I know that
17 years is not a very round number. It is also fairly arbitrary as I am
assuming systems biology started around 2000 (see below). I was last week in
Portugal, where every year for the past 8 years I have been teaching a week
long course on Systems and Synthetic Biology to the GABBA PhD program. This
might be the last year I take part in this course and so I felt it would be a
good time to try to put some thoughts in a blog post. This
course has been jointly co-organised from the beginning with Silvia Santos and we
had several guests throughout the years including Mol Sys Bio editors Thomas
Lemberger and Maria Polychronidou and other PIs: Julio Saez-Rodriguez, Andre Brown,
Hyun Youk and Paulo Aguiar. Some of what I write below has been certainly influenced by discussions
with them. This is not meant as an extensive review so apologies in advance for missing references.
Where
did systems biology come from?
It is not
contentious to say that systems biology came about in response to the ever
narrower view of reductionist approaches in biology. Reductionism is still
extremely important and I assume that, as a movement, it was an opposition to
the idea that biology was animated by some magical force that could never be comprehended.
Since the beginning of the course we have asked students to read the assay “Can a biologist fix a radio?” by Yuri Lazebnik (2002). The article captures well the limitations
of reductionist research. The more we know about a system, apoptosis in Yuri's case, the more complex and non-intuitive some observations may seem. Yuri's
description of how a biologist would try to understand how a radio works is comical
and still very apt today:
We would “remove components one at a time or to
use a variation of the method, in which a radio is shot at a close range with
metal particles. In the latter case radios that malfunction (have a
“phenotype”) are selected to identify the component whose damage causes the
phenotype. Although removing some components will have only an attenuating
effect, a lucky postdoc will accidentally find a wire whose deficiency will
stop the music completely. The jubilant fellow will name the wire Serendipitously
Recovered Component (Src) and then find that Src is required because it is the
only link between a long extendable object and the rest of the radio.”
One of the
driving forces for the advent of systems biology was this limitation, so brilliantly
captured by Yuri, that reductionism can fail when we are overwhelmed with large
systems of interconnected components.
Around the
time that Yuri wrote this article our capacity to make measurements of
biological objects was undergoing a revolution we generally call omics today.
In 2001 the first drafts of the human genome were
published (Lander et al. 2001; Venter et al. 2001). Between 2000 and 2002 we had the first descriptions of large scale
protein-protein (Uetz et al. 2000; Ito et al. 2001; Gavin et al. 2002) and genetic interactions mapping (Tong et al. 2001). The capacity to
systematically measure all aspects of biology appeared to be within our grasp.
The interaction network representation of nodes connected by edges is now an
icon in biology, even if not as recognisable as the double helix. This ever
increasing capacity to systematically measure biology was, alongside the
complexity of highly connected components, the second major driving force for
the advent of systems biology.
What is
systems biology?
So around
2000 biology was faced with this upcoming flood of data and highly complex nonlinear
systems. Reductionism was failing because mental models were insufficient to cope
with the information available. The reaction was a call for increased
formalism, better ways to see how the sum of the parts really works. Perspectives
were written (Kitano 2002) and institutes were born (Institute for SystemsBiology). Within the apparent complexity of biology there might be emergent
principles that we were not seeing simply because we were looking too narrowly
and could not combine information in a formal way. Whatever the system of
interest (e.g. proteins, cells, organisms, ecosystems) there must ways to take
information from one level of abstraction (e.g. proteins) and understand the
relevant system's features of the abstraction layer above it (e.g. cell behaviours).
This comes closest to a definition of systems biology put forward by Tony Hyman (Hyman 2011) but many others have defined it in vaguely similar ways, or maybe in similarly
vague ways.
Power
laws and the perils of searching for universal principles
When introducing
systems biology I have been giving two examples of work that illustrate
some of the benefits (network motifs) but also some of the perils (power law
networks) of trying to find universal principles in biology. One of these
examples was the research on the organisation of biological networks. As soon
as different networks were starting to be assembled, such as protein-protein,
genetic and metabolic networks, an observation was made that the distribution
of interactions per gene/protein is not random (as studied by Paul Erdös). Most
proteins have very few interactions while some rare proteins have a
disproportional large amount of interactions – dubbed “hubs”. Barabasi and many others had a series of
papers describing these non-random distributions, called power-law networks (Jeong et al. 2000), in
all sorts of biological networks. Analogies were drawn to other
non-biological networks with similar properties and it is not an understatement
to say that there was some hype around this. The hope was that by thinking of
the common processes that can give rise to such networks (e.g. preferential
attachment) we would know, in some deep way, how biology is organised. I will
just say that I don’t think this went very far. Modelling biological networks as
nodes and edges allowed the application of graph theory approaches to biology,
which has indeed been a very useful inheritance from this work. However, we
didn't find deep meaning in the analogies drawn between the different
biological and man-made networks, although I am sure some will disagree.
Network motifs, buzzers and blinkers
Around the
same time, the group of Uri Alon published very influential work describing recurring
network motifs in directed networks (Milo et al. 2002; Shen-Orr et al. 2002). For example, in the E. coli transcriptional network
they found some regulatory relationships between 3 different genes/operons that
occurred more often than expected by chance. One example, illustrated to the
right, was named a coherent feedforward loop where an activating signal was
sent from an “upstream” element X to a “downstream” element Z both directly and
indirectly via an intermediate third element. The observation begs the question
of the usefulness of such an arrangement (Mangan and Alon 2003; Kalir et al. 2005). This has been generalised to studying
the relation between any set of such directed interactions with specific
reaction parameters – defined as the topology - and their potential functions. In
a great review Tyson, Chen and Novak summarise some of these ideas of how
regulatory networks can act, among other things as “sniffers, buzzers, toggles
and blinkers” (Tyson et al. 2003).
These and
other similar works showed that, within the complexity of regulatory networks, design
principles can be found that encapsulate the core relationships giving rise
to a behaviour. Once these rules are known, an observed behaviour will
constrain the possible space of topologies that can explain it. This has led
researchers to search for missing regulatory interactions that are needed to
satisfy such expected constraints. For example, Holt and colleagues searched
for a positive feedback that would be expected to exist for the switch-like
dissolution of the sister-chromatid cohesion at the start of anaphase (Holt et al. 2008). This mapping between regulatory networks and
their function can be applied to any system of interest and at any scale. The
same types of regulatory interactions are used for termites building spatially
organised mounds and for growing neurons seeking to form connections (as
illustrated in a review by Dehmelt and Bastiaens). Different communities of
scientists can come together in systems biology meetings and talk in the same
language of design principles. This elegance of finding “universal” rules that
seemingly explain complex behaviours across different systems and disciplines has
been a great gift of systems biology. It is of course important to point out
that such ideas have a much longer history from homoeostasis in biology and
control theory in engineering.
Bottom-up
network models
Alongside
the search for design principles in regulatory interactions the formal mathematical
and computational modelling of biological systems gained prominence (e.g. Bhalla and Iyengar 1999).
Mathematical models are much older than systems biology but they started to be
used more extensively and visibly with the rise of systems biology. Formalising
all of the past knowledge of a system was shown to be a useful way to test if
what is known was sufficient to explain the behaviour of the system. Models were
also perturbed in silico to find the most relevant parameters and generate
novel hypothesis to be tested experimentally. This model refinement cycle has
been used with success for example in the modelling of cell cycle (Novak and Tyson 1993, Tyson Noval 2001; among many others) or circadian
clock (Locke et al. 2005; Locke et al. 2010; Pokhilko et al. 2012). However, this iteration between formal modelling and experiments has not
really taken off across many other systems. The reason for the lack of excitement is not clear to me
although I have the impression that often the models are not used extensively beyond asking if what we know about a system sufficiently
explains all of observed outcomes and perturbations.
Top-down systems biology and everything in
between
From the
start there has been a division between the researchers that identified
themselves as part of the systems biology community. Bottom-up researchers have
been focused on the formal modelling of systems, the discovery of design
principles and emerging behaviours. Top-down researchers would argue that a
truly comprehensive view of a system is needed. These scientists have been more
focused on further developing and applying methods to systematically measure
biological systems. The emphasis in this camp has been on developing generalizable
strategies that can take large-scale observations and identify rules,
regardless of the system of interest. I would say that these works, my own included, have been less powerful in
identifying elegant universal rules. By this I mean, for example, those initial
attempts to find common principles across biological and man-made networks.
Instead of principles, what have been readily transposed across systems have
been approaches such as machine learning methods. Drug screens with behavioural phenotypes, genetic interaction networks or developmental defect screens with
gene knock-downs can all be analysed in the same ways. Such systematic studies have driven costs down
(per observation) and contrary to “representative” experiments in small scale
studies, the large-scale measurements tend to be properly benchmarked for
accuracy and coverage.
What is
still missing are ways to bridge the divide between these two camps. Ways to
start from large-scale measurements that result in models that can be studied
for design features. Studies that include perturbation experiments come closer
to achieve this. Examples for network reconstruction methods have shown that it
should be possible to achieve this but we are not quite there yet (Hill et al. 2016).
From systems biology to systems everything
As
scientific movement systems biology started in cell biology, as far as I can
tell, but has since then permeated many other areas of research. As examples, I have
heard of systems genetics, systems neuroscience, systems medicine, evolutionary systems biology
and systems structural biology. In 2017 we still face a flood of data and
highly complex nonlinear systems. However, the reductionist approaches now typically go
hand-in-hand with attempts to formalise knowledge in quantitative ways to
identify the key relationships that explain the function of interest. In a
sense, the movement of systems biology has succeeded to such an extent that it seems less exciting to me as field in itself. It is a fantastic approach that
is currently being used across most of biology but there is less developments that alter how we do science. I am curious as to what other researchers
that identify themselves with doing systems biology think - What have been
great achievements of systems biology? What are the great challenges that are
not simply applications of systems biology? Questions to think about
for the (equally arbitrary) celebration of the 20 years of the field in 2020.