ABOUT THE SPEAKER
Irina Kareva - Theoretical biologist
Irina Kareva is looking for answers to biological questions using mathematical modeling.

Why you should listen

Dr. Irina Kareva studies cancer as an evolving ecosystem, bringing in insights from various disciplines -- from evolutionary biology to paleontology to ergodic theory -- to understand how we can manage, if not cure, cancer like a chronic disease. She has authored more than 25 publications, including several papers with her parents, who are also mathematicians. The Kareva clan was featured in a Nature article entitled "Relationships: Scions of Science."
 
Kareva is a research scientist at EMD Serono Research Center near Boston Massachusetts, US. Her book, Understanding Cancer from a Systems Biology Point of View: From Observation to Theory and Back, was recently published by Elsevier, and a second book on mathematical modeling of the evolution of heterogeneous populations will be released in mid-2019. 
 
In addition to her scientific studies and endeavors, Kareva also holds a degree in music and works actively as a professional opera singer.  She is a member of the Boston Symphony Orchestra’s Tanglewood Festival Chorus, has performed solo roles in local productions, religious music performances, and can even occasionally be heard in pieces as varied as video game soundtracks and heavy metal recordings.


More profile about the speaker
Irina Kareva | Speaker | TED.com
TED@Merck KGaA, Darmstadt, Germany

Irina Kareva: Math can help uncover cancer's secrets

艾琳娜卡瑞瓦: 數學能幫忙揭開癌症的秘密

Filmed:
1,223,313 views

艾琳娜卡瑞瓦把生物學的語言翻譯成數學的語言,也把數學的翻譯回生物學的。她撰寫描述癌症動態的數學模型,為了要針對腫瘤來開發新的藥物。她認為建立數學模型的力與美,在於它能嚴謹地形式化我們的認知,也能引導我們應該持續探索的方向,和指出哪裡可能是死路。這涉及到要:問對問題,把問題譯成對的方程式,和再翻譯回來。
- Theoretical biologist
Irina Kareva is looking for answers to biological questions using mathematical modeling. Full bio

Double-click the English transcript below to play the video.

00:12
I am a translator翻譯者.
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我翻譯,
00:14
I translate翻譯 from biology生物學 into mathematics數學
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從生物學翻譯成數學,
00:17
and vice versa反之亦然.
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也從數學譯回生物學。
00:19
I write mathematical數學的 models楷模
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我撰寫數學模型,
00:21
which哪一個, in my case案件, are systems系統
of differential微分 equations方程,
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用微分方程系統
00:24
to describe描述 biological生物 mechanisms機制,
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來描述生物的機制,
00:26
such這樣 as cell細胞 growth發展.
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像是細胞的成長。
00:28
Essentially實質上, it works作品 like this.
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基本上,它的運作如下。
00:30
First, I identify鑑定 the key elements分子
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我先要找出關鍵的元素,
00:33
that I believe may可能 be driving主動
behavior行為 over time
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那些我認為會隨著時間
00:35
of a particular特定 mechanism機制.
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驅動特定機制行為的元素。
00:38
Then, I formulate制定 assumptions假設
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接下來我做假設,
00:40
about how these elements分子
interact相互作用 with each other
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臆測這些元素如何彼此互動、
00:43
and with their environment環境.
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與環境互動。
00:44
It may可能 look something like this.
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看起來像圖示這樣。
00:46
Then, I translate翻譯
these assumptions假設 into equations方程,
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然後我把這些假設翻譯成方程式,
00:50
which哪一個 may可能 look something like this.
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看起來像這樣(右圖)。
00:53
Finally最後, I analyze分析 my equations方程
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最後,我分析方程式,
00:55
and translate翻譯 the results結果 back
into the language語言 of biology生物學.
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再把結果譯回生物學的語言。
01:00
A key aspect方面 of mathematical數學的 modeling造型
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建立數學模式的關鍵面向,
01:02
is that we, as modelers建 模,
do not think about what things are;
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並非我們這些建模的人
設想東西「是」什麼,
01:06
we think about what they do.
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而是「做」了什麼。
01:08
We think about relationships關係
between之間 individuals個人,
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我們設想個體間的關係,
01:10
whether是否 they be cells細胞, animals動物 or people,
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不論是細胞、動物或人,
01:13
and how they interact相互作用 with each other
and with their environment環境.
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設想他們如何彼此互動,
如何與環境互動。
01:17
Let me give you an example.
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讓我舉個例子。
01:19
What do foxes狐狸 and immune免疫的 cells細胞
have in common共同?
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狐狸和免疫細胞有什麼共通點?
01:24
They're both predators大鱷,
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兩者都是捕食者,
01:26
except foxes狐狸 feed飼料 on rabbits,
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不過,狐狸吃兔子,
01:29
and immune免疫的 cells細胞 feed飼料 on invaders入侵者,
such這樣 as cancer癌症 cells細胞.
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免疫細胞吃癌細胞之類的入侵者。
01:33
But from a mathematical數學的 point of view視圖,
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但從數學的觀點來看,
01:35
a qualitatively定性 same相同 system系統
of predator-prey捕食 type類型 equations方程
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用性質相同的
捕食者—獵物型方程式系統,
01:39
will describe描述 interactions互動
between之間 foxes狐狸 and rabbits
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就能描述狐與兔間的互動,
01:43
and cancer癌症 and immune免疫的 cells細胞.
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及癌症與免疫細胞間的互動。
01:45
Predator-prey捕食者-獵物 type類型 systems系統
have been studied研究 extensively廣泛
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捕食者—獵物型方程式系統
已經在科學文獻中被廣泛研究,
01:48
in scientific科學 literature文學,
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describing說明 interactions互動
of two populations人群,
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描述兩個族群間的互動,
01:52
where survival生存 of one depends依靠
on consuming消費 the other.
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其中一個族群的生存
仰賴消費另一個族群。
01:55
And these same相同 equations方程
provide提供 a framework骨架
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正是這些方程式提供架構
01:58
for understanding理解
cancer-immune癌症免疫 interactions互動,
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來了解癌症—免疫間的互動,
02:00
where cancer癌症 is the prey獵物,
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癌症是獵物,
02:02
and the immune免疫的 system系統 is the predator捕食者.
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免疫系統是捕食者。
02:04
And the prey獵物 employs採用 all sorts排序 of tricks技巧
to prevent避免 the predator捕食者 from killing謀殺 it,
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而獵物會採用各種詭計
避免遭捕食者獵殺,
02:08
ranging不等 from camouflaging偽裝 itself本身
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詭計的範圍從偽裝自己,
02:10
to stealing偷竊行為 the predator's捕食者的 food餐飲.
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到偷竊捕食者的食物都有。
02:13
This can have some very
interesting有趣 implications啟示.
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這意涵可能饒富興味。
02:15
For example, despite儘管 enormous巨大 successes成功
in the field領域 of immunotherapy免疫治療,
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例如,儘管免疫治療的領域
已取得巨大的成功,
02:20
there still remains遺跡
somewhat有些 limited有限 efficacy功效
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遇到實質固態瘤時功效仍然有限。
02:23
when it comes solid固體 tumors腫瘤.
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02:25
But if you think about it ecologically生態,
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如果從生態的角度來想,
02:28
both cancer癌症 and immune免疫的 cells細胞 --
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癌症和免疫細胞
02:30
the prey獵物 and the predator捕食者 --
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──捕食者和獵物──
02:31
require要求 nutrients營養成分
such這樣 as glucose葡萄糖 to survive生存.
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皆需葡萄糖之類的營養才能生存。
02:35
If cancer癌症 cells細胞 outcompete勝出
the immune免疫的 cells細胞 for shared共享 nutrients營養成分
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在腫瘤微環境中競爭共同的養分時,
如果癌症細胞勝過了免疫細胞,
02:40
in the tumor microenvironment微環境,
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then the immune免疫的 cells細胞 will physically物理
not be able能夠 to do their job工作.
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那麼免疫細胞將無法完成工作。
02:46
This predator-prey-shared捕食者-獵物-共用
resource資源 type類型 model模型
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我研究這種捕食者—獵物
共享資源形式的模型。
02:49
is something I've worked工作 on
in my own擁有 research研究.
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02:51
And it was recently最近 shown顯示 experimentally實驗
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近期有實驗顯示,
02:54
that restoring恢復 the metabolic新陳代謝 balance平衡
in the tumor microenvironment微環境 --
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恢復腫瘤微環境的代謝平衡──
02:58
that is, making製造 sure
immune免疫的 cells細胞 get their food餐飲 --
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亦即確保免疫細胞能獲取食物──
03:01
can give them, the predators大鱷, back
their edge邊緣 in fighting戰鬥 cancer癌症, the prey獵物.
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能讓免疫細胞這捕食者取回優勢
來對抗癌症這獵物。
03:08
This means手段 that if you abstract抽象 a bit,
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意思是,可用抽象一點的方式,
03:10
you can think about cancer癌症 itself本身
as an ecosystem生態系統,
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把癌症本身設想像為生態系統,
03:13
where heterogeneous異質 populations人群 of cells細胞
compete競爭 and cooperate合作
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那裡的各種細胞族群
彼此競爭和合作以取得空間和營養,
03:18
for space空間 and nutrients營養成分,
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03:20
interact相互作用 with predators大鱷 --
the immune免疫的 system系統 --
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和免疫系統這捕食者互動,
03:22
migrate遷移 -- metastases轉移 --
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遷移、轉移……
03:25
all within the ecosystem生態系統
of the human人的 body身體.
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全都發生在人體這生態系統中。
03:28
And what do we know about most
ecosystems生態系統 from conservation保護 biology生物學?
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我們從保育生物學的角度看,
對生態系統了解最多的是什麼?
03:32
That one of the best最好 ways方法
to extinguish撲滅 species種類
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我們知道滅絕物種的最佳方式,
03:35
is not to target目標 them directly
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不是直接針對物種,
03:37
but to target目標 their environment環境.
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而是針對物種的環境。
03:40
And so, once一旦 we have identified確定
the key components組件
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因此,一旦我們找出了
腫瘤環境的關鍵組成,
03:43
of the tumor environment環境,
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we can propose提出 hypotheses假設
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我們就能提出假設、
03:47
and simulate模擬 scenarios場景
and therapeutic治療 interventions干預措施
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模擬情境和干預治療,
03:50
all in a completely全然 safe安全
and affordable實惠 way
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全都以安全和實惠的方式進行,
03:54
and target目標 different不同 components組件
of the microenvironment微環境
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針對微環境中的不同組成成份,
03:57
in such這樣 a way as to kill the cancer癌症
without harming漢寧波 the host主辦,
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能夠殺死癌症卻不傷到宿主,
04:01
such這樣 as me or you.
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不傷到我或你。
04:05
And so while the immediate即時
goal目標 of my research研究
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所以,我研究的當前目標
04:08
is to advance提前 research研究 and innovation革新
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是推動研究和創新,
04:10
and to reduce減少 its cost成本,
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並減少成本。
04:12
the real真實 intent意圖, of course課程,
is to save保存 lives生活.
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當然,真正的目的是要拯救人命。
04:15
And that's what I try to do
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那就是我試著在做的事,
04:17
through通過 mathematical數學的 modeling造型
applied應用的 to biology生物學,
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將數學建模應用到生物學,
04:19
and in particular特定,
to the development發展 of drugs毒品.
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特別是用在藥物的發展上。
04:22
It's a field領域 that until直到 relatively相對
recently最近 has remained保持 somewhat有些 marginal邊緣,
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直到最近,這一直是個
邊緣、不被重視的領域,
04:26
but it has matured成熟.
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但它已經成熟了。
04:28
And there are now very well-developed發達
mathematical數學的 methods方法,
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現在有發展得非常好的數學方法,
04:31
a lot of preprogrammed預先 tools工具,
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有很多預編的程式工具,
04:33
including包含 free自由 ones那些,
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也有很多免費的工具,
04:35
and an ever-increasing不斷增加 amount
of computational計算 power功率 available可得到 to us.
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我們能獲得的計算能力越來越多。
04:40
The power功率 and beauty美女
of mathematical數學的 modeling造型
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數學建模的力與美在於
它能把我們的認知
04:44
lies in the fact事實
that it makes品牌 you formalize形式化,
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04:46
in a very rigorous嚴格 way,
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以非常嚴謹的方式形式化。
04:48
what we think we know.
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04:50
We make assumptions假設,
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我們假設,
04:52
translate翻譯 them into equations方程,
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把假設翻譯為方程式,
04:53
run simulations模擬,
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進行模擬,
04:55
all to answer回答 the question:
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都是要解答這個問題:
04:57
In a world世界 where my assumptions假設 are true真正,
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在假設能夠成立的世界裡,
04:59
what do I expect期望 to see?
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我預期看見什麼?
05:01
It's a pretty漂亮 simple簡單 conceptual概念上的 framework骨架.
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這是很簡單的概念性架構,
05:04
It's all about asking the right questions問題.
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重點是要問對問題。
05:06
But it can unleash發揮 numerous眾多 opportunities機會
for testing測試 biological生物 hypotheses假設.
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它能夠解放出許多
測試生物假設的機會。
05:11
If our predictions預測 match比賽 our observations意見,
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如果我們的預測和觀察相吻合,
05:14
great! -- we got it right,
so we can make further進一步 predictions預測
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很棒!我們做對了。
我們就能改變模型來進一步預測。
05:17
by changing改變 this or that
aspect方面 of the model模型.
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05:20
If, however然而, our predictions預測
do not match比賽 our observations意見,
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然而如果預測和觀察不吻合,
05:24
that means手段 that some
of our assumptions假設 are wrong錯誤,
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那就表示有些假設是錯的,
05:27
and so our understanding理解
of the key mechanisms機制
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我們對於背後生物學的關鍵機制
05:29
of underlying底層 biology生物學
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了解得還不夠完善。
05:30
is still incomplete殘缺.
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05:32
Luckily, since以來 this is a model模型,
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幸運的是,因為這是模型,
05:35
we control控制 all the assumptions假設.
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我們能控制所有的假設,
05:37
So we can go through通過 them, one by one,
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所以能看過一個個假設,
05:39
identifying識別 which哪一個 one or ones那些
are causing造成 the discrepancy差異.
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找出哪一個或哪幾個造成了不一致。
05:43
And then we can fill this newly
identified確定 gap間隙 in knowledge知識
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接著就能把新辨識出來的知識落差,
05:47
using運用 both experimental試驗
and theoretical理論 approaches方法.
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用實驗性和理論性的方式補起來。
05:50
Of course課程, any ecosystem生態系統
is extremely非常 complex複雜,
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當然,生態系統都極度複雜,
05:53
and trying to describe描述 all the moving移動
parts部分 is not only very difficult,
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試圖描述所有會動的部分,
不僅很困難,也無法提供很多資訊。
05:57
but also not very informative信息.
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05:59
There's also the issue問題 of timescales時間表,
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還有時間範圍的議題,
06:01
because some processes流程 take place地點
on a scale規模 of seconds, some minutes分鐘,
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因為有些過程的時間範圍
發生在秒上,有些在分上,
06:05
some days, months個月 and years年份.
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還有的是日、月、年。
06:07
It may可能 not always be possible可能
to separate分離 those out experimentally實驗.
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未必都能在實驗裡分得開。
06:11
And some things happen發生
so quickly很快 or so slowly慢慢地
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有些發生得很快或很慢,
06:14
that you may可能 physically物理
never be able能夠 to measure測量 them.
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實際上根本不可能去量。
06:17
But as mathematicians數學家,
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但身為數學家,
06:19
we have the power功率 to zoom放大 in
on any subsystem子系統 in any timescale時間表
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我們有能力放大
任何子系統的任何時間範圍,
06:25
and simulate模擬 effects效果 of interventions干預措施
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並模擬在任何時間範圍內
06:27
that take place地點 in any timescale時間表.
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所發生的干預效果。
06:31
Of course課程, this isn't the work
of a modelerModeler alone單獨.
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當然,這不只是
建模者一個人的工作,
06:34
It has to happen發生 in close
collaboration合作 with biologists生物學家.
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一定要和生物學家密切合作才行。
06:38
And it does demand需求
some capacity容量 of translation翻譯
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這確實會需要一些翻譯的能力,
06:41
on both sides雙方.
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雙方都要。
06:43
But starting開始 with a theoretical理論
formulation公式 of a problem問題
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從表述問題的理論開始,
06:47
can unleash發揮 numerous眾多 opportunities機會
for testing測試 hypotheses假設
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就能帶出許多機會
來測試假設、
06:50
and simulating模擬 scenarios場景
and therapeutic治療 interventions干預措施,
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模擬情景和干預治療措施,
06:54
all in a completely全然 safe安全 way.
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全都以安全的方式進行。
06:56
It can identify鑑定 gaps空白 in knowledge知識
and logical合乎邏輯 inconsistencies不一致性
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它能夠辨視出知識的落差、
邏輯的不一致,
07:02
and can help guide指南 us
as to where we should keep looking
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能引導我們應該持續探索的方向,
07:05
and where there may可能 be a dead end結束.
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指出哪裡可能是死胡同。
07:07
In other words:
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換言之,
07:08
mathematical數學的 modeling造型
can help us answer回答 questions問題
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數學建模能協助我們回答
直接影響人們健康的問題,
07:12
that directly affect影響 people's人們 health健康 --
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07:15
that affect影響 each
person's人的 health健康, actually其實 --
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其實會影響每個人的健康,
07:18
because mathematical數學的 modeling造型 will be key
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因為數學建模將會是
推進個人化醫學的關鍵。
07:21
to propelling推進 personalized個性化 medicine醫學.
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07:24
And it all comes down
to asking the right question
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而這全都涉及到:要問對問題,
07:27
and translating翻譯 it
to the right equation方程 ...
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要將問題翻譯成對的方程式,
07:30
and back.
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和再翻譯回來。
07:32
Thank you.
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謝謝。
07:33
(Applause掌聲)
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(掌聲)
Translated by Lilian Chiu
Reviewed by Helen Chang

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ABOUT THE SPEAKER
Irina Kareva - Theoretical biologist
Irina Kareva is looking for answers to biological questions using mathematical modeling.

Why you should listen

Dr. Irina Kareva studies cancer as an evolving ecosystem, bringing in insights from various disciplines -- from evolutionary biology to paleontology to ergodic theory -- to understand how we can manage, if not cure, cancer like a chronic disease. She has authored more than 25 publications, including several papers with her parents, who are also mathematicians. The Kareva clan was featured in a Nature article entitled "Relationships: Scions of Science."
 
Kareva is a research scientist at EMD Serono Research Center near Boston Massachusetts, US. Her book, Understanding Cancer from a Systems Biology Point of View: From Observation to Theory and Back, was recently published by Elsevier, and a second book on mathematical modeling of the evolution of heterogeneous populations will be released in mid-2019. 
 
In addition to her scientific studies and endeavors, Kareva also holds a degree in music and works actively as a professional opera singer.  She is a member of the Boston Symphony Orchestra’s Tanglewood Festival Chorus, has performed solo roles in local productions, religious music performances, and can even occasionally be heard in pieces as varied as video game soundtracks and heavy metal recordings.


More profile about the speaker
Irina Kareva | Speaker | TED.com

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