而且不同环境下的蚁群会使用

图片 1
科学技术

4月25日下午,上海生科院研究生部主办的第八十三期“第一作者讲坛”在中科大厦一楼报告厅举行。健康所胡国宏研究员指导的博士研究生王宇峰同学为大家带来了题为“What
can metastatic cancer cells teach
us?——骨转移的机制对我们的启迪”的精彩报告。
DLC1基因位于人染色体8p21,最初因其经常在肝细胞癌中被发现丢失而被命名为Deleted
in Liver
Cancer-1。DLC1是一种RhoGTPase蛋白的GTP水解酶活性活化蛋白(RhoGTPase
Activating
Protein,RhoGAPs),能通过其RhoGAP结构域失活RhoGTPase家族成员如RhoA,RhoB,RhoC和Cdc42。王宇峰等完成的研究表明DLC1在乳腺癌骨转移过程中起着关键性的负调控作用。DLC1功能的行使依赖于它失活RHO-ROCK通路,继而抑制TGFβ诱导的SMAD3
linker
region磷酸化。DLC1对后者的抑制降低了TGFβ诱导的PTHLH的转录和分泌,从而减少骨转移微环境中成熟的破骨细胞,最终阻碍了乳腺癌的破骨性转移。同时他们用一种已通过FDA审批的ROCK抑制剂Fasudil治疗乳腺癌骨转移,结果显示Fasudil能有效阻遏骨转移的进程,这暗示这种药物或许具有进行临床试验的价值。该研究首次证明DLC1-RHO信号通路在调控乳腺癌骨转移微环境中的重要作用,也为临床上治疗乳腺癌骨转移提供了新思路。其相关研究论文已于2014年3月3日在《The
Journal of Clinical Investigation》上发表,并获得了媒体的大量报道。
在报告中,王宇峰同学简洁地阐述了该课题的研究背景,有条理地讲解了完成该课题所取得的成果,随后总结了这项研究成果以及它带来的意义,最后还与在场的同学们分享了自己从肿瘤转移过程领悟出来的对科研的启示。胡国宏研究员在之后的总结中肯定了王宇峰同学对于完成该课题所做出的努力,并以完成这项课题经历过的困境为例和大家分享了自己对于科研的领悟,认为在研究课题过程中多尝试坚持不放弃是走向成功的重要品质之一。并对什么才是成功和研究生应孜孜追求的最终目的做了深入的阐述。在场的同学们认真仔细地聆听了报告,一些同学积极参与提问和讨论,提出了很多有价值的想法。报告结束后,很多同学围绕在其身边讨论技术和观念方面的问题,充分展现了当代研究生对科研的热情和智慧。图片 1

00:12

I study ants in the desert, in the tropical forest and in my kitchen,
and in the hills around Silicon Valley where I live. I’ve recently
realized that ants are using interactions differently in different
environments, and that got me thinking that we could learn from this
about other systems, like brains and data networks that we engineer,and
even cancer.

00:41

So what all these systems have in common is that there’s no central
control. An ant colony consists of sterile female workers — those are
the ants you see walking around — and then one or more reproductive
femaleswho just lay the eggs. They don’t give any instructions. Even
though they’re called queens, they don’t tell anybody what to do. So in
an ant colony, there’s no one in charge, and all systems like this
without central control are regulated using very simple interactions.
Ants interact using smell. They smell with their antennae,and they
interact with their antennae, so when one ant touches another with its
antennae, it can tell, for example, if the other ant is a nestmate and
what task that other ant has been doing. So here you see a lot of ants
moving around and interacting in a lab arena that’s connected by tubes
to two other arenas. So when one ant meets another, it doesn’t matter
which ant it meets, and they’re actually not transmitting any kind of
complicated signal or message. All that matters to the ant is the rate
at which it meets other ants. And all of these interactions, taken
together, produce a network. So this is the network of the ants that you
just saw moving around in the arena, and it’s this constantly shifting
network that produces the behavior of the colony,like whether all the
ants are hiding inside the nest, or how many are going out to forage. A
brain actually works in the same way, but what’s great about ants is
that you can see the whole network as it happens.

02:23

There are more than 12,000 species of ants, in every conceivable
environment, and they’re using interactions differently to meet
different environmental challenges. So one important environmental
challenge that every system has to deal with is operating costs, just
what it takes to run the system. And another environmental challenge is
resources, finding them and collecting them. In the desert, operating
costs are high because water is scarce, and the seed-eating ants that I
study in the desert have to spend water to get water. So an ant outside
foraging, searching for seeds in the hot sun, just loses water into the
air. But the colony gets its water by metabolizing the fats out of the
seeds that they eat. So in this environment, interactions are used to
activate foraging. An outgoing forager doesn’t go out unless it gets
enough interactions with returning foragers, and what you see are the
returning foragers going into the tunnel, into the nest, and meeting
outgoing foragers on their way out. This makes sense for the ant colony,
because the more food there is out there, the more quickly the foragers
find it, the faster they come back, and the more foragers they send
out.The system works to stay stopped, unless something positive happens.

03:39

So interactions function to activate foragers. And we’ve been studying
the evolution of this system. First of all, there’s variation. It turns
out that colonies are different. On dry days, some colonies forage less,
so colonies are different in how they manage this trade-off between
spending water to search for seeds and getting water back in the form of
seeds. And we’re trying to understand why some colonies forage less than
others by thinking about ants as neurons, using models from
neuroscience. So just as a neuron adds up its stimulationfrom other
neurons to decide whether to fire, an ant adds up its stimulation from
other ants to decide whether to forage. And what we’re looking for is
whether there might be small differences among colonies in how many
interactions each ant needs before it’s willing to go out and forage,
because a colony like that would forage less.

04:32

And this raises an analogous question about brains. We talk about the
brain, but of course every brain is slightly different, and maybe there
are some individuals or some conditions in which the electrical
properties of neurons are such that they require more stimulus to fire,
and that would lead to differences in brain function.

04:53

So in order to ask evolutionary questions, we need to know about
reproductive success. This is a map of the study site where I have been
tracking this population of harvester ant colonies for 28 years, which
is about as long as a colony lives. Each symbol is a colony, and the
size of the symbol is how many offspring it had,because we were able to
use genetic variation to match up parent and offspring colonies, that
is, to figure out which colonies were founded by a daughter queen
produced by which parent colony. And this was amazing for me, after all
these years, to find out, for example, that colony 154, whom I’ve known
well for many years, is a great-grandmother. Here’s her daughter colony,
here’s her granddaughter colony, and these are her great-granddaughter
colonies. And by doing this, I was able to learn that offspring colonies
resemble parent colonies in their decisions about which days are so hot
that they don’t forage, and the offspring of parent colonies live so far
from each other that the ants never meet, so the ants of the offspring
colony can’t be learning this from the parent colony. And so our next
step is to look for the genetic variation underlying this resemblance.

06:07

So then I was able to ask, okay, who’s doing better? Over the time of
the study, and especially in the past 10 years, there’s been a very
severe and deepening drought in the Southwestern U.S., and it turns out
that the colonies that conserve water, that stay in when it’s really hot
outside, and thus sacrifice getting as much food as possible, are the
ones more likely to have offspring colonies. So all this time, I thought
that colony 154 was a loser, because on really dry days, there’d be just
this trickle of foraging, while the other colonies were outforaging,
getting lots of food, but in fact, colony 154 is a huge success. She’s a
matriarch. She’s one of the rare great-grandmothers on the site. To my
knowledge, this is the first time that we’ve been able to track the
ongoing evolution of collective behavior in a natural population of
animals and find out what’s actually working best.

07:04

Now, the Internet uses an algorithm to regulate the flow of data that’s
very similar to the one that the harvester ants are using to regulate
the flow of foragers. And guess what we call this analogy? The anternet
is coming.(Applause) So data doesn’t leave the source computer unless it
gets a signal that there’s enough bandwidthfor it to travel on. In the
early days of the Internet, when operating costs were really high and it
was really important not to lose any data, then the system was set up
for interactions to activate the flow of data. It’s interesting that the
ants are using an algorithm that’s so similar to the one that we
recently invented, but this is only one of a handful of ant algorithms
that we know about, and ants have had 130 million years to evolve a lot
of good ones, and I think it’s very likely that some of the other 12,000
species are going to have interesting algorithms for data networks that
we haven’t even thought of yet.

发表评论

电子邮件地址不会被公开。 必填项已用*标注

相关文章

网站地图xml地图