【在 d*****r 的大作中提到】 : it's ok, you can do something else to make living, and in spare time work : on modeling part. The cost is minimal, if you really like it.
Let me speak for the modelers. The modeler's problem is, bench data are far from enough to make meaningful models. Most of the time, the data-generating process require so much more labor than simulation on computers. And modelers often require strict time points, strict location points, strict dose points, to fit into differential equations or machine learning models. With the current imaging or chemical tools, it's almost impossible. That's why they have to make too many compromises, like fitting a curve with only 3 data points. Of course this will generate crap models. That's why technology people like Zhuang Xiaowei, Sunny Xie, Roger Tsien etc will have much more edge now. The point is, it's still TOO EARLY to seriously think of modeling in BIology. Just wait for people like Feng Zhang to work out the engineering and chemistry to make high speed data-generating machine first. At the meantime, modelers should just jump to other fields like financial markets or social networks where there are enough data to play with (Well, I guess you may need to be the core team in Facebook to play with social network data...).
【在 d*****r 的大作中提到】 : Let me speak for the modelers. : The modeler's problem is, bench data are far from enough to make : meaningful models. Most of the time, the data-generating process : require so much more labor than simulation on computers. And modelers : often require strict time points, strict location points, strict dose : points, to fit into differential equations or machine learning models. : With the current imaging or chemical tools, it's almost impossible. : That's why they have to make too many compromises, like fitting a curve : with only 3 data points. Of course this will generate crap models. : That's why technology people like Zhuang Xiaowei, Sunny Xie, Roger Tsien
d*r
57 楼
It's exactly because people saw the problems in systems biology that synthetic biology came out hot. So the rise and fall of systems biology did contribute to the consensus of the importance of synthetic biology. Sometimes science take time to progress. It's not rare that the whole world may take 10 years to figure out the right direction to proceed. But our own lives maybe too short to sacrifice.
it's still the same problem, we don't have enough trustworthy data, far from enough, far far far... far from enough. SO why not just do something else for now.
ye, there're three levels of problems here: 1. lack of data points to fit a curve. 2. limit of current models, like you said, high-dimension curse, and something like NP-hard. Is statistic (machine learning) models better or PDE (kinetical, finite elements, etc) models better? 3. Is current mathematical tools enough? we are already stuck in level 1, so don't even need to think of level 2, or 3. So just 洗洗睡吧.
【在 d*****r 的大作中提到】 : ye, there're three levels of problems here: : 1. lack of data points to fit a curve. : 2. limit of current models, like you said, high-dimension curse, and : something like NP-hard. Is statistic (machine learning) models better : or PDE (kinetical, finite elements, etc) models better? : 3. Is current mathematical tools enough? : we are already stuck in level 1, so don't even need to think of level 2, : or 3. So just 洗洗睡吧. : : 统计背景,但
d*r
61 楼
man, the reason is very simple: 1. every lab needs to get money to survive. 2. to get money you need high profile papers. 3. to get high profile papers you need solve big problems 4. most people most of the time cannot solve big problems, to keep publishing CNS, they have to over-state their findings and data 5. if everyone else in the field over-state their findings and data, then you have to overstate too, unless 1), you are big enough you don't care, or 2), you want to be wiped out of this field 6. finally all big papers looks similar in the field, which is what you complain about.
【在 d*****r 的大作中提到】 : it's still the same problem, we don't have enough trustworthy data, far : from enough, far far far... far from enough. : SO why not just do something else for now.
东西双雄 Uri Alon & Wendel Lim 进两个死一双?! so what are below UCSF and Harvard this Feb report? almost contents of it are trash? //synberc.org/sites/default/files/vol2%202012-02-23.pdf //synberc.org/sites/default/files/vol1_2012-02-24.pdf Uri Alon: HTTPS://dev.sysbio.med.harvard.edu/faculty/alon/index.html Wendel Lim: //limlab.ucsf.edu/people/wendell.html
both may be similar: I mean single cell and Mouse/Rat/Homo sapiens if with RNA-Seq 10^g data by Illumina NGS or even ION platform: 98% Junk Homo sapiens Genomic sequence might hold similar biological functions with dinoflagellate those Junk genomic sequence. please refer one 2009 PhD Dissertation from KATHOLIEKE UNIVERSITEIT LEUVEN FTP://ftp.esat.kuleuven.be/pub/sista/vdbulcke/reports/PhD_thesis_ TimVandenBulcke.pdf file size: 15.6 MB and //www.affymetrix.com/support/technical/index.affx
【在 u**********d 的大作中提到】 : 呵呵,感觉是从一个极端跳向了另一个极端
l*1
75 楼
Please refer 3 papers: No. 1 Age, sex, density, winter weather, and population crashes in Soay sheep. (2001) Science 292:1528-1531. PubMed link: //www.ncbi.nlm.nih.gov/pubmed/11375487 and full text link: //eaton.math.rpi.edu/CSUMS/Papers/Ecostability/coulsonweathersheep.pdf No2. //www.ncbi.nlm.nih.gov/pubmed/15451687 No.3 //www.ncbi.nlm.nih.gov/pubmed/12948681 plus one 2009 book from @Springer "Real World Ecology” ShiLi Miao et al. Editors //www.crc.uqam.ca/Publication/Real%20World%20Ecology.pdf file size: about 6.9 MB it noted Climate Noise may drive ecological chaotic pattern shifting within evolutionary ecology.
其实这个关于《复杂》的故事还有另一本书,最早这几个在桑塔菲搞混沌系统的人, 他们一开始就想要利用混沌系统的研究搞金融交易,他们早在1991年的时候就创办了 一个公司叫Prediction Company,做automatic trading system,当年效仿著名的 LTCM(Long Term Capital Investment)希望用数学在金融市场套利,但是LTCM挂了, Prediction Company还在,被瑞士银行(UBS)收购,成为瑞银系统的一部分,但是 公司地点还是在桑塔菲,公司的领导层还是当年那几个搞复杂系统的科学家。 他们是最早用算法搞高频交易的公司之一,只不过那些混沌理论能不能在华尔街 赚到钱就只有天知道了。 有一本书写这个事情,Google Books上面有节选: http://books.google.com/books/about/The_Predictors.html?id=MQ-x The Predictors How a band of maverick physicists used chaos theory to trade their way to fortune on Wall Street. How could a couple of rumpled physicists in sandals and Eat the Rich T- shirts, piling computers into an adobe house in Santa Fe, hope to take on the Masters of the Universe from Goldman Sachs? Doyne Farmer and Norman Packard may never have read The Wall Street Journal, but they happen to be among the founders of the new sciences of chaos and complexity. Who better to try to find order in the apparently unreasoned chaos of the global financial markets? Thomas Bass first made readers aware of Farmer and Packard in The Eudaemonic Pie, in which he chronicled their assault on the casinos of Las Vegas. Here, Bass takes us inside their start-up company, at first a motley collection of long-haired Ph.D.s, nervously testing their computer forecasting models. As confidence builds, Farmer and Packard make their way to the centers of financial power, where they find investors and ultimately go live with real money. Once they are off and running, The Predictors becomes a dizzying, often hilarious tale of genius and greed, power brokers and rebels, as well as a brisk education in chaos, complexity, and the world financial markets.