La Folie, Quo Vadis, Evo-Devo?

I spend a lot of time at meetings; it is probably a normal part of the ageing process. It also seems that some names are put onto a generic list of people who are often included in discussions on a wide range of topics. Personally I think that it is preferable, when asked, to anticipate in meetings or serve on committees rather than to remain on the outside and complain about the outcome. And if you do not fall asleep during the meeting but actively participate you will probably be invited to more. However, many meetings would be significantly more productive—and even more enjoyable—if all participants could adhere to some basic standards of behaviour.
I am serious about not sleeping. It always amazes me to see someone take a little nap right in the middle of a debate. Of course, the inevitable ringing of a mobile phone eventually wakes these sleepers up. There was a time when mobile phones were a rarity, followed quickly by the phase when their ring tones became the Muzak of meetings. Finally, and to everyone’s relief, the ‘mute’ button was discovered, but now a vibration signals a committee member to catapult out of the chair to the lobby while talking on the phone. This is a real disruption during discussions and it shows that the priority given to the phone puts the meeting in second place.
A more recent development is the increasingly ubiquitous presence of laptops, which have become a similar nuisance. They are, of course, a great tool for digging out missing information and they greatly reduce the amount of paper we have to carry around the globe. But often these aids are used to surf the web or read the news. Although openly reading a newspaper during a meeting would be considered extremely rude, some still seem to think that it is acceptable to sneak a quick read of the latest sports or political news online. I do not think it is. The pervasiveness of BlackBerries is further exacerbating this problem as some cannot switch off their dependence on constant drip-feed of e-mail.
Of course, it can be necessary to consult with a neighbour during a meeting, but loud and persistent personal discussions are a distraction for all and insulting to the speaker. And for some peculiar reason, those speaking in a foreign language seem to lose control of the volume. This is another example of bad meeting manners.
Then there is the ever-present problem of conflict of interest in grant review panels, when a candidate is being considered by a selection board that includes his or her colleague. Some panel members handle such situations appropriately and immediately leave the room. Some need a reminder from the Chair. But there are still those who argue that there is no real conflict and act injured if someone suggests that their presence may influence the outcome in some way. Others leave, but not before making a short speech to highlight a few positive things about the candidate, who thus has an unfair advantage not available to the other applicants. Again, they are not meeting standards.

Systems biology is a new field in the molecular life sciences. It is new as molecular biology was in the fifties and as cell biology was in the seventies. In my definition, systems biology is a science that aims to elucidate the general principles that govern the emergence of biological function from the interactions of components of living systems. Biological function is defined as all it takes for an organism to survive momentarily and under various stresses that reflect what its ancestors have been subject to in evolution. The components are the biological macromolecules, largely (although not completely) encoded by the genome, or higher order aggregates of such components. Indeed, biology appears to be organized in a modular fashion. This is clear in the sense of structure, with examples such as the structure of catalytic units (enzymes), confinement units (membranous vesicles), and inheritable information (chromatin). It is less clear perhaps in the sense of units of function such as metabolic pathways, endocytosis and division. Yet, if only to make understanding by the human mind possible, systems biology also aims to understand cell function in terms of those well- and ill-defined higher order modules.
An academically quite interesting spin-off of the genome sequencing effort has been the verification of what had been suspected: life requires a minimum magnitude. Living organisms do not come much smaller than 1 mm3. Here, a living organism is defined as an organizational unit that does not require other living organisms for its continued existence. This excludes viruses and artificial life. It is an interesting exercise to estimate the minimum size of life, as the process of such estimation entails the realization of several aspects that are essential to it. Because any physical object undergoes damage, be it from cosmic radiation, from predation or from simple diffusion of components, it needs to engage in maintenance processes. According to the second law of thermodynamics, processes dissipate Gibbs energy; hence, life needs to find a way to obtain Gibbs energy from its environment. It must do this at a comparatively high rate to compete with other processes (often of other living organisms that attempt to do the same). Because ‘dead’ (i.e. merely physical-chemical) processes that harvest Gibbs energy in one form or another are leaky, have a low stoichiometry, and are slow at ambient temperature, life needs catalysts that couple (photo-) chemical reactions to Gibbs energy-fixing reactions (such as the synthesis of ATP or the generation of an electrochemical potential difference for protons). The capturing of photon free-energy is simplest if it induces the movement of an electron in space. Part of the corresponding electric energy can then be captured if that movement is (partly) across a membrane closed to ion permeation, and if the electron can recombine with a proton and reduce a transmembrane carrier molecule such as ubiquinone. In molecular terms, the simplest way to make such a stable membrane is a closed bilayer of bipolar molecules, which in a world dominated by carbon, oxygen, phosphate and hydrogen is a phospholipid bilayer. The membrane also serves to keep the catalysts together, but requires more catalysts, this time for the transport of food across that membrane. Clearly, versatile catalysts are needed, which should be encodable. Hence proteins are necessary, with encoding being done by a relatively inert, readily replicable molecule. Lipids, DNA and proteins would have to be synthesized from any form of carbon, hydrogen, oxygen and nitrogen in the environment, which requires metabolic pathways. The latter syntheses need to be catalyzed by enzymes. The simplest way to carry out metabolism is in a modular fashion (i.e. one reaction at a time) employing a series of standard reactions (i.e. dehydrogenases, isomerases, transferases, lyases and, if free energy is needed, ligases). Accordingly, a pathway leading fromglucose to glycerol (a building block of phospholipids) involves some ten such steps (i.e. ten enzymes). Doing the sums, one then readily estimates the need for some 150 reactions (i.e. of some 150 proteins and 150 genes) to sustain life. The actual minimumgenome size is 300 genes therefore our sums bring us to the right order of magnitude.
More important, however, is the realization that life has a minimum threshold. Life consisting of three processes is impossible. Consequently, one cannot expect to carry out a reduction of an organism of 300 genes to 100 subsystems of three genes each, understand each of those subsystems as being alive for one hundredth, add the 100 times understanding of one hundredth of life and then understand life: life is a systems property. And, as the minimum number of required genes is more than 100, the understanding of life somehow requires the simultaneous understanding of more than 100 processes.

Transcriptional regulation networks in cells orchestrate gene expression. In this network the 'nodes' are operons, and each 'edge' is directed from an operon that encodes a transcription factor to an operon that it directly regulates (an operon is one or more genes transcribed on the same mRNA). We asked whether one can decompose such networks into basic building blocks. To accomplish this, we generalize the concept of motifs, widely used in analyzing sequences, to the level of networks. We define 'network motifs', patterns of interconnections that recur in many different parts of a network, at frequencies much higher than in randomized networks that preserve the number of incoming an outgoing edges for each node. We developed algorithms for detecting network motifs and applied them to one of the best-characterized regulation network, that of transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motifs also allow an easily interpretable view of the entire known transcriptional network of the organism. This work is available in pdf form. The transcriptional database contains 577 interactions between 116 TFs and 419 operons. It was based on an existing database (RegulonDB). We enhanced RegulonDB by an extensive literature search, adding 35 new TFs, including alternative sigma factors, and over a hundred new interactions from the literature. The dataset consists of established interactions in which a TF directly binds a regulatory site.

http://www.wretch.cc/album/show.php?i=tear2001&b=1&f=1135926837&p=1
Network models are crucial for shaping our understanding of complex networks and help to explain the origin of observed network characteristics. There are three models that had a direct impact on our understanding of biological networks.
Random networks

http://www.wretch.cc/album/show.php?i=tear2001&b=1&f=1135926836&p=0
Network biology offers a quantifiable description of the networks that characterize various biological systems.Here we define the most basic network measures that allow us to compare and characterize different complex networks.
Degree
The most elementary characteristic of a node is its degree (or connectivity), k,which tells us how many links the node has to other nodes. For example, in the undirected network shown in part of the figure, node A has degree k = 5. In networks in which each link has a selected direction (see figure, part b) there is an incoming degree, kin,which denotes the number of links that point to a node, and an outgoing degree, kout,which denotes the number of links that start from it. For example, node A in part b of the figure has kin = 4 and kout = 1. An undirected network with N nodes and L links is characterized by an average degree = 2L/N (where <> denotes the average).
Degree distribution
The degree distribution,P(k), gives the probability that a selected node has exactly k links.P(k) is obtained by counting the number of nodes N(k) with k = 1, 2… links and dividing by the total number of nodes N. The degree distribution allows us to distinguish between different classes of networks. For example, a peaked degree distribution, as seen in a random network, indicates that the system has a characteristic degree and that there are no highly connected nodes (which are also known as hubs).By contrast, a power-law degree distribution indicates that a few hubs hold together numerous small nodes.
Scale-free networks and the degree exponent
Most biological networks are scale-free,which means that their degree distribution approximates a power law,P(k) ~ k exp(–γ), where γ is the degree exponent and ~ indicates ‘proportional to’. The value of γ determines many properties of the system. The smaller the value of γ, the more important the role of the hubs is in the network.Whereas for γ>3 the hubs are not relevant, for 2> γ>3 there is a hierarchy of hubs,with the most connected hub being in contact with a small fraction of all nodes, and for γ = 2 a hub-and-spoke network emerges,with the largest hub being in contact with a large fraction of all nodes. In general, the unusual properties of scale-free networks are valid only for γ<3,when the dispersion of the P(k) distribution,which is defined as σ2 = – 2, increases with the number of nodes (that is,σ diverges),resulting in a series of unexpected features, such as a high degree of robustness against accidental node failures71. For γ>3, however,most unusual features are absent, and in many respects the scale-free network behaves like a random one.
Shortest path and mean path length
Distance in networks is measured with the path length,which tells us how many links we need to pass through to travel between two nodes.As there are many alternative paths between two nodes, the shortest path — the path with the smallest number of links between the selected nodes — has a special role. In directed networks, the distance AB from node A to node B is often different from the distance BA from B to A. For example, in part b of the figure,BA = 1, whereas AB = 3. Often there is no direct path between two nodes.As shown in part b of the figure, although there is a path from C to A, there is no path from A to C. The mean path length,< >, represents the average over the shortest paths between all pairs of nodes and offers a measure of a network’s overall navigability.
Clustering coefficient
In many networks, if node A is connected to B, and B is connected to C, then it is highly probable that A also has a direct link to C. This phenomenon can be quantified using the clustering coefficient33 CI = 2nI/k(k–1),where nI is the number of links connecting the kI neighbours of node I to each other. In other words,CI gives the number of ‘triangles’that go through node I, whereas kI(kI –1)/2 is the total number of triangles that could pass through node I, should all of node I’s neighbours be connected to each other. For example, only one pair of node A’s five neighbours in part a of the figure are linked together (B and C),which gives nA = 1 and CA = 2/20.By contrast, none of node F’s neighbours link to each other, giving CF = 0. The average clustering coefficient,, characterizes the overall tendency of nodes to form clusters or groups.An important measure of the network’s structure is the function C(k),which is defined as the average clustering coefficient of all nodes with k links.For many real networks C(k) ~ k – 1,which is an indication of a network’s hierarchical character.
The average degree , average path length < > and average clustering coefficient depend on the number of nodes and links (N and L) in the network.By contrast, the P(k) and C(k) functions are independent of the network’s size and they therefore capture a network’s generic features,which allows them to be used to classify various networks.

D. Rind
The climate that we experience results from both ordered forcing and chaotic behavior; the result is a system with characteristics of each. In forecasting prospective climate changes for the next century, the focus has been on the ordered system's responses to anthropogenic forcing. The chaotic component may be much harder to predict, but at this point it is not known how important it will be.
Is the climate system "complex," and does it matter for long-range (decadal-scale) climate forecasts? The answer to the first question is definitely "yes"; the very concept of complexity originally arose in concert with atmospheric processes. To the second question, we have to answer "we don't know." If it is important, it will just make predictions of the anticipated climate change of the next century that much more difficult.

With the discovery of DNA, the completion of genome sequencing of a number of organisms, and the advent of powerful high-throughput measurement technologies such as microarrays, it is now commonly said that biology has gone through a revolution. But I also have heard it said that biology is only about to go through a scientific revolution, much as physics did in the 17th century. In messianic hopes, people foretell the coming of the Newton of biology, but it is up to us, the scientific community, to set the stage for that to happen.
Both views are valid, each in their own sense. The discovery of DNA and the more recent development of powerful new technologies have certainly revolutionized our understanding of the inner workings of life and allowed us to probe deep into the machinery of living organisms, much as the Copernican system and Galileo’s telescope helped revolutionize astronomy. It was Sir Isaac Newton, however, who placed science on a solid footing by formalizing existing knowledge in terms of mathematical models and universal laws. In some sense, this was the real scientific revolution because it permitted prediction of physical phenomena in a general setting, as opposed to simply describing individual observations. The difference is profound. Whereas a mathematical equation can adequately describe a given set of observations, it may be missing the needed universality for making predictions. Kepler’s equations pertained to planets in our solar system. Newton’s laws could be used to predict what would happen to two arbitrary bodies anywhere in the universe. The universality of a scientific theory coupled with mathematical modeling allows us to make testable predictions. This ability will have a profound effect on the field of biology.
The hallmarks of a great scientific theory are universality and simplicity. Newton’s law of gravity is a case in point. The fact that the force of attraction between any two bodies is proportional to the product of their masses and inversely proportional to the square of the distance between them is both universal and simple. These issues are especially important today in the rapidly evolving field of genomics, where formal mathematical and computational methods are becoming indispensable. So what should be our guiding principles, our beacons of scientific inquiry? One such fundamental principle underpinning all scientific investigation is Ockham’s razor, also called the “law of parsimony.”

作者: Joaquin (被收買了...orz) 看板: P_EL_HARZART
標題: 出國
時間: Sat Dec 3 00:02:10 2005

作者: Joaquin (被收買了...orz) 看板: P_EL_HARZART
標題: 風城exile
時間: Mon Oct 31 23:33:15 2005

作者: Joaquin (被收買了...orz) 看板: P_EL_HARZART
標題: 傻了
時間: Sun Oct 30 22:27:27 2005