See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/238851947
Uri Alon, An Introduction to Systems Biology: Design Principles of Biological
Circuits, Chapman & Hall/CRC, London, ISBN 1584886420, GBP 30.99, 2007
(320 pp.)
Article in Mathematical Biosciences · October 2008
DOI: 10.1016/j.mbs.2008.07.002
CITATIONS
READS
5
20,727
1 author:
Armindo Salvador
University of Coimbra
105 PUBLICATIONS 1,239 CITATIONS
SEE PROFILE
All content following this page was uploaded by Armindo Salvador on 28 December 2017.
The user has requested enhancement of the downloaded file.
Book review: Uri Alon (2007). “An introduction to systems biology: design principles of
biological circuits” London: Chapman & Hall/CRC
ISBN 1584886420, GBP 30.99, 320 pp
Armindo Salvador
Molecular Systems Biology Group – Centre for Neuroscience and Cell Biology
and
Biological Chemistry Group – Chemistry Department
The University of Coimbra
Largo D. Diniz
3004-535 Coimbra
Portugal
Email: salvador@cnc.uc.pt
Phone: +351-91-9619593
Fax: +351-239-827703
Over a dozen books carrying the term “systems biology” in the title have been published
recently, riding the recent rise of this field to scientific prominence. Most of these books [e.
g., 1; 2; 3; 4; 5]1 emphasize the analysis of “omic” experimental data and/or large-scale
mathematical modeling so as to describe how biological systems work at the molecular level.
While this is doubtlessly a very important problem in biology, the object of this review —
Uri Alon (2007). “An introduction to systems biology: design principles of biological
circuits” London: Chapman & Hall/CRC (ISBN 1584886420, GBP 30.99, 320 pp) — focuses
1
This is just a list of books I happened to have the occasion to consult recently. It is neither the result of a
systematic survey of the literature on systems biology or an implicit endorsement of these books.
on a no less important problem. Namely, that of discovering design principles in biological
circuits and understanding why nature adheres to those principles.
From decades of research in molecular biology it is emerging that nature has
converged time and again on similar molecular circuits. Such extensive convergent evolution
owes to a combination of the following four factors. First, various physical-chemical
constraints limit what molecular circuits are feasible. Second, not all feasible circuits are
equally likely to be created form an existing biological network by random mutation. Third,
only circuits that function very well and whose performance is robust to fluctuations enable
the carrying organisms to survive natural selection. Fourth, similar functional properties (e.
g., fast responses, robustness to perturbations, high gains of outputs with respect to inputs)
are required quite often in different contexts. Because most feasible circuits that could evolve
from existing biological networks through a small number of frequent genetic events (mostly
point mutations, gene duplications and gene deletions) are rarely found in living organisms,
the last two factors are critical for explaining the observed convergence. This convergence
and its underlying causes have deep implications. They mean that molecular biology might
one day be structured around a number of simple laws or principles whose understanding
hinges largely on engineering considerations similar to those applying to human-designed
circuits. The major breakthroughs in the exact sciences occurred when the main regularities
(laws) were discovered and then explained. From this process ensued the predictive power
that earned these sciences the qualifier “exact”, which still sets them apart from biology. If a
similar process is nowadays taking place in molecular biology this is largely through the
discovery and explanation of design principles.
The focus on discovering and explaining engineering-derived design principles of
biological networks was pioneered by Michael Savageau in the early 1970’s. And indeed it is
to Michael Savageau’s 1976 classic “Biochemical Systems Analysis: A Study of Function
and Design in Molecular Biology” [6] that Uri Alon’s book most directly compares. Both
books focus on design principles of biological networks, both are designed to serve as
textbooks for graduate or advanced undergraduate students with a standard background in
either biological or exact sciences/engineering, both are highly relevant and thoughtprovoking reading for advanced researchers as well, both draw extensively on the respective
author’s research, both are rich in bright insights on why biological circuits are as they are.
Unfortunately, Michael Savageau’s book has long been out of print, and used volumes are
currently selling for US$300. However, the lucky reader that manages to get hold of both
books will find that despite the above-mentioned similarities these books cover
complementary topics, use different approaches and offer complementary perspectives on
topics they have in common. Thus, whereas Savageau’s book devotes considerable space to
discussing mathematical modeling approaches and trades conciseness for deeper and more
nuanced discussions of the various topics, Alon’s book discusses just the strictly necessary
essentials of modeling and emphasizes conciseness and simplicity. The latter book also
benefits greatly from the hindsight gained from 30 intervening years of biological research
and, in particular, from the author’s direct experimental tests of key concepts. It includes
chapters on bacterial chemotaxis, robust patterning in development, kinetic proofreading and
optimal gene expression levels, topics that were less extensively or not at all covered in
Savageau’s book. On the other hand, it gives little attention to metabolic networks, which
deserved substantial attention in Savageau’s book.
The field of Systems Biology often appears daunting to biology and exact
sciences/engineering students alike. To the former because of the mathematics involved, to
the latter because of the amount of biological information they perceive as having to learn.
Uri Alon’s book contributes substantially to attenuate these fears. It provides very concise
and clear explanations of the basic biology involved and then goes on to develop an intuitive
understanding of the issues based on very simple “toy models”. More-detailed discussions of
technical aspects are deferred to appendices so that they do not interfere with the flow of the
main text. The required mathematical skills are, in general, within the reach of advanced
undergraduate Biology students. Each chapter is complemented with a superb set of
exercises. Each of these sets starts with a few problems whose solution is explained step by
step and then presents several problems whose solutions are not provided. The exercises are
not only designed to train the student’s analytical skills, but each addresses also a relevant
and stimulating biological issue or elaborates on issues discussed in the respective chapter.
They are neither trivial nor too daunting. Additionally, each chapter contains a “Further
reading” section that provides a short selection of the most relevant references about each
topic. (A more extensive list of cited and “interesting” references is provided at the end of the
book.)
The printing I reviewed contains a fair number of typos. However, these are just a
minor nuisance and do not hinder understanding of the text.
Most of the book is structured around the notion of network motifs [7; 8]. It is thus
surprising, and what I find the book’s main drawback, that it contains little critical discussion
of the implications and caveats of this notion. Because the biological meaning and
implications of the network motifs are prone to misinterpretation, it is worth discussing these
issues here. Network motifs are defined as patterns of interactions that occur in a regulatory
network significantly more often than in randomized networks that preserve the same number
of edges (i. e. interactions) and nodes (e. g., molecular species) [7; 8]. Alon and co-workers
hypothesize that patterns of interactions that qualify as network motifs must have been
selected based on some advantage they give to the organisms. Because molecular interactions
are easily lost and created by mutation, they argue, mutational drift would otherwise quickly
have randomized those patterns. The idea of looking for patterns that are significantly overrepresented over what is expected in absence of selection has been quite successful in
biological sequence analysis as a way to pinpoint binding sites for transcription factors and
other very recurrent sequence features. However, its practical application to biological
networks runs into some difficulties. First, the expected distribution of interaction patterns in
absence of selection may depart substantially from the randomized networks envisaged by
Alon and co-workers. This happens because topological and physical-chemical constraints
prevent some interactions and favor others, and because the process whereby new genes are
created — often gene duplication, with the daughter gene initially inheriting the interactions
of the parent gene — further biases the distribution of interaction patterns. For instance, in
metabolic networks each metabolite’s intrinsic chemical properties severely limit the
reactions in which it can participate, and in protein interaction networks co-localization
favors the creation of specific interactions [10]. Some patterns may thus qualify as network
motifs (in the sense defined above) as consequence of these constraints and bias rather than
as consequence of selection [9]. This problem of evolutionary interpretation becomes acute
where, as in Chapter 6, profiles of motifs are compared among different types of networks
that are subject to distinct constraints and bias. The problem might be addressed by
evaluating under/over-representation of the patterns with respect to more-realistic null models
that take all the constraints and bias above into account. However, how such null models
could be constructed remains unclear. A second concern is that statistics of interaction
patterns may be biased by the presently incomplete and fairly unreliable knowledge about the
structure of some biological networks.
Suppose now that we have comprehensive accurate information about a biological
network, used the correct null model and found some network motifs with respect to this null
model. These network motifs would then truly represent patterns that occur more often than
they would in absence of natural selection. Why would these patterns become motifs? The
prime necessary condition for a pattern of interactions to become a network motif in the sense
defined in this paragraph is that it can provide function(s) that are required in many instances
in the network. Furthermore, in order to be selected over other patterns of interactions that
can provide similar functions the motifs should perform better than these alternatives.
However, non-motifs do not necessarily perform poorly where they occur, nor are they
necessarily under weaker selection than motifs. They may just provide more unique but no
less essential or reliable functions.
Uri Alon partly deflects the concerns above by presenting network motifs simply as
“a way to detect building-block patterns in complex networks” (p. 27). Indeed — and
irrespective of the added value of statistical comparisons with respect to a questionable null
model — the results and discussions in the book convincingly show that transcription and
other biological networks are mostly composed of a small number of highly recurrent
patterns of interactions, and that the frequent patterns differ among different types of
networks in a way that can be rationalized with reference to the networks’ functions. It also
shows that in the right conditions the highly prevalent patterns can perform functions whose
biological usefulness in the context where the patterns occur is plausible. In some cases it
goes on to show that other patterns of interaction that are less prevalent tend perform those
functions less effectively or less robustly.
In face of the results above, it is tempting to think that a motif’s function could be
univocally inferred from its structure or from minimal information about the nature of the
interactions involved, and that a universal set of design principles would apply to all
realizations of each motif. Unfortunately, these expectations may be over-optimistic. Many
network motifs can perform several different functions, depending on context, on the
properties of the edges, on how the inputs from different edges relate to each other, etc. This
is illustrated by the incoherent type 2 feed-forward-loop (FFL, Figure 1), which is one of
eight possible types of FFL in transcriptional networks. Focusing just in the case where the
same signal modulates both transcription factors and exploring a broad range of parameter
values and the three qualitatively different ways how the signal might influence the action of
each transcription factor, Wall et al. [11] find an extensive repertoire of input-output patterns.
This repertoire of behaviors allows these circuits to alternatively provide various different
functions. Compounding the problem, transcription networks do not represent a totally
autonomous layer of regulation, but are instead enmeshed with metabolic and protein
interaction networks. Thus, many “edges” in transcription networks may actually represent
complex processes, inputs to the network motifs may be correlated, and there may be external
(i. e. non-transcriptional) regulatory loops connecting outputs to inputs. This suggests that
one cannot in general reliably ascribe a function to a specific realization of a network motif
without close examination of its parameterization and biological context.
Perhaps nature will again reveal itself simpler than it could be, and it will turn out that
most instances of the same network motif provide the same function and adhere to similar
design principles. This can only be ascertained after studying many more biological circuits
in detail. However, it has been a constant in the history of science that the laws of nature were
only discovered through painstaking work in identifying and eliminating potential
confounding factors in a deliberate search for regularities. Uri Alon’s book is an excellent
contribution to motivate and prepare minds for such an endeavor in molecular biology. It is
thus a must-read for any student entering the field of systems biology and a very stimulating
and enjoyable reading for any researcher in the field.
References
[1] L. Alberghina, and H.V. Westerhoff, (Eds.), Systems biology: definitions and
perspectives, Springer, Berlin, 2005.
[2] E. Klipp, R. Herwig, A. Kowald, and C. Wierling, Systems Biology in Practice: Concepts,
Implementation and Application, Wiley-VCH, Weinheim, 2005.
[3] A. Kriete, and R. Eils, (Eds.), Computational systems biology, Academic Press, San
Diego, 2006.
[4] B.O. Palsson, Systems biology: properties of reconstructed networks, Cambridge
University Press, New York, 2006.
[5] I. Rigoutsos, and G. Stephanopoulos, (Eds.), Systems Biology, Oxford University Press,
New York, 2007.
[6] M.A. Savageau, Biochemical Systems Analysis: A Study of Function and Design in
Molecular Biology, Addison-Wesley, Reading, Mass., 1976.
[7] S.S. Shen-Orr, R. Milo, S. Mangan, and U. Alon, Network motifs in the transcriptional
regulation network of Escherichia coli. Nature Genetics 31 (2002) 64-68.
[8] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, Network
Motifs: Simple Building Blocks of Complex Networks. Science 298 (2002) 824-827.
[9] Y. Artzy-Randrup, S.J. Fleishman, N. Ben Tal, and L. Stone, Comment on "Network
Motifs: Simple Building Blocks of Complex Networks" and "Superfamilies of
Evolved and Designed Networks". Science 305 (2004) 1107c.
[10] J. Kuriyan, and D. Eisenberg, The origin of protein interactions and allostery in
colocalization. nature 450 (2007) 983-990.
[11] M.E. Wall, M.J. Dunlop, and W.S. Hlavacek, Multiple functions of a feed-forward-loop
gene circuit. Journal Of Molecular Biology 349 (2005) 501-514.
Figure 1: The incoherent type 2 feed-forward-loop. The –| symbols denote repression.
X
Y
Z
View publication stats