We inherit our hereditary characteristics from our parents. The basic unit responsible for inheritance in our body is the gene. More technically, a gene is a hereditary unit consisting of a sequence of DNA that occupies a specific location on a chromosome and determines a particular characteristic in an organism. Genes are like words on the long string of DNA. The description of the fundamental process of synthesizing proteins from the information on genes is called the “Central Dogma.” According to this dogma, DNA is used to synthesize RNA, and in turn, RNA is used to synthesize proteins. Hence, this dogma dictates the link between genes and proteins. Proteins are actually a translated and three-dimensional version of the linear information stored in genes.

The Structure of DNA

DNA (Deoxyribonucleic acid) is our repository of genetic information. Although there are organisms such as RNA viruses that possess RNA (ribonucleic acid) as their genetic material, virtually all other living organisms inherit their genes through DNA. Hence, DNA is vital for the existence and perpetuation of life on Earth.

In a simple comparison, DNA can be likened to a sequence of letters where each letter is a single nucleotide, and the alphabet has only four letters: A, T, C and G. Although this alphabet is extremely small compared to those used in human communication today, we are still capable of capturing the vast size of human DNA with this analogy: Our DNA is composed of a sequence of nearly 3 billion (3,000,000,000) of these letters. What this means is that, if you were to type out your genetic code, you would have a 5,000-volume encyclopedia, with each volume containing 400 pages, and each page having 1,500 letters! But then, how do we even fit this formidable size of information in every single cell of our body? The answer lies in the astonishing folding, packaging and wrapping steps DNA goes through upon synthesis. Positioning nucleotides side by side, each DNA molecule would take up about 6 feet (~2 meters) of space. However, after all the packaging steps, DNA becomes compact enough to fit in not only a cell, but also in the microscopic nucleus of each cell.

Genes and the Human Genome Project

Unfortunate for our alphabet analogy above, the 3 billion nucleotides in DNA do not contain any spaces to let us know where each word begins and ends. The Human Genome Project accomplished the task of unraveling what these 3 billion letters are (each one is one of A,T,C and G) and this was a major achievement of humanity. However, it was not until then that we realized the real challenge DNA posed us: Where were the genes in DNA? In other words, how would we understand the words and sentences in this 3-billion string of letters? Apart from efforts to discover the DNA sequences of other organisms, it is not unfair to say that the interest and workforce once focused on the Human Genome Project has now almost completely shifted to this latter “real” challenge of discovering the genes in DNA.

How we wish life could be that easy! Just as completing the human DNA sequence made us realize that we did not know where the genes are, discovering some genes allowed us to understand that we would still be missing a major part of the picture even if we knew exactly where each gene was. Do we not frequently encounter instances in everyday life where one word means different things depending on context? So, is there any good reason to think that genes on our chromosomes will be any less complex? Unfortunately not. Quite to the contrary, the sense is growing that genes are actually far more complex and intricate than we originally thought. For one thing, a single gene may not cause an immediate effect, but may interact with a network of other genes to produce the final effect. Diseases that are caused by individual genes are actually very few, a famous example being cystic fibrosis. But diseases that are affected by the interaction of many genes are far more numerous and prevalent, for example, breast cancer, Alzheimer’s disease, Type 1 diabetes mellitus, multiple sclerosis and obesity.

This latter group of diseases is appropriately called “complex diseases.” Efforts are under way to decipher the intricate genetic and protein networks responsible for such diseases; however, there are so many (known and also unknown) variables that biologists have already called for help. Research problems such as complex diseases that require the interaction of biologists, mathematicians, computer scientists and statisticians alike have led to the advent of the currently very popular field of “Systems Biology.” Viewing the cell as a large factory, this field aims to understand all molecular networks and interactions that make up the very sophisticated machinery in living systems. After deciphering how cells operate flawlessly as a complex system, humans will be better able to discover causes of diseases, and will also be in a much better position to manipulate cells to cure diseases.

The idea of manipulating cells and cell components such as genes and proteins has actually led to “Synthetic Biology,” which is, in essence, the engineering approach to Systems Biology. Synthetic biologists try to engineer gene and protein networks in the cellular machinery to program cells for synthesizing custom-tailored molecules. This can be in the form of redesigning or producing mass amounts of existing molecules, or synthesizing nonexistent molecules that have medical or other potential uses. The overall significance of the field can be well understood by the following quote from one of the pioneers of the field, UC Berkeley professor Jay Keasling: “(Synthetic biology is) doing for biology what electrical engineering did for physics and what chemical engineering has done for chemistry.”

One example of synthetic biology comes from Jay Keasling’s lab. In collaboration with the Gates Foundation and OneWorld Health, the first nonprofit pharmaceutical in the US, Dr. Keasling’s lab is engineering a new metabolic pathway in E.coli to produce the precursor to artemisinin, currently the most effective treatment for malaria. The prospects include a drastic drop in cost, from dollars to dimes. Moreover, success in redesigning a metabolic pathway in bacteria holds great promise for reproducibility for other similar pathways important for the pharmaceutical, cosmetics and food industries.

Genomics vs. proteomics

Molecular biologists, today, are inundated with neologies ending with the suffix “-ome” and “-omics.” The consequence is that the expression “–omics” craze has found its place in the everyday language of these scientists. Basically, the suffix “-om-” refers to a totality of some sort. All the genes considered as a whole in an organism’s cell are called the “genome, and similarly all the proteins this genome can synthesize are referred to as the “proteome.” “Genomics” and “proteomics” refer to the study of the relevant “-ome,” as opposed to studying genes and proteins one by one.

Even though there exist so many –omics words in the literature these days, genomics and proteomics remain the most popular and useful ones. Proteomics can be thought of as the natural successor to genomics because it is fundamentally the next level of complexity after genomics. While scientists explore gene networks and their interactions in genomics, proteomics involves the study of all the proteins and their interactions in the cellular machinery of an organism. Unfortunately, the next level of complexity does not mean “linearly more complex” in this case; studying networks of three-dimensional molecules is an immensely more daunting task than studying those of one-dimensional DNA sequences. However, luckily for us, scientists are up to this challenge. Yet again, we observe a shift in focus in the scientific community from genomics to proteomics.

The main motivation for this shift can be roughly understood with an analogy from marketing or another one from military warfare. In the former, if you want a better marketing strategy for your product, you should target end-users first and foremost. Understanding behavioral patterns and preferences of end-users is much more important than understanding likes of your vendors, because eventually it is the end-user who will determine the demand for your product. In the latter analogy, we think of an army of soldiers who receive orders from a general commander; however, these orders can later be modified or completely annulled by orders from other commanders still in the hierarchical order. If you think about how reliable and informative knowing the orders that each soldier has received from the general commander is going to be, you will understand how useful it will be to have information on genes without supplementary information on proteins. Gene products, either RNAs or proteins, may undergo some steps called “post-translational modification” that are not completely understood, and worse yet may not be completely deterministic (implying random factors).

So, with the help of the analogies mentioned above, we can reason that the shift in focus of the scientific community from genomics to proteomics is mainly due to the fact that biological functions are carried out, not by DNA or genes, but by proteins and (although much less frequently than by proteins) by RNA molecules. For medical and other practical purposes, it is more important to acquire information on the proteome rather than the genome. This, of course, is not to suggest underestimating the importance of the genome. The genome preserves its significance as the origin and source of genetic information. It is just not as beneficial to think about the genome without looking at the final product, that is the proteome.

Conclusion

The completion of the rough draft of the Human Genome Project in 2000 marked the end of the Genetic Era and paved the way to the Genomic Era. The breakthroughs that have taken place since this cornerstone event have been breathtaking, awe-inspiring and maybe even hard to catch up with. The Genomic Era had given birth to different fields in a span of few years, and the biological scientific community has had to shift its focus from genomics to proteomics even without having sorted out the puzzles of the genome. The advent of the “-omics craze” was probably a by-product of this shift because suddenly each sub-field of molecular biology had to adapt a holistic approach in its explorations. Investigating a single entity, whether it be a gene or a protein or another molecule, quickly became stigmatized as “obsolete.”

This transition to a holistic approach has resulted in the interaction of biologists with scientists from quantitative fields such as mathematics, statistics and computer science. These interactions gave rise to truly interdisciplinary research fields such as systems biology, synthetic biology and computational biology. More and more scientists today believe that competence in the future will rely on incorporating expertise from these different fields. With each new discovery, realizing the level of complexity and the intricacy in the design of our body leaves us in true awe. Moreover, these discoveries only make it easier for us to grasp how little we know about the miraculous design of biological systems. On the other hand, this awareness makes us even more motivated to delve into scientific efforts because understanding the science behind creation takes us directly to the understanding of our Creator.

Jason Newfoundland is a PhD candidate in Bioinformatics at University of Michigan.

Notes

1. http://www.answers.com/topic/gene?cat=technology
2. This amazing process is demonstrated in this link: http://www.dnai.org/text/mediashowcase/index2.html?id=556
3. Synthetic Biology: Change on the Horizon, Karsten Temme, http://no.oneslistening.com/277
4. A glossary for –omics words exists at http://www.genomicglossaries.com/content/omes.asp

In his Models of My Life, social scientist Herbert Simon stated that science’s purpose is to find meaningful simplicity in the midst of disorderly complexity. Although the systems in nature and society seem to be very complex, we need simple theories to understand them. The key to finding these simple rules or principles, which Plato called perfect form and thing-in-itself and Kant described as a kind of untouchable essence behind physical things or systems, is mathematics.

Mathematics and social science

One of mathematics’ main challenges is its uses in social sciences. But does mathematics, which is considered rigid and restrictive and the best tool for explaining matter, have laws for human life? The latest studies in sociology, biology, and epidemiology show that such laws and meaningful patterns do exist and can be discovered. One of the great achievements of mathematics and physics in sociology is explaining the six degrees of separation (i.e., the small world) phenomenon.(1)

Almost everyone has met someone far from our home who is a friend of a friend. This is so common that it has become a clich: It’s a small world “ even though there are now more than 6 billion people. Even more surprising is the structure of social networks, the map of who knows whom, which shows that all people are closely connected.(2)

A social network is a collection of people, each of whom is acquainted with some subset of others. This can be represented as a set of points (nodes) denoting people, joined in pairs by lines (edges) denoting acquaintance, in a company, university, or even a global community. Social scientists have studied such networks, both empirically and theoretically, for at least 50 years.

A revealing experiment

One of the first and famous empirical studies was conducted in 1967 by Stanley Milgram, a Harvard psychology professor: the small world phenomenon. He asked: Starting with any two people in the world, what is the probability that they will know one another? In a large social network, although X and Z might not know each other, they might have a mutual acquaintance. Moreover, X might be linked to Z by a series of links. In other words, X knows a, who knows b, who knows c … who knows y, who knows Z. But how many intermediate acquaintance links are needed, on the average, to connect X and Z?

According to Milgram (3), this phenomenon raises the issue of a certain mathematical structure in society, one that often plays a part in discussions about history, sociology, and other disciplines. For example, during western Europe’s Dark Age, cities became isolated because inter-city communications broke down and thus severely limited the network of individual acquaintances. Thus, social disintegration was expressed in the communities’ growing isolation and people’s infrequent contact with non-local people.

Milgram asked his test subjects, chosen at random from Nebraskan and Kansan telephone directories, to get a letter to one of his Boston stockbroker friends. The letters were to be sent passing them from one person to another, but only if both parties were on a first-name basis. Since it was unlikely that the letter’s initial recipient and a Boston stockbroker would be on a first-name basis, their best strategy was to pass the letter to someone whom they felt would be socially or geographically closer to the stockbroker.

A moderate number of the letters eventually reached their destination. Milgram discovered that the average number of steps in this process was about six. This is usually considered evidence of the small world hypothesis: Most pairs of people, even in a very large population, can be connected by a short chain of intermediate acquaintances.

Milgram’s work passed into folklore and was immortalized in John Guare’s 1990 play Six Degrees of Separation, where Ouisa claims: Everybody on this planet is separated by only six other people. Six degrees of separation. Between us and everybody else on this planet. The president of the United States. A gondolier in Venice…. It’s not just the big names. It’s anyone. A native in a rain forest. A Tierra del Fuegan. An Eskimo. I am bound to everyone on this planet by a trail of six people. It’s a profound thought…. How every person is a new door, opening to other worlds.(4)

Watts and Strogatz used this model to explain the six degrees of separation in society- and nature-based networks.(5) Their model employs mathematical graph theory, which consists of nodes (people) and links (acquaintance). They modified the regular network, in which every node has short-range fixed number of connections, by adding long-range connections to the regular network to simulate the short path length between nodes. This model is now used in such areas as networks connected with biological metabolism, genomes, proteins, the Internet backbone, power grids, companies, and the stock market.

Research on social networks has helped health professionals understand how epidemics spread. Epidemiological models help them to predict how fast a disease will spread and to develop small world theory strategies to combat them, for scientists now realize that this social network property is a main cause for the outbreak of epidemics. This network offers a super-connected web of stepping stones for infectious diseases. After analyzing the social network’s structure, public health specialists can devise new vaccination strategies to slow or stop the epidemic. The small world theory also explains the rapid spread of news, rumors, fashions, and gossip.

Other types of systems show similar properties: networks of actors involved in the same movie, scientific collaboration networks of scientists who coauthor an article, and even the Internet, where millions of web pages are connected via mutual links. Recent research shows that in all of these networks, there are only a few steps between one node and any other node. The average shortest path between a network’s nodes, is 3.5 for the actor network and 9.5 for the scientific collaboration network. Despite the Internet’s more than 800 million nodes, there are, on average, only 19 steps between one web page and any other.(6)

Conclusion

The small world theory is a great success of the theory of complexity in nature and social life. It reveals an underlying dynamic of interconnectedness that expresses itself indelibly in who we are and how we think, behave, and communicate. Such close interconnectedness reveals one important fact for constructing a peaceful and beneficial world: There are no strangers, for everyone is one of our friends’ or neighbors’ friend or relative. The key element in understanding people is communication. Mutual love and good relations continue as long as we understand each other. We are loyal and faithful to the extent that we share our friends’ troubles, because ignorance only builds an impenetrable wall between us.(7)

Hasan Guclu is a doctoral student in the field of very large complex systems, such as computer networks, social networks, epidemics, and surface growth.

Footnotes

1. See, respectively, A. L. Barabasi, Linked (Perseus Publishing: 2002); D. J. Watts, Small Worlds (Princeton University Press: 1999); and M. Buchanan, Nexus (W. W. Norton: 2002).
2. D. J. Watts and S. H. Strogatz, Collective Dynamics of Small World Networks, Nature 393 (1998):440-42.
3. S. Milgram, The Individual In A Social World (Addison-Wesley: 1977).
4. J. Guare, Six Degrees of Separation: A Play (Vintage: 1990).
5. Watts and Strogatz, Collective Dynamics.
6. A. L. Barabasi, Linked.
7. F. Gulen, Pearls of Wisdom (The Fountain: 2001).

ATM is currently one of the most attractive technologies in the digital data communications field. In ATM, information is transmitted in short fixed-length blocks that are called cells (Figure 1). An ATM cell consists of a header and a data field that carries the actual information-either video, voice or data. The cell header contains a label denoting the routing address.

Not only was ATM created to overcome the difficulties in existing transfer methods but also to allow the creation of a Broadband Integrated Service Digital Network (B-ISDN).

Transmission speeds up to the physical limits (e.g., 622.05 Mbps signaling rate nowadays) are achievable with ATM. By contrast, there are limitations to the upgrading possible, with the most popular traditional Local Area Network (LAN) topologies (e.g., Ethernet and Token Ring), to higher bandwidth, which means incapacity to support current and of course future real-time applications.

Although the connection-oriented ATM was initially designed for Wide Area Networks (WANs), its unique features including flexibility, scalability, high transfer capacity and support for multimedia applications, also fired the imagination of the LAN vendors in the early 1990s. Since ATM is suitable for both LANs and WANs, historical separation of traditional LANs that are connectionless and WANs that are connection-oriented will eventually disappear. This will form a universal platform for data communications as well as replace the conventional LAN topologies. With full deployment of ATM in design, manufacturing and maintenance of the future networks, the overall costs will be relatively smaller.

The traditional LANs use a shared media-access (e.g. bus) method. That means all stations of the network have to share one transmission medium and have to contend for access. The limitations of shared media-access method are overcome by the ATM’s new approach of switching systems based on central media-access management (Figure 2). Since each user has a dedicated connection to one of the ATM switch ports, users no longer need to contend for access as opposed to the legacy LANs. Moreover ATM LAN users are provided WAN services through another port on the LAN switch. On the other side, the traditional LANs have no direct wide-area capabilities-they must depend on a separate piece of equipment to convert the LAN rates and protocols into WAN-compatible format.

Problems that arise in transmission through the current networks of voice, video and data simultaneously (or in real-time) could well be prevented by using ATM.

With its low-cost networking and technology ATM will soon provide scientist and engineers greater global freedom to exchange data, images (e.g., medical imaging applications) and models in real-time. Beyond the physical boundaries of classrooms, students (especially disabled) will be able to be part of a class and interact with others just as though they were there using high-speed and reliable ATM networks.

GLOSSARY

B-ISDN: A high-speed (above 1.544 Mbps signaling rate) network standard that grew from traditional narrowband ISDN.

Connection-oriented: A network (e.g., WANs) that establishes, either permanently or on a call-by-call basis, a specific circuit path for transmission.

Connectionless: A network (e.g., legacy LANs) in which no particular path is established for the transfer of information.

Ethernet: A LAN using 10 Mbps signaling rate.

LAN: High-speed network connecting personal computers, printers, and other data equipment within an office or campus.

Mbps: Mega bit per second

Token Ring: A LAN using 4 or 16 Mbps signaling rate.

WAN: High-speed network connecting communications equipment nationally and internationally.

References

• PARULKAR G. M.: ‘Local ATM Networks’, IEEE Network March 1993, p.S-9.
• VENIERIS I. S., ANGELOPOULOS J. D. & STASSINOPOULOS G. I.:
• ‘Efficient Use of Protocol Stacks for LAN/MAN-ATM Interworking’, IEEE Journal on Selected Areas in Communications October 1993, 11(8) p.1160-1170,.
• KIM B.G. & WANG P.: ‘ATM Network: Goals and Challenges’, Communications of the ACM February 199538 (2), pp. 39-44.
• The ATM Forum: http:/ /www.atmforum.com/