標題: Systems biology 101—what you need [打印本頁] 作者: 牧童 時間: 2007-8-25 01:40 標題: Systems biology 101—what you need 作者Trey Ideker 翻譯 牧童
Systems biology has spurred interest in 系統生物學已激起千萬研究人員
thousands of researchers, some just starting 的興趣,有些才開始他們的事業,
their careers, others well established but 其余的,那些系統生物學的初學者,
interested in learning more about it.What is 包括有興趣的科學家和大學生,
the best plan for scientists and students 他們學習系統生物學的最佳方案
interested in a career in systems biology? 是什么?
Why the excitement? 他們為何有興趣啊?
The use of systematic genomic, proteomic 利用系統的基因組技術知識\蛋白質
and metabolomic technologies to construct 和新陳代謝技術知識構建
models of complex biological systems and 復雜生物系統和疾病的模型
diseases is becoming increasingly commonplace. 已漸近普及.
These endeavors, collectively known 這些努力,集合一起被公認為
as systems biology1,2, establish an approach 系統生物學1,2,構建一種方法----
by which to interrogate and iteratively 由此查詢和反復地精煉我們的
refine our knowledge of the cell. In so 各局部的知識. 在這一查詢和提煉
doing, systems biology integrates knowledge 過程中,
from diverse biological components
and data into models of the system as a
whole.
Although the notion of systems science
has existed for some time3, these approaches
have recently become far more powerful
because of a host of new experimental technologies
that are high-throughput, quantitative
and large-scale4. As evidence of the
impact ‘systems’ thinking has had on biology,
consider the explosive growth of new
research institutes, companies, conferences
and academic departments that have the
words ‘systems biology’ in the title or mission
statement. Several journals are now
either entirely devoted to reporting systems
biology research or are sponsoring regular
sections devoted to current issues in systems
or computational biology, such as this inaugural
section in Nature Biotechnology. And
under the leadership of Elias Zerhouni, the
National Institutes of Health (Bethesda,
MD,USA) has released a new ‘roadmap’ that
includes interdisciplinary science and integrative
systems biology as core focus areas5;
the UK’s Biotechnology and Biological
Sciences Research Council has also targeted
predictive and integrated biology as a strategic
aim over the next five years6.
Where to start
Because of the need to couple computational
analysis techniques with systematic biological
experimentation, more and more universities
are offering PhD programs that
integrate both computational and biological
subject matter (Table 1). Several of these
programs, such as those recently initiated by
the Massachusetts Institute of Technology
(MIT, Cambridge, MA, USA) and Harvard
University (Cambridge, MA, USA), include
‘systems biology’ directly in the name.
Others offer courses of study from within
physics, engineering or biology departments
(e.g., the systems biology syllabus within the
bioengineering department at the University
of California, San Diego, CA, USA).
Apart from PhD programs with course
offerings in systems biology, a number of
institutions offer intensive short courses
(Table 2). These include the Institute for
Systems Biology (Seattle,WA, USA), Oxford
University (Oxford, UK) and Biocentrum
Amsterdam (Holland). There are also several
other emerging initiatives and educational
programs around the globe (Table 3).
Given the pace of the field, it is probably
too early to endorse one particular syllabus
as the correct or best option. However,
clearly all programs must provide a rigorous
understanding of both biology and quantitative
modeling. Thus, many require that all
students, regardless of background, perform
hands-on research in both computer programming
and in the wet laboratory.
Required course work in biology typically
includes genetics, biochemistry, molecular
and cell biology, with laboratory work associated
with each of these. Course work in
quantitative modeling might include probability,
statistics, information theory, numerical
optimization, artificial intelligence and
machine learning, graph and network theory,
and nonlinear dynamics. Of the biological
course work, genetics is particularly
important, because the logic of genetics is,
to a large degree, the logic of systems biology.
Of the course work in quantitative
modeling, graph theory and machine learning
techniques are of particular interest,
because systems approaches often reduce
cellular function to a search on a network of
biological components and interactions7,8.
A course of study integrating life and quantitative
sciences helps students to appreciate
the practical constraints imposed by experimental
biology and to effectively tailor
research to the needs of the laboratory biologist.
At the same time, knowledge of the
major algorithmic techniques for analysis of
biological systems will be crucial for making
sense of the data.
Other paths
An alternative to pursuing a cross-disciplinary
program is to tackle one field initially
and then learn another in graduate school.
Examples would include choosing an
undergraduate major in engineering and
then obtaining a PhD in molecular biology,
or starting within biochemistry then pursuing
course work in computer programming.
This leads to a common question: when
contemplating a transition, is it better to
switch from quantitative sciences to biology
or vice versa?
Although some feel that it is easier to
move from engineering into biology, the
honest answer is that either trajectory can
work. Some practical advice is that if coming
from biology, start by becoming familiar
with Unix, Perl and Java before diving into
more complex computational methodologies.
If coming from the quantitative
sciences, jump into a wet laboratory as soon
as possible—when shaky hands become
steady, you’re well on your way.
The job market
What jobs are new systems biologists likely
to find? With the formation of myriad new
academic departments and centers, the academic
job market is booming. On the other
hand, biotech firms and ‘big pharma’ have
been more cautious about getting involved9.
However, most agree that in the long-term,
systems approaches promise to influence
drug development in several areas: first, target
identification, in which drugs are developed
to target a specific molecule or
molecular interaction within a pathway;
second, prediction of drug mechanism-ofaction
(MOA), in which a compound has
known therapeutic effects but the molecular
NATURE BIOTECHNOLOGY VOLUME 22 NUMBER 4 APRIL 2004 473作者: monica 時間: 2007-8-27 22:32
呵呵，樓主把剩余的部分留給我們自己翻譯了.考我們啊?:liuhan: