Catalyzing Inquiry at the Interface of Computing and Biology

340 CATALYZING INQUIRY

Box 10.3 Continued

This last point is often misunderstood. For many outsiders, computer science is the same as computer pro-

gramming—a view reinforced by many introductory “computer science” courses that emphasize the writing

of computer programs. But it is better to understand computer programs as the specialized medium in which

the ideas and abstractions of computer science are tangibly manifested. Focusing on the writing of the com-

puter program without giving careful consideration to the abstractions embodied in the program is not unlike

understanding the writing of a novel as no more than the rules of grammar and spelling.

Algorithmic thinking, information representation, and computer programs are themes central to all subfields

of computer science and engineering research. They also provide material for intellectual study in and of

themselves, often with important practical results. The study of algorithms is as challenging as any area of

mathematics, and one of practical importance as well, since improperly chosen or designed algorithms may

solve problems in a highly inefficient manner. The study of programs is a broad area, ranging from the highly

formal study of mathematically proving programs correct to very practical considerations regarding tools with

which to specify, write, debug, maintain, and modify very large software systems (otherwise called software

engineering). Information representation is the central theme underlying the study of data structures (how

information can best be represented for computer processing) and much of human-computer interaction (how

information can best be represented to maximize its utility for human beings).

Finally, computer science is closely tied to an underlying technological substrate that evolves rapidly. This

substrate is the “stuff” out of which computational hardware is made, and the exponential growth that char-

acterizes its evolution makes it possible to construct ever-larger, ever-more-complex systems—systems that

are not predictable based on an understanding of their individual components. (As one example, the proper-

ties of the Internet prove a rich and surprisingly complex area of study even though its components—comput-

ers, routers, fiber-optic cables—are themselves well understood.)

A second report of the National Research Council described fluency with information technology as requiring

three kinds of knowledge: skills in using contemporary IT, foundational concepts about IT and computing, and

intellectual capabilities needed to think about and use IT for purposeful work.^2 The listing below is the

perspective of this report on essential concepts of IT for everyone:

• Computers (e.g., programs as a sequence of steps, memory as a repository for program and data, overall
  organization, including relationship to peripheral devices).


• Information systems (e.g., hardware and software components, people and processes, interfaces (both
  technology interfaces and human-computer interfaces), databases, transactions, consistency, availability, per-
  sistent storage, archiving, audit trails, security and privacy and their technological underpinnings).


• Networks: physical structure (messages, packets, switching, routing, addressing, congestion, local area
  networks, wide area networks, bandwidth, latency, point-to-point communication, multicast, broadcast, Eth-
  ernet, mobility), and logical structure (client/server, interfaces, layered protocols, standards, network services).


• Digital representation of information: concept of information encoding in binary form; different informa-
  tion encodings such as ASCII, digital sound, images, and video/movies; precision, conversion and interoper-
  ability (e.g., of file formats), resolution, fidelity, transformation, compression, and encryption; standardization
  of representations to support communication.


• Information organization (including forms, structure, classification and indexing, searching and retrieving,
  assessing information quality, authoring and presentation, and citation; search engines for text, images, video,
  audio).


• Modeling and abstraction: methods and techniques for representing real-world phenomena as computer
  models, first in appropriate forms such as systems of equations, graphs, and relationships, and then in appro-
  priate programming objects such as arrays or lists or procedures. Topics include continuous and discrete

(^2) Computer Science and Telecommunications Board, National Research Council, Being Fluent with Information Technology, National
Academy Press, Washington, DC, 1999.