The entire sequence of nucleotides in the human genome is now known, along with the genome sequences of many other organisms, including bacteria, archaea, fungi, plants, and animals. These accomplishments have been made possible by the development of new methods and DNA-sequencing machines.
The sequencing of the human genome is a scientific and technological achievement comparable to landing the Apollo astronauts on the moon in 1969. But itis only the beginning of an even bigger research endeavor, an effort to learn how the activities of the myriad proteins encoded by the DNA are coordinated in cells and whole organisms.
The best way to make sense of the deluge of data from genome-sequencing projeds and the growing catalog of known protein unctions is to apply a systems approach at the cellular and molecular levels. Figure 1 illustrates the results of a large study that mapped a network of protein interactions within a cell ofa fruit fly, a popular research organism.
The model is based on a database of thousands ofknown proteins and their known interactions with other proteins. For example, protein A may attach to and alter the activities of proteins B, C. and D, which then go on to interact with stit! other proteins. The figure maps these protein partnerships to their cellular locales.
The basics of the systems strategy are straightforward. First, it is necessary to inventory as many parts of the system as possible, such as all the known genes and proteins in a cell(an application of reductionism). Then it is necessary to investigate how each part behaves in relation to others in the working system-all the protein-protein interactions, in the case of our fly cell example. Finally, with the help of computers and specialized software, it is possible to pool all the data into the kind of system network pictured in Figure 1.
Though the basic idea ofsystems biology is simple, the practice is not, as you would expect from the complexity ofbiological systems. It has taken three key research developments to bring systems biology within reach. One is "high-throughput" technology, tools that can analyze biological materials very rapidly and produce enormous amounts of data. The automatic DNA-sequencing machines that made the sequencing of the human genome possible are examples of high-throughput devices. The second is bioinformatics, which is the use ofcomputational tools to store, organize, and analyze the huge volume of data that result from high-throughput methods. The third key development is the formation of interdisciplinary research teams-melting pots of diverse specialists that may include computer scientists, mathematicians, engineers, chemists, physicists, and, of course, biologists from a variety of fields.
A systems map of interactions among
proteins in a cell. This diagram maps 2,346 proteins (dots) and their network of interadions (lines connecting the proteins) in afruit fly cell. Systems biologists develop such models from huge databases of information about molecules and their interadions in the cell. A major goal of this systems approach is to use the models to predict how one change. such as an increase in the activity of a particular protein, can ripple through the cell's molecular circuitry to cause other changes. The tolal number of proteins in this type of cell is probably in the range of 4,000 10 7,000.
source: Campbell and Reece book
The sequencing of the human genome is a scientific and technological achievement comparable to landing the Apollo astronauts on the moon in 1969. But itis only the beginning of an even bigger research endeavor, an effort to learn how the activities of the myriad proteins encoded by the DNA are coordinated in cells and whole organisms.
The best way to make sense of the deluge of data from genome-sequencing projeds and the growing catalog of known protein unctions is to apply a systems approach at the cellular and molecular levels. Figure 1 illustrates the results of a large study that mapped a network of protein interactions within a cell ofa fruit fly, a popular research organism.
The model is based on a database of thousands ofknown proteins and their known interactions with other proteins. For example, protein A may attach to and alter the activities of proteins B, C. and D, which then go on to interact with stit! other proteins. The figure maps these protein partnerships to their cellular locales.
The basics of the systems strategy are straightforward. First, it is necessary to inventory as many parts of the system as possible, such as all the known genes and proteins in a cell(an application of reductionism). Then it is necessary to investigate how each part behaves in relation to others in the working system-all the protein-protein interactions, in the case of our fly cell example. Finally, with the help of computers and specialized software, it is possible to pool all the data into the kind of system network pictured in Figure 1.
Though the basic idea ofsystems biology is simple, the practice is not, as you would expect from the complexity ofbiological systems. It has taken three key research developments to bring systems biology within reach. One is "high-throughput" technology, tools that can analyze biological materials very rapidly and produce enormous amounts of data. The automatic DNA-sequencing machines that made the sequencing of the human genome possible are examples of high-throughput devices. The second is bioinformatics, which is the use ofcomputational tools to store, organize, and analyze the huge volume of data that result from high-throughput methods. The third key development is the formation of interdisciplinary research teams-melting pots of diverse specialists that may include computer scientists, mathematicians, engineers, chemists, physicists, and, of course, biologists from a variety of fields.
A systems map of interactions among
proteins in a cell. This diagram maps 2,346 proteins (dots) and their network of interadions (lines connecting the proteins) in afruit fly cell. Systems biologists develop such models from huge databases of information about molecules and their interadions in the cell. A major goal of this systems approach is to use the models to predict how one change. such as an increase in the activity of a particular protein, can ripple through the cell's molecular circuitry to cause other changes. The tolal number of proteins in this type of cell is probably in the range of 4,000 10 7,000.
source: Campbell and Reece book