraneous to the research process and can lead to debilitating personal stress, poor self-image, and stagnation in the work.
We’ve created a partial list of both types to help researchers differentiate between the two. Read more at The Chronicle of Higher Education.
Showing posts with label article. Show all posts
Showing posts with label article. Show all posts
Thursday, February 5, 2015
Friday, August 22, 2014
Elucidating missing links of the TCR signaling network
Just published:
Phosphorylation site dynamics of early T-cell receptor signaling. LA Chylek, V Akimov, J Dengjel, KTG Rigbolt, WS Hlavacek, B Blagoev. PLOS ONE 9, e104240
Stimulation of the T-cell receptor (TCR) can trigger a cascade of biochemical signaling events with far-reaching consequences for the T cell, including changes in gene regulation and remodeling of the actin cytoskeleton. A driving force in the initiation of signaling is phosphorylation and dephosphorylation of signaling proteins. This process has been difficult to characterize in detail because phosphorylation takes place rapidly, on the timescale of seconds, which can confound efforts to decode the order in which events occur. In addition, multiple residues in a protein may be phosphorylated, each involved in distinct regulatory mechanisms, necessitating analysis of individual sites.
To characterize the dynamics of site-specific phosphorylation in the first 60 seconds of TCR signaling, we stimulated cells for precise lengths of time using a quench-flow system and quantified changes in phosphorylation using mass spectrometry-based phosphoproteomics. We developed a computational model that reproduced experimental measurements and generated predictions that were validated experimentally. We found that the phosphatase SHP-1, previously characterized primarily as a negative regulator, plays a positive role in signal initiation by dephosphorylating negative regulatory sites in other proteins. We also found that the actin regulator WASP is rapidly activated via a shortcut pathway, distinct from the longer pathway previously considered to be the main route for WASP recruitment. Through iterative experimentation and model-based analysis, we have found that early signaling may be driven by transient mechanisms that are likely to be overlooked if only later timepoints are considered.
Phosphorylation site dynamics of early T-cell receptor signaling. LA Chylek, V Akimov, J Dengjel, KTG Rigbolt, WS Hlavacek, B Blagoev. PLOS ONE 9, e104240
To characterize the dynamics of site-specific phosphorylation in the first 60 seconds of TCR signaling, we stimulated cells for precise lengths of time using a quench-flow system and quantified changes in phosphorylation using mass spectrometry-based phosphoproteomics. We developed a computational model that reproduced experimental measurements and generated predictions that were validated experimentally. We found that the phosphatase SHP-1, previously characterized primarily as a negative regulator, plays a positive role in signal initiation by dephosphorylating negative regulatory sites in other proteins. We also found that the actin regulator WASP is rapidly activated via a shortcut pathway, distinct from the longer pathway previously considered to be the main route for WASP recruitment. Through iterative experimentation and model-based analysis, we have found that early signaling may be driven by transient mechanisms that are likely to be overlooked if only later timepoints are considered.
Thursday, June 5, 2014
Pathetic thinking
Modelers with shared biological interests can have varying opinions about what a useful model looks like and the purpose of modeling, or rather the opportunities that exist to perform important work in a particular field.
In a recent commentary, Jeremy Gunawardena [BMC Biol 12: 29 (2014)] argues that models in biology are “accurate descriptions of our pathetic thinking.” He also offers three points of advice for modelers: 1) “ask a question,” 2) “keep it simple,” and 3) “If the model cannot be falsified, it is not telling you anything.” I whole-heartedly agree with these points, which are truisms among modelers; however, in my experience, the advice is followed to an extreme by some researchers, who interpret “ask a question” to mean that every model should be purpose-built to address a specific, narrow question, which ignores opportunities for model reuse, and who interpret “keep it simple” to mean that models should be tractable within the framework of traditional approaches only, ignoring new approaches that ease the task of modeling and expand the scope of what’s feasible. Some extremists seem to even hold the view that the mechanistic details elucidated by biologists are too complex to consider and therefore largely irrelevant for modelers.
Gunawardena may have given these extremists encouragement with his comment, “Including all the biochemical details may reassure biologists but it is a poor way to model.” I acknowledge that simple, abstract models, which may focus on capturing certain limited influences among molecular entities and processes and/or certain limited phenomenology, have been useful, and are likely to continue to be useful for a long time. However, there are certainly many important questions that can be feasibly addressed that do depend on consideration of not “all” of the biochemical details but rather on consideration of more, or even far more, of the biochemical details than usually considered by modelers today.
The messy details would also be important for the development of “standard models,” which do not currently exist in biology. Standard models in other fields, such as the Standard Model of particle physics, drive the activities of whole communities and tend to be detailed, because they consolidate understanding and are useful in large part because they identify the outstanding gaps in understanding. Would standard models benefit biologists?
An affirmative answer is suggested by the fact that there are many complicated cellular regulatory systems that have attracted enduring interest, such as the EGFR signaling network, which has been studied for decades for diverse reasons. A comprehensive, extensively tested, and largely validated model for one of these systems, meaning a standard model, would offer the benefits of such a model (which have been proven in non-biological fields) and would aid modelers by providing a trusted reusable starting point for asking not one question but many questions.
The extremists should take note of the saying attributed to Einstein, "Everything should be as simple as possible, but not simpler."
Gunawardena J (2014). Models in biology: 'accurate descriptions of our pathetic thinking'. BMC biology, 12 (1) PMID: 24886484
Bachman, J., & Sorger, P. (2011). New approaches to modeling complex biochemistry Nature Methods, 8 (2), 130-131 DOI: 10.1038/nmeth0211-130
Chelliah V, Laibe C, & Le Novère N (2013). BioModels Database: a repository of mathematical models of biological processes. Methods in molecular biology, 1021, 189-99 PMID: 23715986
In a recent commentary, Jeremy Gunawardena [BMC Biol 12: 29 (2014)] argues that models in biology are “accurate descriptions of our pathetic thinking.” He also offers three points of advice for modelers: 1) “ask a question,” 2) “keep it simple,” and 3) “If the model cannot be falsified, it is not telling you anything.” I whole-heartedly agree with these points, which are truisms among modelers; however, in my experience, the advice is followed to an extreme by some researchers, who interpret “ask a question” to mean that every model should be purpose-built to address a specific, narrow question, which ignores opportunities for model reuse, and who interpret “keep it simple” to mean that models should be tractable within the framework of traditional approaches only, ignoring new approaches that ease the task of modeling and expand the scope of what’s feasible. Some extremists seem to even hold the view that the mechanistic details elucidated by biologists are too complex to consider and therefore largely irrelevant for modelers.
Gunawardena may have given these extremists encouragement with his comment, “Including all the biochemical details may reassure biologists but it is a poor way to model.” I acknowledge that simple, abstract models, which may focus on capturing certain limited influences among molecular entities and processes and/or certain limited phenomenology, have been useful, and are likely to continue to be useful for a long time. However, there are certainly many important questions that can be feasibly addressed that do depend on consideration of not “all” of the biochemical details but rather on consideration of more, or even far more, of the biochemical details than usually considered by modelers today.
The messy details would also be important for the development of “standard models,” which do not currently exist in biology. Standard models in other fields, such as the Standard Model of particle physics, drive the activities of whole communities and tend to be detailed, because they consolidate understanding and are useful in large part because they identify the outstanding gaps in understanding. Would standard models benefit biologists?
An affirmative answer is suggested by the fact that there are many complicated cellular regulatory systems that have attracted enduring interest, such as the EGFR signaling network, which has been studied for decades for diverse reasons. A comprehensive, extensively tested, and largely validated model for one of these systems, meaning a standard model, would offer the benefits of such a model (which have been proven in non-biological fields) and would aid modelers by providing a trusted reusable starting point for asking not one question but many questions.
The extremists should take note of the saying attributed to Einstein, "Everything should be as simple as possible, but not simpler."
Gunawardena J (2014). Models in biology: 'accurate descriptions of our pathetic thinking'. BMC biology, 12 (1) PMID: 24886484
Bachman, J., & Sorger, P. (2011). New approaches to modeling complex biochemistry Nature Methods, 8 (2), 130-131 DOI: 10.1038/nmeth0211-130
Chelliah V, Laibe C, & Le Novère N (2013). BioModels Database: a repository of mathematical models of biological processes. Methods in molecular biology, 1021, 189-99 PMID: 23715986
Friday, March 14, 2014
The alternate routes of allergic responses
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.
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.
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)
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