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Abstract

This month, the two flagship biology journals of BioMed Central, Journal of Biology and BMC Biology, join forces under the title BMC Biology, as a journal whose aim is to maintain and develop the strengths of both. We have chosen the title BMC Biology not as a signal of the predominance of that journal over Journal of Biology, but to affirm the connection of the fused publication with BioMed Central, and its close relationship with its sibling journals of the BMC series (see [1]). But we like the genetic principle of codominance; and of course hybrid vigor. That said, the fused publication will look and behave more like Journal of Biology than BMC Biology in most ways. We shall continue to publish the topical and authoritative review and comment that have regularly appeared in Journal of Biology, which will also bring its publication policy and speed of response to the fused journal (more on policy below). But listing on the Web of Science and Journal Citation Record will be as BMC Biology. In combining two journals, we are swimming against the tide of ever-proliferating new journals, a point remarked by Gregory Petsko in a Comment [2] written for us to mark the occasion and in which, with the verve and effrontery with which regular readers of his column in our sister journal Genome Biology will be familiar, he deplores such proliferation - inviting, perhaps, dissent. But we agree of course that this particular fusion is rational. In the combined journal, what is new, and what is not? What's new To launch the new BMC Biology, we are publishing the first in an occasional series of special question-and-answer features, in which we invite biologists with a strong personal view on a subject of topical interest or fundamental importance to record a video interview which is posted online with the edited text, and so can be viewed or read, or both, according to preference. Our first interviewee is Martin Raff, the founding Editor-in-Chief of Journal of Biology and member of the Editorial Board of the fused journal. He speaks on autism [3], in which he developed a passionate interest when his grandson was diagnosed at a year and a half as autistic, and tackles issues ranging from the promise of genomic and induced stem cell technologies to the reasons for the apparent increase in incidence. The next Video Q&A, to be posted in May, will be from John Mattick, on the importance and roles of noncoding RNA - just as passionate, and - at least as concerns his perspective on biology - just as personal. We also have a new emblematic image (Figure 1). Figure 1 The BMC Biology image. The problem of representing all of biology is encapsulated in the image we have devised as an emblem for the fusion journal. Our protocellular lipid bilayer surrounding a circular representation of a rootless phylogenetic tree omits explicit reference to molecular and cellular structure and much else; and purists will find fault with the phylogeny. We must ask you all to settle for the Gestalt. What's not BMC Biology and Journal of Biology between them have been committed to the publication of biological research papers of sufficient interest or importance to justify drawing them to the attention of a broad general readership, and papers selected for publication in the fusion journal will reflect, by and large, the selection criteria of both parents, so that the range of papers published will be greater than for either. But although some papers are undoubtedly more worthy of general attention than others, biology, in the main, has become so specialized, and biologists so focused, that there are few research papers that can be comfortably read, still less properly appreciated, by people much outside their immediate field. Journal of Biology has addressed this paradox of the so-called general journal by publishing short commentaries, which it has called minireviews, with two functions. For those papers selected for publication in the journal for their exceptional interest or importance, it has published a commentary explaining the significance of the paper for nonspecialists. Papers making a significant but less striking contribution, including not only many published by BMC Biology, but also a selection of those published in other journals published by BioMed Central, have been the stimulus for minireviews giving a more general perspective on the issues they reflect or address. This will continue in the new BMC Biology, except that the two functions of the minireviews will be explicit in two different names: those on papers of exceptional interest will be called 'Focus', and those with a broader remit will be called 'Commentary'. More of the same, with an experimental twist Journal of Biology will also bring to the fusion its policy (already shared in part with BMC Biology) of taking advice from Editorial Board members on the suitability in principle of submitted papers for the journal before sending them to referees, so that referees are asked to judge only the technical soundness of the paper, and not its level of interest. Authors may, as before, choose to enquire in advance of submission whether their paper will seem as interesting to the journal's advisors and editors as it does to them. A more unconventional contribution to the editorial policy of the fused journal will be the transfer intact from Journal of Biology of its re-review opt-out experiment [4]. This was conceived to address a widespread disgruntlement with current behavioral tendencies of referees, memorably compared in a Comment article by Virginia Walbot in Journal of Biology [5] to those of pit bulls; and to restore a greater share of the responsibility for the quality of the published paper to its authors. The rationale for and operation of re-review opt-out are explained in the editorial [4] we published when we started the experiment, and I will not recapitulate them in detail here. But the essential point is that when authors revise a paper in response to referees (this applies only to revisions, not to resubmissions), they may choose whether the referees are consulted again before publication. We said we would continue the experiment for as long as it was having no clearly adverse effect on our ability to maintain the quality of published papers. This has not happened to date, and we will report back when that changes, or after six months of experience with the fusion journal, whichever is the sooner. Hairballs revisited, the hope of progress, and the diversity of Q&As For the inauguration of the Journal of Biology-BMC Biology fusion, we are launching a new series - 'The hope of progress' - on biology relevant to clinical problems. I have already mentioned our Video Q&A with Martin Raff, which is a special contribution to the series: the two other Hope of progress launch features are reviews on biology-based cancer therapy [6] and on vaccine adjuvants [7], and they are introduced in an accompanying editorial [8], in which some of the issues of psychiatric genomics raised by Martin Raff are briefly discussed. Our other Q&A - non-video - is also relevant to psychiatric genomics, but in the broader context of genome-wide association studies (GWAS) in general. In it, John Brookfield explains [9] the genetic and evolutionary principles underlying the current major collaborative efforts to understand what has become known as the genetic architecture of complex diseases, how they can be bedevilled by the structure of populations, and why they may be most successful for the diseases of old age. Brookfield's Q&A joins earlier Journal of Biology Q&As tackling concepts critical to topical issues in modern biology but with which modern biologists are not always wholly at ease (see [10] for a full listing of Journal of Biology Q&As). The very first of our Q&As was from James Ferrell [10], a lively assault on the confusion of the uninitiated about systems biology, and featuring the familiar systems biology hairball (see Figure 1 in [11]) - a representation of nodes and edges with more iconic than explanatory power. In this inaugural collection for the fusion journal, the hairball is revisited (and indeed the same hairball is reproduced) in an article by Arthur Lander [12], the author of one of our most accessed items of 2009 (on the stem cell concept [13]). Ferrell asked "What is systems biology?" Lander can be said to ask rather "Why is systems biology?" - a question that he answers with an eloquent and absorbing disquisition on the absolute necessity of modelling, at all levels, if we wish to advance beyond knowledge to understanding.

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The 'stem cell' concept: is it holding us back?

Arthur Lander (2009)

Developmental biology, regenerative medicine and cancer biology are increasingly occupied with the molecular characterization of stem cells. Yet recent work adds to a growing body of literature suggesting that 'stemness' cannot be reduced to the molecular features of cell types, and is instead an emergent property of cell lineages under feedback control.

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Are we training pit bulls to review our manuscripts?

Virginia Walbot (2009)

Good early training of graduate students and postdocs is needed to prevent them turning into future generations of manuscript-savaging reviewers. How can we intercalate typical papers into our training?

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Q&A: Systems biology

James Ferrell (2009)

What is systems biology? Systems biology is the study of complex gene networks, protein networks, metabolic networks and so on. The goal is to understand the design principles of living systems. How complex are the systems that systems biologists study? That depends. Some people focus on networks at the 'omics'-scale: whole genomes, proteomes, or metabolomes. These systems can be represented by graphs with thousands of nodes and edges (see Figure 1). Others focus on small subcircuits of the network; say a circuit composed of a few proteins that functions as an amplifier, a switch or a logic gate. Typically, the graphs of these systems possess fewer than a dozen (or so) nodes. Both the large-scale and small-scale approaches have been fruitful. Figure 1 Human protein-protein interaction network. Proteins are shown as yellow nodes. Interactions from CCSB-HI1 (Rual et al., Nature 2005, 437:1173–1178) and from (Stelzl et al., Cell 2005, 122:957–968) are shown as red and green edges, respectively. Literature-Curated Interactions (LCI) extracted from databases (BIND, DIP, HPRD, INTACT and MINT) that are supported by at least 2 publications are shown as blue edges. Interactions common to two of those 3 datasets are represented with the corresponding mixed color (yellow for (Rual et al., 2005) and (Stelzl et al., 2005), magenta for Rual and LCI, cyan for (Stelzl et al., 2005) and LCI). Interactions common to all 3 datasets are shown as black edges. (Figure kindly provided by Nicolas Simonis and Marc Vidal.) Why is systems biology important? Stas Shvartsman at Princeton tells a story that provides a good answer to this question. He likens biology's current status to that of planetary astronomy in the pre-Keplerian era. For millennia people had watched planets wander through the nighttime sky. They named them, gave them symbols, and charted their complicated comings and goings. This era of descriptive planetary astronomy culminated in Tycho Brahe's careful quantitative studies of planetary motion at the end of the 16th century. At this point planetary motion had been described but not understood. Then came Johannes Kepler, who came up with simple theories (elliptical heliocentric orbits; equal areas in equal times) that empirically accounted for Brahe's data. Fifty years later, Newton's law of universal gravitation provided a further abstraction and simplification, with Kepler's laws following as simple consequences. At that point one could argue that the motions of the planets were understood. Systems biology begins with complex biological phenomena and aims to provide a simpler and more abstract framework that explains why these events occur the way they do. Systems biology can be carried out in a 'Keplerian' fashion – look for correlations and empirical relationships that account for data – but the ultimate hope is to arrive at a 'Newtonian' understanding of the simple principles that give rise to the complicated behaviors of complex biological systems. Note that Kepler postulated other less-enduring mathematical models of planetary dynamics. His Mysterium Cosmographicum showed that if you nest spheres and Platonic polyhedra in the right order (sphere-octahedron-sphere-icosahedron-sphere-dodecahedron-sphere-tetrahedron-sphere-cube-sphere), the sizes of the spheres correspond to the relative sizes of the first six planets' orbits. This simple, abstract way of accounting for empirical data was probably just a happy coincidence. Happy coincidences are a potential danger in systems biology as well. Is systems biology the antithesis of reductionism? In a limited sense, yes. Some 'emerging properties', as discussed below, disappear when you reduce a system to its individual components. However, systems biology stands to gain a lot from reductionism, and in this sense systems biology is anything but the antithesis of reductionism. Just as you can build up to an understanding of complex digital circuits by studying individual electronic components, then modular logic gates, and then higher-order combinations of gates, one may well be able to achieve an understanding of complex biological systems by studying proteins and genes, then motifs (see below), and then higher-order combinations of motifs. What are emergent properties? Systems of two proteins or genes can do things that individual proteins/genes cannot. Systems of ten proteins or genes can do things that systems of two proteins/genes cannot. Those things that become possible once a system reaches some level of complexity are termed emergent properties. Can you give a concrete example of an emergent property? Three proteins connected in a simple negative-feedback loop (A → B → C -| A) can function as an oscillator; two proteins (A → B-|A)can not. Two proteins connected in a simple negative-feedback loop can convert constant inputs into pulsatile outputs; a one-protein loop (A -| A) cannot. So pulse generation emerges at the level of a two-protein system and oscillations emerge at the level of a three-protein system. In systems biology there is a lot of talk about nodes and edges. What is a node? An edge? Biological networks are often depicted graphically: for example, you could draw a circle for protein A, a circle for protein B, and a line between them if A regulates B or vice versa. The circles are the nodes in the graph of the A/B system. Nodes can represent genes, proteins, protein complexes, individual states of a protein, and so on. A line connecting two nodes is an edge. The edge can be directed: for example, if A regulates B, we write an arrow – a directed edge – from A to B, whereas if B regulates A we write an arrow from B to A. Or the edge can be undirected; for example, it represents a physical interaction between A and B. Staying with graphs, what's a motif? As defined by Uri Alon, a motif is a statistically over-represented subgraph of a graphical representation of a network. Motifs include things like negative feedback loops, positive feedback loops, and feed-forward systems. Isn't positive feedback the same thing as feed-forward regulation? No. They are completely different. In a positive-feedback system, A activates B and B turns around to activate A. A transitory stimulus that activates A could lock the system into a self-perpetuating state where both A and B are active. In this way, the positive-feedback loop can act like a toggle switch or a flip-flop. A positive-feedback loop behaves much like a double-negative feedback loop, where A and B mutually inhibit each other. That system can act like a toggle switch too, except that it toggles between A on/B off and A off/B on states, rather than between A off/B off and A on/B on states. Good examples of this type of system include the famous lambda phage lysis/lysogeny toggle switch, and the CDK1/Cdc25/Wee1 mitotic trigger. In a feed-forward system, A impinges upon C directly, but A also regulates B, which regulates C. A feed-forward system can be either 'coherent' or 'incoherent', depending upon whether the route through B does the same thing to C as the direct route does. There is no feedback – A affects C, but C does not affect A – and the system cannot function as a toggle switch. A good example of feed-forward regulation is the activation of the protein kinase Akt by the lipid second messanger PIP3 (PIP3 binds Akt, which promotes Akt activation, and PIP3 also stimulates the kinase PDK1, which phosphorylates Akt and further contributes to Akt activation). Since both routes contribute to Akt activation, this is an example of coherent feed-forward regulation. Uri Alon's classic analysis of motifs in Escherichia coli gene regulation identified numerous coherent feed-forward circuits in that system. In high school I hated physics and math, but I loved biology. Should I go into systems biology? No. What kind of physics and math is most useful for understanding biological systems? Some level of comfort in doing simple algebra and calculus is a must. Beyond that, probably the most useful math is nonlinear dynamics. The Strogatz textbook mentioned below is a great introduction to nonlinear dynamics. Do I need to understand differential equations? Systems biologists often model biological processes with ordinary differential equations (ODEs), but the fact is that almost none of them can be solved exactly. (The one that can be solved exactly describes exponential approach to a steady state, and it's something every biologist should work out at some point in his or her training.) Most often, systems biologists solve their ODEs numerically, often with canned software packages like Matlab or Mathematica. Ideally, a model should not only reproduce known biology and predict unknown biology, it should also be 'robust' in important respects. What is robustness, and why is it important to systems biologists? Robustness is the imperviousness of some performance characteristic of a system in the face of some sort of insult – such as stochastic fluctuations, environmental insults, or deletion of nodes from the system. For example, the period of the circadian oscillator is robust with respect to changes in the temperature of the environment. Robustness can be quantitatively defined as the inverse of sensitivity, which itself can be defined a few ways – often sensitivity is taken to be: d ln ⁡ R e s p o n s e d ln ⁡ P e r t u b a t i o n so that robustness becomes d ln ⁡ P e r t u b a t i o n d ln ⁡ R e s p o n s e Robustness is important to systems biologists because of the attractiveness of the idea that a biological system must function reliably in the face of myriad uncertainties. Maybe robustness, more than efficiency or speed, is what evolution must optimize to create successful biological systems. Modeling can provide some insight into the robustness of particular networks and circuits. Just as a biological system must be robust with respect to insults the system is likely to encounter, a successful model should also be robust with respect to parameter choice. If a model 'works', but only for a precisely chosen set of parameters, the system it depicts may be too finicky to be biologically useful, or to have been 'found' in evolution. What other types of models are useful in systems biology? ODE models assume that each dynamical species in the model – each protein, protein complex, RNA, or whatever – is present in large numbers. This is sometimes true in biological systems. For example, regulatory proteins are often present at concentrations of 10 to 1,000 nM. For a four picoliter eukaryotic cell, this corresponds to 24,000 to 2,400,000 molecules per cell. This is probably large enough to warrant ODE modeling. However, genes and some mRNAs are present at concentrations of one or two molecules per cell. At such low numbers, each individual transcriptional event or mRNA degradation event becomes a big deal, and the appropriate type of modeling is stochastic modeling. Sometimes systems are too complicated, or have too many unknown parameters to warrant ODE modeling. In these cases, Boolean models and probabilistic Bayesian models can be particularly useful. Sometimes it is important to see how dynamical behaviors propagate through space, in which case either partial differential equation (PDE) models or stochastic reaction/diffusion models may be just the ticket. Where can I go for more information? Review articles Hartwell LH, Hopfield JJ, Leibler S, Murray AW: From molecular to modular biology. Nature 2005, 402(Suppl):C47–C52. Kirschner M: The meaning of systems biology. Cell 2005, 121:503–504. Kitano H: Systems biology: a brief overview. Science 2002, 295:1662–1664. Textbooks Alon U: An Introduction to Systems Biology: Design Principles of Biological Circuits. Boca Raton, FL: Chapman & Hall/CRC; 2006. Heinrich R, Schuster S: The Regulation of Cellular Systems. Berlin: Springer; 1996. Klipp E, Herwig R, Kowald A, Wierling C, Lehrach H: Systems Biology in Practice: Concepts, Implementation and Application. Weinheim, Germany: Wiley-VCH; 2005. Palsson B: Systems Biology: Properties of Reconstructed Networks. Cambridge University Press; 2006. Strogatz SH: Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering. Boulder, CO: Westview Press; 2001.

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Journal ID (nlm-ta): BMC Biol

Title: BMC Biology

Publisher: BioMed Central

ISSN (Electronic): 1741-7007

Publication date Collection: 2010

Publication date (Electronic): 12 April 2010

Volume: 8

Page: 44

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Publisher ID: 1741-7007-8-44

DOI: 10.1186/1741-7007-8-44

PMC ID: 2864102

PubMed ID: 20388191

SO-VID: de6472cf-50c8-43d5-84cb-8213d7a42e77

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Date received : 8 April 2010

Date accepted : 12 April 2010

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The 'stem cell' concept: is it holding us back?

Are we training pit bulls to review our manuscripts?

Q&A: Systems biology

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