CMOL – Center for Models of Life, Niels Bohr Institute

Publiceret Januar 2014

In the Center for Models of Life, CMOL, we use methods from physics to develop models that deal with computation and communication in biological systems. We model regulation of living systems with the aim to understand the strategies of gene regulation and dynamics of information transfer along signaling pathways, as well as to unravel the interplay between function and evolution.

Who we are

The center is funded by the Danish National Research Foundation from 2005 to 2015, and located at the Niels Bohr Institute, University of Copenhagen. We are combination of scientists with backgrounds in physics of complex systems, statistical physics, biophysics, as well as molecular biology. Our approach is to develop models with predictive power for speci?c biological systems. We use the insight gained from speci?c models to create a broader theoretical understanding of the basic principles underlying biological design and function.

Research covered by center

Structure, function and logic of regulatory networks

The interior of cells is crowded with small molecules (e.g. ions, sugars, etc) and macromolecules (e.g. RNA, DNA, proteins), which interact with each other to form a complex regulatory network. A central approach is simplification of these often very complicated regulatory circuits into their core logical structure. Combinations of positive and negative feedbacks, together with combinations of local activity and longer ranging regulation, open for a way to simplify a number of biological model systems[1]. In Fig. 1 we show an example of how a combination of positive and negative feedbacks generates a threshold response in the galactose system.[1,2] Another combination of similar type of feedback is found to lead to sorting of Fe molecules in essential and less essential protein production.

2014-1 CMOL Figur 1 SmallFig.1. Combination of positive and negative feedbacks generates a threshold response in the galactose system. (Larger version)

Epigenetics in cis: positive feedback involving nucleosomes and associated read/write enzymes

“Trans” acting gene regulation using freely diffusing transcription factors is commonly observed in both prokaryotes and eukaryotes. However, other and more local mechanisms exist, which open for a more fine-grained regulation. Such cis-acting gene regulation involves proteins that influence regulation locally on the genome. Regulatory systems with a local component would for example be expected in the olfactory system, where each of the hundreds of nearly identical genes expresses a particular receptor protein[3], in a way that allows only one gene to be transcribed in each cell. Another more central cis-acting regulatory system is associated to polycomb silencing of Hox genes, which is essential for development of most multicellular eukaryotes.[4]

We mainly focus on nucleosomes as a main substrate for cis-acting regulation, although a similar mathematical formalism may well work in maintaining gene silencing via DNA methylation. Nucleosomes are protein complexes that package eukaryotic DNA and carry various alternative chemical modifications (eg. acetylation and/or methylation). Read-write enzymes carry out specific additions and removals of these nucleosome modifications, thereby mediating positive feedback circuits on localized regions of a chromosome. In Fig. 2, we outline a simple model for such a feedback system, which by itself is able to sustain a genomic region in either a stable active state or a stable inactive state, for many generations[6]. In our work we explore necessary and sufficient conditions for sustaining epigenetics by a number of such regulatory circuits[8], thereby predicting the possible toolbox for cis-regulatory mechanisms in eukaryotic cells.[7,9]

2014-1 CMOL Figur 2
Fig. 2. Simple model for positive feedback circuits on localized regions of a chromosome, which by itself is able to sustain a genomic region in either a stable active state or a stable inactive state, for many generations.

How bacteria don’t rely on gambling

In evolution, the theory of Darwin is undisputed, but this does not mean that other mechanisms may also be at work. Darwinian evolution views selection as a challenge to an organism and only those individuals survive that happen to be best adapted. Lamarck, long before Darwin, proposed a theory of evolution where the challenge induces a change to the genome of individuals that is beneficial for survival and can then be passed on to the offspring. While rarely observed, the suggested mechanism does seem to occur in some cases. One of these is the bacterial CRISPR system – a system where bacteria dynamically adjust their genome by picking up pieces of phage-DNA. Phages are predators that use the bacterium to reproduce. We showed by theoretical models, how Lamarckian evolution and the spatial structure developed by bacterial populations enables efficient defense and increases diversity – similar to what is observed in nature. As shown in the schematic image in Fig. 3, space plays a critical and beneficial role in supporting locality and defense of bacteria.[10]

2014-1 CMOL Figur 3

Fig. 3: Spreading of infected bacteria in space. The landscape shows bacteria of varying immunity, with tall mountains indicating most recent acquisition of defense against a specific phage type. This phage type is indicated by red spheres and all other phages are shown as gray spheres. In this schematic, phages can only infect bacteria that lie in the flat surface, not those along the hills.

How Immune Cells Find their Way to the Infection

Immune response protects us from bacterial infections. As a part of the response, white blood cells have to find their way from the blood stream to the site of infection. If the response is too weak, bacteria can reinforce their presence leading to chronic infection. On the other hand, an overly large response is dangerous for the host and can cause diseases such as asthma, atherosclerosis, multiple sclerosis, inflammatory bowel disorder and arthritis. It remains an open question how white blood cells get directed to the site of infection in right amounts and in a precise manner.

The model proposed by our group members[11] predicts that the signal about an inflammation is propagated by cytokine waves. A “propagating wave” is an effective and reliable way of transmitting signals across large distances and is employed by a number of biological systems, including pulse propagation in neurons. In case of inflammatory signaling the benefit is two-fold. First, the transient nature of a wave minimizes exposure of tissue cells to toxic signaling molecules. Second, the wave creates sharp gradients of signaling molecules, which allows for an efficient translocation of inflammation-resolving white blood cells. Such a translocation would be essentially impossible if the signaling molecules were passively diffusing away from the inflammation site.

The modeling pinpoints a few essential characteristics for robust inflammatory response, including in particular a fast transient response of the so-called NF-κB system, and a positive feedback between the NF-κB system and the signaling molecules that is highlighted by the waves in Fig. 4.[11]

2014-1 CMOL Figur 4
Fig. 4. Left: Subsequent initiation of signaling waves from the inflammation site (black dot) in a system of vertically aligned cells. The white blood cells (white trajectories) guided by the waves towards the center of inflammation. Right: Propagating signaling waves in a 2-dimensional system.

More about us – Visit our website

We have many more activities that are not listed in this article. Please visit our website to find more about our center. Especially we have a large number of Java applets at “Live-models” section ( that visualize our models and allow everyone to play with the model by changing parameters and seeing the effect on the systems’ behaviors. We also host workshops and summer schools on a broad range of research subjects about living systems.


1. K. Sneppen, S. Krishna, and S. Semsey, Simplified models of biological networks. Annu Rev. Biophys. 2010;39:43-59.

2. S. Semsey, S. Krishna, K. Sneppen, and S. Adhya, Signal integration in the galactose network of Escherichia coli. Mol. Microbiol. 2007;65:465-476.

3. S.H. Fuss, M. Omura, and P. Mombaerts, Local and cis Effects of the H Element on Expression of Odorant Receptor Genes in Mouse. Cell 2007;130:373 – 384.

4. L.A. Boyer et al. Polycomb complexes repress developmental regulators in murine embryonic stemm cells. Nature 2006;441:349-353.

5. G. Thon and T. Friis. Epigenetic inheritance of transcriptional silencing and switching competence in fission yeast. Genetics 1997;145:685-96.

6. I. B. Dodd, M. A. Micheelsen, K. Sneppen, and G. Thon. Theoretical analysis of epigenetic cell memory by nucleosome modification. Cell 2007;129: 813-822.

7. K. Sneppen, M. A. Micheelsen, and I. B. Dodd. Ultrasensitive gene regulation by positive feedback loops in nucleosome modification. Molec. Systems Biol. 2008;4:182.

8. K. Sneppen and I. B. Dodd. A Simple Histone Code Opens Many Paths to Epigenetics. PLoS Comput. Biol. 2012;8:e1002643.

9. I. B. Dodd and K. Sneppen. Barriers and Silencers: A Theoretical Toolkit for Control and Containment of Nucleosome-Based Epigenetic States, J. Mol. Biol. 2011;391:671-678.

10. J. O. Haerter and K. Sneppen. Spatial Structure and Lamarckian Adaptation Explain Extreme Genetic Diversity at CRISPR Locus. mBio 2012;3:e00126-12.

11. P. Yde, B. Mengel, M. H. Jensen, S. Krishna and A. Trusina. Modeling the NF-κB mediated inflammatory response predicts cytokine waves in tissue. BMC Systems Biology 2011;5:115.