In systems biology, the study of universal principles is often associated with bottom-up modeling of small circuits, such as toggle switches, which are expected to follow the same rules independent of their concrete molecular substrate Tyson et al. It is also exemplified by the search for general network motifs, which implement recurring functions such as signal amplifiers or noise filters at many different places in a biological network Milo et al.
It can be questioned whether the identification of general laws is relevant as a research aim for biology, but universal design principles clearly play a central role in engineering approaches that inspire a large part of systems biology.
It has turned out that principles like robustness and evolvability often lead to recurrent structural arrangements in the cellular machinery — although we still need to understand to which extent these arrangements can be considered as following general laws, and to which extent they are predictive and useful. Complexity , finally, is the most specifically systems biology-related asthetic quality of the three. It exemplifies the third root of systems biology in the areas of systems and network theory.
Systems biology is only justified as a distinct research area because living systems are complex: the interactions of a large variety of distinct components lead to emergent behavior that cannot be predicted when studying only isolated components or subsystems. However, complexity is a difficult concept to define in itself: one could, for example, define the complexity of a system as the length of its description after removing all irrelevant random features — for a biological system that would imply abstracting away the accidents of evolution while still retaining the same functionality.
For instance, the exact sequence of a protein kinase will be irrelevant for its function in a signal transduction cascade, and it will also be coincidence whether the cascade is initiated by a receptor with seven or five transmembrane helices.
And at higher levels, the target of a particular feedback loop may not matter, as long as the resulting gain of the pathway is maintained within the proper limits. It is obvious that discriminating between random and functional features of a complex system can be an arbitrary procedure: which features are to be considered random and which are part of functionality?
Would the relevant description of a fruit fly maintain all mechanisms responsible for the morphological traits of the genus Drosophila , or only general characteristics of insects, or perhaps just the general abstract principles that are essential for any living system? The fact that the evolution of complex systems, including the emergence of high-level features like robustness and modularity, is largely driven by non-adaptive processes Lynch, a , b makes the decision even more challenging.
But no matter where the boundary is drawn, it is clear that any complete description of a biological system would be very voluminous. Systems biologists deal with this issue continuously, condensing our current knowledge into manageable quantitative or qualitative descriptions models , navigating the tricky issues of finding the appropriate level of abstraction and handling the perennial incompleteness of the available data.
The icon of this aspect of systems biology would be the large network map of metabolic and signaling pathways, and as in the case of molecular diversity, the individuality of the network components matters. A protein—protein interaction map in which proteins are anonymous nodes with arbitrary labels that can be randomly permuted is far less complex than a metabolic pathway map with associated individual kinetic and regulatory information and full consideration of the specific biophysical properties of enzymes and metabolites.
However, the excitement about complexity is also far from universal. For many scientists, the particular details and intricacies of the tangled web of cellular interactions are only boring.
The study of complexity seems antithetical to the main movement of biology towards greater reductionism, the progressive dissection of biological mechanisms into ever-smaller components and simpler principles. This apparent conflict will be discussed in more detail below. Systems biology is at its most attractive when all three of these asthetic qualities are evident. A prototypical example would be a comprehensive assessment of transcript diversity to identify simple design principles implementing specific regulatory functions in a complex cellular network, such as Kalir et al.
Thus, good systems biology research should contain a combination of the previous three asthetic qualities. Examples of such activities at the outskirts of systems biology would be provided by a computational modeler who simulates the control structures of a regulatory pathway, but is intimidated by the molecular diversity of the cellular system, or a mathematician who uses correlation structures in gene expression data to infer causal links in the cellular machinery, but treats the individual genes as uniform black-box entities, or the medical biologist who uses genome-wide molecular profiling to study the intricate network underlying a complex disease phenotype, but ignores the relevance of general engineering principles for the understanding of an evolved biological system.
It should also be obvious that method development whether at the theoretical, computational or experimental level is not part of the science of systems biology itself but only provides the necessary tools. Excluding some activities from the core of systems biology naturally leads to an attempt at defining systems biology by clearly and exhaustively stating what does not belong to its realm.
This is necessarily controversial, given the financial and institutional consequences such exclusivity may have. Therefore, it is good to remember that the following is just a personal and non-prescriptive attempt at clarifying the unique characteristics of systems biology.
Systems biology is not holistic, at least not in a simple and exclusive sense; it is not some kind of post-modern non-reductionist science that breaks with a perceived physics-centered methodology inappropriate for the biological sciences.
This topic has been discussed in detail by Bruggeman et al. While systems biology aims at the behavior of biological systems as a whole rather than the behavior of their components in isolation, this activity is perfectly compatible with traditional scientific methodology and reasoning and does not require a weakening of the scientific standards of hypothesis testing and refutation.
The conflict between reductionism and the study of complex systems is largely a straw man, set up to re-emphasize the rather obvious fact that molecules are not alive, only organisms are. Ultimately, systems biology must be predictive, thus it must allow the refutation of hypotheses by targeted perturbation of the system — which is most convincingly done by the rewiring of individual components, thus in a reductionist mode.
Moreover, most of systems biology is crucially dependent on the availability of reliable data from classical experimentation on individual system components. Not all biology is systems biology, nor will it ever be so, and not everybody should do it. Large parts of molecular and cell biology are busily expanding our horizon by studying the natural history of cellular components in isolation or as parts of well-defined sub-structures and locally linear pathways.
This is not only providing essential building blocks for future systems biology, but also continues to be a worthwhile activity in its own right. Finally, not every form of mathematical biology is systems biology, and in particular the study of ecological systems would not be included in the strict definition, even though it has used quantitative modeling and integrated approaches much earlier than the molecular and cell biological domains.
Why then should it be excluded now? The reasons initially are historical modern systems biology grew out of molecular biology and gained momentum in particular after the completion of the human genome project and pragmatic molecular systems allow a far more diverse array of experimental intervention.
These two reasons, however, would be insufficient, especially when considering that molecular principles manifest themselves at both the cellular and the ecological level and that ecological biology clearly shares the same motivating asthetic qualities as systems biology: biodiversity, simple general laws and complex networks of interaction are at the core of the discipline. The reason for this exclusivity will become clear in the next section, where I will describe what I consider the ultimate aim of systems biology.
To define a research field, it can be helpful to try to identify its most ambitious ultimate aim, the question that when answered would finish the research program. How does this translate into an ultimate aim for Systems biology? Boogerd et al. The focus on the deep historical roots of biological phenomena is deeply embedded in the scientific philosophy of biology. This is what is supposed to set biology apart from the non-historical sciences of physics and chemistry.
Initially, an evolutionary viewpoint can help us understanding the general design principles of living systems: for instance, by revealing the common patterns of cellular and developmental circuitry that achieve the necessary balance between robustness and evolvability that characterizes life. In the long run, however, a true understanding of the organizational principles of life will only be demonstrated if we can show that we are able to design entirely novel i.
Systems biology is an experimental science, and many definitions of systems biology include repeated iterations between modeling, prediction and experimentation at their core. Predicting successfully how a system will behave in response to a change in a few parameters, while essentially remaining the same, is not enough to prove a true understanding of how the system works.
For this, it would be necessary to show that one is able to rebuild the system, using new components and new blueprints Kim and Eils, This will not lead to a decreasing appreciation of the existing biodiversity, but deepen and enrich biology in general, as the contingently evolved species are put in perspective by the comparison to the much larger realm of potential species.
This is naturally a very ambitious aim, but the recent emergence of synthetic biology as a seriously debated research activity shows that it may not be unrealistic Endy, ; Channon et al. For more complex systems, we are still far from such an understanding: for instance, we understand developmental biology well enough to create flies with extra wings by a simple, local perturbation , but so far we would have no clue how to engineer a pig with wings — which would require a much more far-reaching rewiring of developmental pathways.
Ethical objections might be raised against the creation of novel life as the ultimate aim of Systems biology, but this does not seem to be justified: as in physics, thought experiments could become a standard part of the conceptual tool kit of systems biology, and careful proof-of-principle studies might remain focused on ethically uncontroversial partial systems.
This ultimate aim will also help to achieve some of the more specific, but no less ambitious, aims of systems biology, such as the provision of personalized medicine Hood et al. For the functional rewiring of the building blocks, already now we are not constrained to follow evolutionary models.
The technological limitations of synthetic biology are very obvious Kwok, , but it is also obvious that many of these coincide with a lack of systems level understanding. The non-linearities and unexpected interactions inherent in complex engineered biosystems are at the same time a main challenge for synthetic biology and the core focus of systems biology.
It can therefore be envisaged that these two emerging disciplines will increasingly align their research agenda. So, what is systems biology?
Based on the preceding discussions, I suggest the following tentative definition: systems biology is the research endeavor that provides the scientific foundation for successful synthetic biology.
It is based on the comprehensive study of the molecular diversity of living systems, both natural and synthetic, the identification of simplifying general principles and patterns that are recurring features in living and engineered systems, and the integration of our biological knowledge in complex models of the regulatory networks that characterize life.
The author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. I thank Eriko Takano and Marnix Medema for their constructive criticism of the manuscript. This work was supported by an NWO Vidi fellowship. National Center for Biotechnology Information , U. Front Physiol. Published online May Prepublished online Mar Author information Article notes Copyright and License information Disclaimer.
Received Mar 3; Accepted Apr This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited. This article has been cited by other articles in PMC.
Abstract Systems biology is increasingly popular, but to many biologists it remains unclear what this new discipline actually encompasses. Keywords: systems biology, computational modeling, synthetic biology. Whether we explicitly recognize it or not, multiscale phenomena are part of our daily lives.
We organize our time in days, months and years, as a result of the multiscale dynamics of the solar system.
Our society is organized in a hierarchical structure, from towns to states, countries and continents. The human body is a complex machine, with many little parts that work by themselves or with other parts to perform specific functions.
Organelles inside each cell in our bodies interact with one another to maintain a healthy functioning cell that moves, differentiates and dies. Two or more types of tissues work together to form an organ that performs a specific task e. Two or more organs work together to form organ systems, such as the digestive system and the nervous system, that perform more complex tasks. All these organ systems interact with each other to enable a healthy functioning organism.
Traditional approaches to modeling real world systems focus on a single scale that imparts a limited understanding of the system. The pace at which biotechnology has grown has enabled us to collect large volumes of data capturing behavior at multiple scales of a biological system. Genetic as well as environmental alterations to the DNA, expression levels of RNAs, expression of genes and synthesis of proteins — all this is measurable now within a matter of days at a rapidly declining cost.
So, it is really up to scientists and data analysts to make use of this variety of data types and build integrative models that enable a comprehensive understanding of the system under study. Multiscale models do just that. By integrating models at different scales and allowing flow of information between them, multiscale models describe a system in its entirety, and as such, are intrinsic to the principles of systems biology.
The statistical analysis of population averages suppresses valuable individual-specific information. Such stratification will allow a proper impedance match against appropriate and effective drugs. Each cell in a cell population of apparently identical cells is a distinct individual. The quantized subtypes perform different functions and form a network — much like a social network in human populations.
So understanding how an organ works will require understanding the coordinated integration of the functioning of all the quantized cell types.
Because of such cellular heterogeneity, even the most potent target-selective drug will kill only a fraction of tumor cells — explaining the inexorable drug resistance in malignant tumors. This new insight on cellular heterogeneity calls for the measurement of all molecular profiles in individual cells. Tissues must be seen not as an amorphous mass but analyzed as dynamical populations of cells and at single-cell resolution.
If DNA is the blueprint for life, then proteins are the bricks. The genes in DNA are translated into proteins, strings of amino acids that fold into three-dimensional structures.
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