工程化案例解析系统生物信息学：生物学与工程的交叉探索

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"Systems Bioinformatics: An Engineering Case-Based Approach" 是一本将工程学方法应用于生物和生物医学研究的开创性指南，旨在利用来自技术领域的计算工具，提供一个定量的系统生物信息学研究方法。这本书通过将生物过程置于工程学的背景下，帮助工程师运用他们的技术技能解决新颖的生物学问题，并促进从生物学向合成生物学设备的反向工程。本书的主要特点包括： 1. 系统生物信息学介绍：系统生物信息学是将工程学的思维方式引入生物信息学的研究，通过量化分析来理解和解析复杂的生物系统。它结合了生物学、计算机科学、数学和工程学等多个学科，为解决生物学难题提供了新的途径。 2. 工程案例基础：书中的案例研究使读者能够在实际工程问题的框架内学习和应用生物信息学概念，这种方法有助于强化理论知识与实践应用之间的联系。 3. 信号处理技术：书中详细介绍了在生物数据分析中使用的信号处理技术。这些技术对于处理如基因表达数据、蛋白质谱数据等复杂生物信号至关重要，能够帮助提取有价值的信息并减少噪声干扰。 4. 细胞调控系统与传统控制系统比较：作者探讨了细胞内的调节网络如何与传统的工程控制系统的原理相似。这种比较有助于理解生物系统的动态行为，并可能启发新的控制策略设计。 5. 蛋白质和基因网络：书中深入探讨了蛋白质和基因网络的结构和功能，包括它们的相互作用、调控机制以及对生物过程的影响。这些内容对于理解细胞功能和疾病发生具有重要意义。 6. 推断网络和网络动力学：书中涵盖了如何构建和分析推断网络，以理解生物系统的行为模式和动态变化。这些网络模型可以帮助预测系统响应、识别关键分子和揭示生物学通路。 7. 免责声明：尽管书中内容经过精心开发和验证，但作者和出版商明确表示不提供任何明示或暗示的保证。用户在使用书中的程序或方法时需自行承担风险，因为错误的应用可能导致人身伤害或财产损失。 8. 系列图书：本书是Artech House的"Bioinformatics & Biomedical Imaging Series"的一部分，由Steven Wong（哈佛大学）主编，反映了该领域不断发展的研究趋势和重要性。 "Systems Bioinformatics: An Engineering Case-Based Approach" 是一本将工程学方法与生物信息学相结合的宝贵资源，为工程师、生物学家和相关领域的研究人员提供了理解和解决生物学挑战的新视角。通过实例和工程方法，这本书不仅传授了生物信息学的知识，还鼓励读者将这些知识应用于实际问题，推动生物学和工程学的交叉领域研究。

the Massachusetts Institute of Technology (MIT) Electrical Engineering and Com-

puter Science Department and Harvard/MIT Health Science and Technology (with

Professor Manolis Kellis of MIT).

Like the courses it originated from, this book targets upper level undergraduate

and first year graduate students in engineering disciplines. It does not try to cover

every subfield of bioinformatics; rather, it seeks to teach concepts and ways of

thinking about biological problems using an engineering approach. To do this, it is

organized by engineering concepts, and presents cases in biology for in-depth ex-

ploration. Thus, this book is an excellent stand-alone text for an introductory/moti-

vational seminar or course on the subject. It can also serve in juxtaposition to a more

classically organized text—which covers the breadth of bioinformatics—by adding

in-depth cases for study. Last, it can serve as a complementary text to traditional

texts, which are often organized by biological concepts. By teaching bioinformatics

from multiple perspectives, the editors have found that students gain a deeper un-

derstanding of the fundamental concepts.

The book has the recommended co- or prerequisites of Signals and Systems

(e.g., 6.003 at MIT), Probabilistic Systems Analysis and Applied Probability (e.g.,

MIT 6.041/6.431), and Introductory Biology (Molecular) (e.g., MIT 7.012). For

those who have not had one or more of the above classes, a couple of review ses-

sions may be useful.

Some of the course materials and methodologies from the HST.480/6.092

courses (now in this book) were also subsequently used in HST.512 Genomic Med-

icine at Harvard Medical School, 6.872/HST 950 Biomedical Computing at MIT,

and BS771 Design and Analysis of Microarray Experiments at Boston University.

In addition, the 6.092 course was published on online for the public via MIT’s

Open Courseware initiative (http://ocw.mit.edu), a potentially useful online re-

source for readers.

The text is divided into six parts. Contrary to most bioinformatics books that

present material based on biological concepts, this book’s parts are categorized

based on fundamental engineering concepts and applications, in a manner consis-

tent with its engineering-oriented approach.

In Part I, the fundamental biology is introduced from an engineering perspec-

tive. The first chapter presents the needed molecular and cellular biology back-

ground and can be treated within a review session if the course includes a

prerequisite biology course similar to MIT’s 7.012 “Introductory Biology” (Mole-

cular). A number of engineering analogies are presented to facilitate presentation of

the material. In the second chapter, the book moves from the genomics to pro-

teomics—looking at ways that engineering and automation can be used to explore

genes and proteins in a parallel, high-throughput manner.

Parts II through IV focus on engineering analysis methods. Part II starts with

signal processing methods. Chapter 3 introduces biological signal processing with

applications, while Chapter 4 focuses on a case study in mass spectrometry. Part III

discusses controls and systems. Chapter 5 introduces the fundamentals and appli-

cations in gene regulation. Chapter 6 focuses on modeling cellular circuits. In Part

IV, probabilistic data networks and communications are covered. Chapter 7 dis-

xvi Preface

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cusses topologies of cellular networks and how some biological properties can be

ascertained solely from network connectivity. The final chapter of this part, Chap-

ter 8, introduces and expands on the use of Bayesian networks to link genetic in-

formation (single nucleotide polymorphisms, or SNPs) to human disease.

Parts V and VI switch from discussing analysis to tackling issues in design. After

introducing the area of synthetic biology in Chapter 9, Part V goes on to look at

computer-aided design (CAD) tools adapted from circuit design to biomolecular

circuitry design in Chapter 10. Next, a case study with a chemical engineering in-

dustrial perspective is presented on applying cellular engineering to perturb cellular

pathways. The final part, Part VI, looks at how biological designs and principles

can be applied back to engineering. In Chapter 12, the biology of sequence hy-

bridization is discussed along with its various applications to engineering, ranging

from DNA-based computers to nanoparticle assembly.

In Chapter 13, it is shown how massive parallelization via DNA computing can

be used to break encryption algorithms previously thought to be secure. Finally,

Chapter 14 examines how navigation and source localization strategies can be in-

spired by biological designs involving chemotaxis. The book concludes by summa-

rizing the field and looking at future avenues of research in this area. For those

interested in additional resources, source code, and related materials, the book’s In-

ternet site can be accessed under artechhouse.com.

Because this work has been an international effort, there are many people

whose contributions were critical to its publication. The editors would like to thank

the editors at Artech House—particularly acquisitions editor Wayne Yuhasz, who

invited us to write this book and worked hard with us to complete it on a tight

schedule—and Barbara Lovenvirth, who helped in the manuscript review process.

The editors would like to say thank you to 6.092 co-course director Manolis Kel-

lis, Assistant Professor at the MIT Electrical Engineering and Computer Science De-

partment’ to Prof. Isaac Kohane at Harvard Medical School; to the Harvard

Partners Center for Genetics and Genomics; and to the faculty and student mem-

bers of the Harvard/MIT Health Science and Technology Division’s Graduate Com-

mittee as well as the Electrical Engineering and Computer Science Division,

especially Anne Hunter, for their support of the HST 480/6.092 courses.

The editors would like to thank the contributing authors to the text: Gregory

Crowther, Catherine Speake, Alicia McBride, and Mary Lidstrom (Chapter 1);

Stephanie Mohr, Yanhui Hu, and Joshua LaBaer (Chapter 2); Maya Said (Chapter 3),

Peter Monchamp, Lucio Cetto, Jane Zhang, and Rob Henson (Chapter 4); Fulvia Fer-

razzi and Riccardo Bellazzi (Chapter 5); Tae Jun Lee, Chee Meng Tan, Dennis Tu,

and Lingchong You (Chapter 6); Vinayak Muralidhar and Gabor Szabo (Chapter 7);

Paola Sebastiani and Maria Abad-Grau (Chapter 8); Cody Wood (Chapter 9);

Jonathan Goler and Tom Knight (Chapter 10); Brian Baynes and William Blake

(Chapter 11); Olgica Milenkovic (Chapter 12); Michael Shan-Hui Ho, Weng-Long

Chang, and Minyi Guo (Chapter 13); and Gail Rosen and Paul Hasler (Chapter 14).

Additionally, the editors would like to acknowledge Ehsan Afkhami, now at

Mathworks, for his contributions early in the book development process. Thank

you as well to the following people who helped in reviewing and editing the

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