ScienceWeek

MEDICAL BIOLOGY: SYSTEMS BIOLOGY AND PREVENTIVE MEDICINE

The following points are made by L. Hood et al (Science 2004 306:640):

1) Biological information is divided into the digital information of the genome and the environmental cues that arise outside the genome. Integration of these types of information leads to the dynamic execution of instructions associated with the development of organisms and their physiological responses to their environments. The digital information of the genome is ultimately completely knowable, implying that biology is unique among the sciences, in that biologists start their quest for understanding systems with a knowable core of information. Systems biology is a scientific discipline that endeavors to quantify all of the molecular elements of a biological system to assess their interactions and to integrate that information into graphical network models [1-4] that serve as predictive hypotheses to explain emergent behaviors.

2) The genome encodes two major types of information: (i) genes whose proteins execute the functions of life and (ii) cis control elements. Proteins may function alone, in complexes, or in networks that arise from protein interactions or from proteins that are interconnected functionally through small molecules (such as signal transduction or metabolic networks). The cis control elements, together with transcription factors, regulate the levels of expression of individual genes. They also form the linkages and architectures of the gene regulatory networks that integrate dynamically changing inputs from signal transduction pathways and provide dynamically changing outputs to the batteries of genes mediating physiological and developmental responses [5]. The hypothesis that is beginning to revolutionize medicine is that disease may involve perturbations of the normal network structures of a system through genetic perturbations, and/or perturbations by pathological environmental cues, such as infectious agents or chemical carcinogens.

3) A model of a metabolic process (galactose utilization) in yeast was developed by the authors from existing literature data to formulate a network hypothesis that was tested and refined through a series of genetic knockouts and environmental perturbations. Messenger RNA (mRNA) concentrations were monitored for all 6000 genes in the yeast genome, and these data were integrated with protein/protein and protein/DNA interaction data from the literature by a graphical network program.

4) The model provided new insights into the control of a metabolic process and its interactions with other cellular processes. It also suggested several concepts for systems approaches to human disease. Each genetic knockout strain had a distinct pattern of perturbed gene expression, with hundreds of mRNAs changing per knockout. approximately 15% of the perturbed mRNAs potentially encoded secreted proteins. If gene expression in diseased tissues also reveals patterns characteristic of pathologic, genetic, or environmental changes that are, in turn, reflected in the pattern of secreted proteins in the blood, then perhaps blood could serve as a diagnostic window for disease analysis. Furthermore, protein and gene regulatory networks dynamically changed upon exposure of yeast to an environmental perturbation. The dynamic progression of disease should similarly be reflected in temporal change(s) from the normal state to the various stages of disease-perturbed networks.

References (abridged):

1. E. H. Davidson et al., Science 295, 1669 (2002)

2. E. H. Davidson, D. R. McClay, L. Hood, Proc. Natl. Acad. Sci. U.S.A. 100, 1475 (2003)

3. H. Kitano, Science 295, 1662 (2002)

4. U. Alon, Science 301, 1866 (2003)

5. E. V. Rothenberg, E. H. Davidson, in Innate Immunity, R. A. B. Ezekowitz, J. A. Hoffman, Eds. (Humana, Totowa, NJ, 2003), pp. 61-88

Science http://www.sciencemag.org

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THEORETICAL BIOLOGY: ON COMPUTATIONAL AND SYSTEMS BIOLOGY

The following points are made by Albert Goldbeter (Current Biology 2004 14:R601):

1) Systems biology, computational biology, integrative biology --many names are being used to describe an emerging field that is characterized by the application of quantitative theoretical methods and a tendency to take a global view of problems in biology. This field is not entirely novel, but what is clear and significant is that the life sciences community recognizes its increasing importance. This is the really new aspect: many experimentalists are beginning to accept the view that theoretical models and computer simulations can be useful to address the dynamic behavior of complex regulatory networks in biological systems.

2) Theoretical or mathematical biology has existed for many decades, as attested by the journals that carry these terms as part of their names. Until recently, however, these journals were outside of the mainstream and largely ignored by the majority of molecular and cell biologists. As the attitude to theoretical approaches in biology is shifting, it is not surprising to see their revival under new names, if only because a change in name is often needed to focus attention. After all, even at the cellular level, many sensory systems are built to respond to changes in stimulus intensity and adapt to constant signals.

3) The hype that currently surrounds computational and systems biology has the beneficial consequences of triggering further interest and creating a momentum for new opportunities, but it also carries some dangers [1], in particular that of making the field appear merely a fashion. The French stylist Coco Chanel once said la mode, c'est ce qui se demode - fashion is what comes out of fashion . In the view of the author, this does not apply to computational approaches to biological dynamics, which are here to stay.

4) Regarding the surge of interest in theoretical approaches to biology it is natural to ask: why now? One triggering factor is undoubtedly the completion of genome projects for a number of species and realization that the sequences alone cannot tell us how cells and organisms function. Understanding dynamic cellular behavior and making sense of the data that are accumulating at an ever increasing pace requires the study of protein and gene regulatory networks. This network approach naturally encourages one to take a more integrative view of the cell and, at an even higher level, of the whole organism.

5) Quantitative models show that certain types of biological behavior occur only in precise conditions, within a domain bounded by critical parameter values. This can contrast with the intuitive expectations from simple verbal descriptions. This is well illustrated by cellular rhythms [2,3]. Thus, cytosolic Ca2+ oscillations are triggered in various types of cell by treatment with a hormone or neurotransmitter. But repetitive Ca2+ spiking only occurs in a range of stimulation bounded by two critical values: below and above this range, the intracellular Ca2+ concentration reaches a low or a high steady-state level, respectively. Another example is the well-known generation of oscillations in models based on negative feedback. It is straightforward to explain in words why oscillations can readily be generated by negative feedback; but this verbal explanation largely misses the point, as it fails to explain why oscillations only occur in precise conditions, which critically affect both the degree of cooperativity of repression and the delay in the negative feedback loop.(2-5)

References (abridged):

1. North, G. (2003). Biophysics and the place of theory in biology. Curr. Biol. 13, R719-R720

2. Goldbeter, A. (1996). Biochemical Oscillations and Cellular Rhythms. (Cambridge, UK: Cambridge Univ. Press)

3. Goldbeter, A. (2002). Computational approaches to cellular rhythms. Nature 420, 238-245

4. Thomas, R. and d'Ari, R. (1990). Biological Feedback. (Boca Raton, FL: CRC Press)

5. Pomerening, J.R., Sontag, E.D., and Ferrell, J.E.Jr. (2003). Building a cell cycle oscillator: hysteresis and bistability in the activation of Cdc2. Nat. Cell Biol. 5, 346-351

Current Biology http://www.current-biology.com

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THEORETICAL BIOLOGY: ON BIOLOGICAL ANALYSIS

The following points are made by Eors Szathmary (Current Biology 2004 14:R145):

1) Billions of years of evolution have produced organisms of stunning diversity. Some of these are relatively simple, like the bacteria; others show impressive complexity. For two decades, the author has worked on a theoretical reconstruction and understanding of the major transitions that generated the levels of biological organization that we see today. Although many in biology have an antipathy to mathematics, the author "simply cannot live without it." A large part of his research consists of making models of intermediate stages of organization and the evolutionary transitions between them.

2) Although theoretical biology is avoided by many biologists because of their formulae phobia, theoretical biology is not necessarily mathematical, at least not when important ideas and concepts are conceived for the first time. The theory of Charles Darwin (1809-1882), as he presented it, was not mathematical (although later he commented that his reluctance to embrace mathematics was foolish, as mathematically minded persons seem to have an "extra sense"). But neither was the conceptualization by Michael Faraday (1791-1867) of the electromagnetic field: the mathematical structure was built later by James Clerk Maxwell (1831-1879). The theoretical evolutionary embryologist August Weismann (1834-1914) was often more rigorous than Darwin, but still not mathematical.

3) The Golden Age of theoretical biology was the first half of the 20th century, when Ronald Fisher (1860-1962), John Burdon Sanderson Haldane (1892-1964) and Sewall Wright (1889-1988) founded population genetics and Alfred Lotka (1880-1949), Vito Volterra (1860-1940) and Vladimir Kostitzin (1883-1963) started to build up theoretical ecology. These seeds have born many fruits since then. Take evolutionary biology, for example. A few decades after the Golden Age, evolutionary biologists started to tackle (ultimately with considerable success) questions where the Darwinian answer is far from obvious. Why do we age? Why are there sterile insect castes? At first it does not seem to make much sense to argue that your death or sterility increases your fitness. But evolutionary theory can provide satisfactory resolutions of these conundrums. In some cases even the question itself cannot be formulated well enough without some modeling: the problem of the evolutionary maintenance of sex is a case in point. Whole sub-disciplines, like evolutionary game theory, have been set up to meet such challenges.

4) The problems become a lot harder when we come to the large-scale dynamics of evolution. Imagine, say, a thousand Earth-like planets with exactly the same initial conditions of planetary development. After one, two, three billion years (and so on), how many of them would still have living creatures? And would they be like the eukaryotes? We have simply no knowledge about the time evolution of this distribution, and "educated" guesses differ widely.(1-4)

References:

1> Benner, S.A. (2003). Synthetic biology: Act natural. Nature 421, 118

2. Ganti, T. (1971). The Principle of Life (in Hungarian). (Budapest: Gondolat)

3. Ganti, T. (2003). The Principles of Life. ( Oxford University Press)

4. Maynard Smith, J. and Szathmary, E. (1995). The Major Transitions in Evolution. (Oxford: Freeman/Spektrum),

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