This little article attempts to introduce the research that professor Charlotte Hemelrijk and her group “Behavioural Ecology and Self-organization” at the University of Groningen perform on understanding the complexity of bird flocks and fish schools. In the context of the Honours College course “Leadership or Not in Animal Societies” the questions was addressed whether a single leader is necessary to organize the complex behaviour that one can observe in large and moving fish schools or bird flocks.

Is a leader required to guide the instant movement of thousands of birds or fish into one direction in order to find food or escape an enemy?

Or is some kind of intrinsic property that emerges from the school or flock sufficient to explain the observed behaviour?

In the following I will describe how the estimation of movements parameters, computer simulations, and the careful observation of fish schools in nature led to a robust model that can explain why no leader is necessary to coordinate the movement of fish schools.

For fish it is very attractive to organize in schools because spawning an area, finding food, access to mates, protection from predation and hydrodynamic effects all become more optimal for the individual fish. In order to understand how complex school behaviour evolves it first became necessary to be able to describe the formation of a school in general. It has been hypothesized that collective movements, as they occur in a school, are characterized by a directional and temporal coordination. This coordination might only become possible if individuals mutually influence each other by the distance towards other members of the school (Huth and Wissel, 1992). Fig. 1 shows which effects the distance of one to another fish has on its movement according to the formulated hypothesis. These parameters were then used in a computer simulation in order to test whether the resulting model schooling behaviour resembled the natural schooling behaviour. Interestingly, the parameters seemed to be sufficient to describe the natural behaviour (Fig. 1 (C)) that has been noted earlier (Partridge, 1981).

Fig.1Fig. 1: Hypothesized effects of the presence of a second fish on the movement of the first fish depending of the distance to each other. (A) and (B) display how a first fish reacts when a second fish is present in four different proximity zones (based on Huth and Wissel, 1992). (C) shows how the modelled fish schooling behaviour (bottom) clearly resembles the observed behaviour in reality (top) (based on Partridge, 1981).     

Based on the above described parameters attraction, alignment, and avoidance further studies were able to significantly link the behaviour of individual fish to distinct school shapes. However the researchers needed to introduce the factor “speed” into their model in order to being able to reproduce observations in nature (Kunz and Hemelrijk, 2003 and Hemelrijk and Hildenbrandt, 2008). The researchers found that through coordination and collision avoidance a transition to an oblong shaped (with respect to movement direction) school occurred as a function of speed. In other words, a fish school reduced its width and increases its lengths at increasing velocities because this enables the individual fish to avoid collisions. Later, Hemelrijk and colleagues were able to prove that the conclusion drawn from their model also holds true for fish schools in real-life experiments (Hemelrijk et al., 2010).

In order to narrow the gap to the experimental observations, the researchers also introduced a factor to describe the effect that the number of neighbours have on the movement decisions of the fish. They hypothesized the existence of two mechanisms that could govern the movement of individual fish when surrounded by more than one neighbouring fish. Fig. 2 schematically depicts both hypothesis and their respective outcomes in computer model when applied over a number of “decision cycles”. The first model assumes that the movement of an individual fish (3.) who has at least two neighbours (1. and 2.) in his field of view largely is the result of taking the average path between both fish. The second model was assumed to be more realistic because the fish (3.) would have a priority direction that largely depends on factors such as distance to his neighbour. A computer simulation of both models, however, resulted in a surprising outcome. After a number of cycles the priority model had led to a disturbed school pattern that is never observed in nature. On the contrary, the average direction model resulted in an accurate reproduction of field observations. The researchers therefore assumed that the priority direction effect is probably averaged out in a large school because there are many and changing neighbours. The final result is an average directional movement.

 Fig.2Fig. 2: Two models and their simulation results that take neighbouring fish into account during the movement decision process of an individual fish. (A) The average direction model assumes that the movement of fish 3. is the result of averaging between the direction of its neighbours. The priority direction model assumes that fish 3. decides to follow the closest neighbour. (B) Simulations of both models resulted in a dispersed fish distribution in the case of the priority model (right) and a more realistic ordered fish distribution for the average model (left).

The findings presented in Fig. 2 and the fundamental work presented in Fig. 1 therefore prove that individual fish can lead to an emergent property, such as the coordinated behaviour of a large group, without requiring a leader. It is important to note that the individual perceptions of the fish within and on the edges of the school are vital for the coordination. This means that the final direction of the fish school is probably and to a large extend based on the “decisions” that fish on the edges of the school make. These movements “decisions” might be based on knowledge and experience, but also on motivational factors such as hunger. Whether these factors can drive the behaviour of individual fish and therefore the movement of the whole school still remains to be elucidated.

The computational tools of theoretical biology therefore seem to be a good approach to describe complex behavioural patterns in animals groups. Other research projects of the Hemelrijk group used similar parameter-simulation approaches to describe the behaviour of bird flocks and their internal dynamics (Hildenbrandt et al., 2010). Also in bird flocks no real leader is necessary, but the individual movement decisions of birds in their neighbouring context seem to govern the movement of the flock as a whole. These studies can also help to improve understanding on how large groups of humans act in situations of panic and fear when rational decisions might become overruled by movement decisions that are based on the individual context.


Hemelrijk, C.K., and Hildenbrandt, H. (2008). Self-Organized Shape and Frontal Density of Fish Schools. Ethology 114, 245–254.

Hemelrijk, C.K., Hildenbrandt, H., Reinders, J., and Stamhuis, E.J. (2010). Emergence of Oblong School Shape: Models and Empirical Data of Fish. Ethology 116, 1099–1112.

Hildenbrandt, H., Carere, C., and Hemelrijk, C.K. (2010). Self-organized aerial displays of thousands of starlings: a model. Behavioral Ecology 21, 1349–1359.

Huth, A., and Wissel, C. (1992). The simulation of the movement of fish schools. Journal of Theoretical Biology 156, 365–385.

Kunz, H., and Hemelrijk, C.K. (2003). Artificial fish schools: collective effects of school size, body size, and body form. Artif. Life 9, 237–253.

Partridge, B.L. (1981). Internal dynamics and the interrelations of fish in schools. J. Comp. Physiol. 144, 313–325.

Yes, also bacteria seem to have an immune system. Bacteria frequently become attacked by phages and viruses, so they need protection too!

Here I want to briefly introduce the CRISPR/Cas system which is a very interesting type of bacterial defense system using former viral DNA sequences to guide bacterial DNA endonucleases to cellular targets where viral DNA is present. It has always been hypothesized that CRISPR/Cas could be used for biotechnological non-invasive genome editing. However, recently a number of breakthroughs concerning this application have been described. In the following I especially would like to discuss three Science journal; papers that, in my opinion, have been groundbreaking in paving the way for real future applications of CRISPR/Cas and on the other hand helped to understand the molecular basis of this system. Here I will especially concentrate on the type II CRISPR/Cas system (of in total three). In general a bacterial immune response against a viral invader can be split up into three phases: Adaption, expression, and interference. Fig. 1 schematically shows how the type II CRISPR/Cas system is currently assumed to work during the expression and interference phases.


Fig. 1: Schematic depiction of the type II CRISPR/Cas system present in bacteria to destroy invading DNA originating from viruses. The CRISPR/Cas gene cluster contains previously obtained sequence information about foreign DNA (colored triangles) which are separated by repeats (black rectangles). Upon induction this information is transcribed into pre-crRNA together with the expression of the Cas9 protein and so-called tracrRNA which serves as a universal linker to connect crRNA with Cas9. In the following steps tracrRNA and pre-crRNA are cleaved to smaller sizes at least twice. Now the crRNA-Cas9-tracrRNA complex is able to bind foreign DNA at a homologous site termed “protospacer” which is followed by a second, but very short identifier called PAM (protospacer adjacent motif). Once stable binding has been achieved Cas9 seems to cleave the invading DNA and thereby induces double-stranded breaks that inhibit the expression of viral genes. Scheme created by myself, based on (1) Supplementary Fig. 1.

Even though Fig. 1 depicts some of the molecular details that occur during a CRISPR/Cas mediated response there is probably more to the system. Especially during the adaptive phase that governs the incorporation of DNA fragments into the bacterial genome important functional key features are still unidentified. Soon after the first functional properties of the CRISPR/Cas system became evident in the late 1980s researchers began to hypothesize about the biotechnological usability of this defense system. Since then the expression and interference phases have been studied very extensively and especially during the last couple of months some exiting insights have been gained with regard to an actual application by different researchers. Emmanuelle Charpentier and coworkers described in a proof-of-principle study how a fused and custom made crRNA-tracrRNA can be applied to target sequences of interest in a DNA plasmid and in addition identified the two Cas9 protein domains that are responsible for the double-stranded target DNA cleavage (Fig. 2) (1). Partially based on Charpentier’s groundbreaking work, in a second and third paper published last month, researchers from the Massachusetts Institute of Technology and Harvard Medical School describe an exciting approach to silence entire gene loci in mouse and human cellular DNA. The key to successfully being able to target specific sequences in eukaryote cells seems to have been the co-delivery of an expression vector including both pre-crRNA sequences and the Cas9 genes (2). This approach also makes use of the chimeric crRNA-tracrRNA hybrid (Fig. 2) that mimics the naturally occurring crRNA:tracrRNA duplex described by Charpentier and colleagues (1). In a parallel study targeting rates of 4 to 25% under different conditions are described. In addition 40% of all human exons are identified by a bioinformatic approach as being potentially available for CRISPR/Cas silencing. Cloning these target sequences into a 200 base pair format for the first time allowed the creation of a library describing potential target sites in the human genome (3).

Chimeric_crRNA-tracrRNAFig. 2: Schematic depiction of the the naturally occurring crRNA:tracrRNA duplex which in conjunction with Cas9 is able to cleave viral DNA in a target specific manner (top). By creating a linker loop that fuses two functional RNA sequences it became possible to engineer a crRNA-tracrRNA chimera that, based on experimental evidence, has the same functionality as its natural counterpart and has been proven to be very effective for genome targeting purposes in eukaryotic organisms (bottom). Based on (1) Fig. 5.

I consider the CRISP/Cas system extremely interesting because of its seemingly simplistic nature consisting of only a cleavage protein, a target, and a target-identifier. Whether this is the whole story remains to be seen, but some of the most important functional elements now seem to have been identified on a molecular level. The construction and usability of an artificial linker which connects RNAs and endonuclease demonstrates how far knowledge has proceeded. Or in Richard Feynman’s famous words: “What I cannot create, I do not understand“. We might not understand it completely, but at least we can “create” an important part of it. I am confident that CRISP/Cas will play a very important role in bacterial/industrial biotechnology and also genome editing in the future because it dramatically decreases the challenges that accompany the silencing of eukaryotic genes in their native context. By further advancing knowledge and consequently the technology it might even become possible to use the system for genome editing through achieving controlled and sticky-end like double-stranded breaks. This would enable the ligation of desired sequences into eukaryote genes.

It would not be the first time that a giant leap across the phylogenetic divide is made. The bacterial Taq polymerase used during PCR everyday around the globe is just one example how small the world can be at the molecular level. Darwin would have enjoyed it.


(1) Jinek M. et al., A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity, Science 2012 Aug 17;337(6096):816-21.

(2) Cong L., et al., Multiplex Genome Engineering Using CRISPR/Cas Systems, Science. 2013 Jan 3 [electronic publication ahead of print].

(3) Mali P., et al., RNA-Guided Human Genome Engineering via Cas9, Science. 2013 Jan 3 [electronic publication ahead of print].

Random impressions

February 7, 2013

No biology today, just photos from Boston. Click here for a little soundtrack and click the individual photos for higher resolution.

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