https://centre.santafe.edu/complextime/w/index.php?title=Aging_and_Adaptation_in_Infectious_Diseases_II/Session_IV:_Short_Talks_for_Late_Arrival&feed=atom&action=historyAging and Adaptation in Infectious Diseases II/Session IV: Short Talks for Late Arrival - Revision history2024-03-19T02:39:21ZRevision history for this page on the wikiMediaWiki 1.35.6https://centre.santafe.edu/complextime/w/index.php?title=Aging_and_Adaptation_in_Infectious_Diseases_II/Session_IV:_Short_Talks_for_Late_Arrival&diff=4421&oldid=prevAmyPChen at 19:49, May 3, 20192019-05-03T19:49:42Z<p></p>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|Description=Drawing trajectories through disease space and visualizing how they vary</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|Description=Drawing trajectories through disease space and visualizing how they vary</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|Pre-meeting notes=We have a data set that follows mice suffering from malaria from start to finish. We’ve looked at the microbiota, circulating immune cells, cytokines and metabolites to produce a time series that follows about 800 variables. We can map many of these, like the metabolites, onto function based networks that were worked out decades ago. We can also make networks de novo based only on the data. We added variation to this system by measuring these parameters across 8 different mouse strains that show extreme variation in their survival as well as testing aged mice for one strain. The problem I now face is showing how these networks vary over strain space and age in a way that helps the viewer understand the biology behind these changes. Should we be modeling the trajectory of the infections through interesting phase spaces? Should we be observing how the networks change over time and genetic space, and how should we do that?</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|Pre-meeting notes=We have a data set that follows mice suffering from malaria from start to finish. We’ve looked at the microbiota, circulating immune cells, cytokines and metabolites to produce a time series that follows about 800 variables. We can map many of these, like the metabolites, onto function based networks that were worked out decades ago. We can also make networks de novo based only on the data. We added variation to this system by measuring these parameters across 8 different mouse strains that show extreme variation in their survival as well as testing aged mice for one strain. The problem I now face is showing how these networks vary over strain space and age in a way that helps the viewer understand the biology behind these changes. Should we be modeling the trajectory of the infections through interesting phase spaces? Should we be observing how the networks change over time and genetic space, and how should we do that?</div></td></tr>
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<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">|Presenter=JennyTung</del></div></td><td colspan="2"> </td></tr>
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<tr><td class='diff-marker'>−</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">|Pre-meeting notes=In social species, including our own, interactions with other members of the same species powerfully shape the environment that animals face each day. These interactions mediate the evolutionary costs and benefits of group living. My lab uses nonhuman animal models—particularly social primates—to study how the nature and timing of social interactions impact health and fitness-related outcomes. Recently, we used an experimental model for social status in captive rhesus macaques to show that social status-driven gene expression patterns carry a signature of past social history. We also found that high gene expression levels in inflammation-related pathways predict high social status in wild male baboons. Given that inflammation is widely thought to be costly, our findings suggest that both social adversity in rhesus macaques and competition for rank in male baboons may have long-term effects on health during aging. Together, our findings emphasize the importance of social context in shaping the relationship between social status and immune function. </del></div></td><td colspan="2"> </td></tr>
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</table>AmyPChenhttps://centre.santafe.edu/complextime/w/index.php?title=Aging_and_Adaptation_in_Infectious_Diseases_II/Session_IV:_Short_Talks_for_Late_Arrival&diff=4372&oldid=prevAmyPChen at 21:16, April 30, 20192019-04-30T21:16:54Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 21:16, April 30, 2019</td>
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<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">|Pre-meeting notes=We have a data set that follows mice suffering from malaria from start to finish. We’ve looked at the microbiota, circulating immune cells, cytokines and metabolites to produce a time series that follows about 800 variables. We can map many of these, like the metabolites, onto function based networks that were worked out decades ago. We can also make networks de novo based only on the data. We added variation to this system by measuring these parameters across 8 different mouse strains that show extreme variation in their survival as well as testing aged mice for one strain. The problem I now face is showing how these networks vary over strain space and age in a way that helps the viewer understand the biology behind these changes. Should we be modeling the trajectory of the infections through interesting phase spaces? Should we be observing how the networks change over time and genetic space, and how should we do that?</ins></div></td></tr>
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</table>AmyPChenhttps://centre.santafe.edu/complextime/w/index.php?title=Aging_and_Adaptation_in_Infectious_Diseases_II/Session_IV:_Short_Talks_for_Late_Arrival&diff=4336&oldid=prevAmyPChen: Created page with "{{Agenda item |Start time=May 1, 2019 10:00:00 AM |End time=May 1, 2019 12:00:00 PM |Is presentation=No |Presenter=JeanCarlson |Agenda sub-items={{Agenda sub-item |Presenter=A..."2019-04-29T17:08:16Z<p>Created page with "{{Agenda item |Start time=May 1, 2019 10:00:00 AM |End time=May 1, 2019 12:00:00 PM |Is presentation=No |Presenter=JeanCarlson |Agenda sub-items={{Agenda sub-item |Presenter=A..."</p>
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|Start time=May 1, 2019 10:00:00 AM<br />
|End time=May 1, 2019 12:00:00 PM<br />
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|Presenter=JeanCarlson<br />
|Agenda sub-items={{Agenda sub-item<br />
|Presenter=AndreaLGraham<br />
|Description=Biozzi mice and the antibody-lifespan connectio<br />
|Pre-meeting notes=Research on laboratory mice has provided much of what we know about the fundamental biology of the mammalian immune system. Yet because so few life-long experiments have been conducted on mice, we know remarkably little about immunosenescence in “the model mammal.” Classic work on Biozzi mice is an important exception. I will describe some of the experiments and key insights of the work of Biozzi and colleagues in the 1960s-1980s, especially on links between antibody responsiveness and organismal longevity. <br />
}}{{Agenda sub-item<br />
|Presenter=ShenshenWang;JimingSheng<br />
|Description=Understanding immunosenescence with an interplay of innate and adaptive immunity<br />
|Pre-meeting notes=In this collaborative project, we seek to understand various observations on immunosenescence, such as the breakdown of innate-adaptive collaboration and increasing variability of individual performance later in life. Earlier theoretical works have yielded much insight on the capacity of innate and adaptive immunity separately, yet these models with only one arm of the defense cannot explain the observed inflamm-aging (aging with inflammation). By considering the crosstalk between innate and adaptive responses, we build an integrative model that shows promising results consistent with experiments. Our preliminary findings highlight potential determinants of individual fates as well as the timing of inflammation dominance. <br />
}}{{Agenda sub-item<br />
|Presenter=DavidSchneider<br />
|Description=Drawing trajectories through disease space and visualizing how they vary<br />
}}{{Agenda sub-item<br />
|Presenter=JennyTung<br />
|Description=Social behavior and immune gene regulation: do past experiences accumulate to influence aging?<br />
|Pre-meeting notes=In social species, including our own, interactions with other members of the same species powerfully shape the environment that animals face each day. These interactions mediate the evolutionary costs and benefits of group living. My lab uses nonhuman animal models—particularly social primates—to study how the nature and timing of social interactions impact health and fitness-related outcomes. Recently, we used an experimental model for social status in captive rhesus macaques to show that social status-driven gene expression patterns carry a signature of past social history. We also found that high gene expression levels in inflammation-related pathways predict high social status in wild male baboons. Given that inflammation is widely thought to be costly, our findings suggest that both social adversity in rhesus macaques and competition for rank in male baboons may have long-term effects on health during aging. Together, our findings emphasize the importance of social context in shaping the relationship between social status and immune function. <br />
}}<br />
}}</div>AmyPChen