The work of the group lies on the interface between statistical mechanics and quantitative biology. At the centre is the relationship between fluctuations and noise in biological systems, the corresponding statistical ensembles, and biological function. This connection emerges at very different levels and timescales, from stochastic modeling of gene expression to the evolution of regulatory DNA. Our current research focus is on cancer, especially the emergence of therapy resistance.

The emergence of resistance to targeted cancer therapy

Molecularly targeted cancer therapy can lead to rapid regression of a tumour. However, in most cases this regression is followed by the rapid expansion of therapy-resistant mutants. In an collaboration with researchers at the Department of Translational Medicine (Sos and Thomas labs, University Clinic Cologne), we have explored the spectrum of mutations conferring resistance to therapy in a cell-line model of lung cancer (PC-9 cells). This has lead to drug combinations which inhibits all resistant mutants found in a population of tumour cells which is large enough be detected in a clinical setting. Based on these results, we are currently exploring dosing schemes using the predicted evolution of the resistant clones.

[1]   Characterizing Evolutionary Dynamics Reveals Strategies to Exhaust the Spectrum of Subclonal Resistance in EGFR-Mutant Lung Cancer,
  N. Müller, (...), J. Berg, and J. Brägelmann, Cancer Research 83 (15): 2471–2479 (2023)

[2]   A genomics-based classification of human lung tumors,
  D. Seidel et al. (>100 authors), Science Translational Medicine 5:209ra153 (2013)

Resistant clones emerging in a population of PC-9 cells after 3 weeks of treatment with the EGFR-inhibitor erlotinib. In [1] we characterize and target the different resistance mechanisms found in a population corresponding in size to a tumour of 1cm diameter.

Stochastic dynamics of gene expression

Gene transcription takes place out of equilibrium, with the rate of expression of a gene constantly changing due to the presence or absence of specific proteins called transcription factors. Random fluctuations of molecular concentrations can have large effects on cells, such as transitions between metastable states. In a collaboration with experimentalists of the Veening lab (Groningen), we have pinpointed the fluctuation causing the transition to the induced state of the paradigmatic lactose-uptake pathway in E. coli and developed a statistical theory of this transition [1]. Past work was on out-of-equilibrium properties of gene expression [2], the determination of regulatory interactions between genes from gene expression time series [3], and fluctuations in the dynamics of non-coding RNA [4].

[1]   What makes the lac-pathway switch: identifying the fluctuations that trigger phenotype switching in gene regulatory systems
  P. Bhogale, R. Sorg, J.-W. Veening, J. Berg, Nucleic Acids Research; doi: 10.1093/nar/gku839 (2014)

[2]   Out-of-equilibrium dynamics of gene expression and the Jarzynski equality,
  J. Berg, Phys.Rev.Lett 100, 188101 (2008)

[3]   Dynamics of gene expression and the regulatory inference problem,
  J. Berg, Europhys. Lett. 82, 28010 (2008)

[4]   Quantitative analysis of competition in post-transcriptional regulation reveals a novel signature in target expression variation,
  F. Klironomos and J. Berg, Biophysical Journal, 104 (4), 951-958 (2013)

The rate at which the lac-pathway switches to the induced state (log-scale) against the inverse of the inducer concentration. Our model predicts a simple relationship that is borne out very well experimentally (orange triangles), see [1].

Statistical mechanics, inverse problems, and inference

We study pure and applied problems at the interface between statistical mechanics and machine learning. An example is the inverse Ising problem: how to reconstruct couplings and fields of an Ising model given observables such as spin-spin correlations. We contributed a novel method based on the Bethe approximation, which is exact on tree-like lattices [1], and extended mean-field approaches to both ferromagnetic and glassy low-temperature phases of Ising models [2].

[1]   Inverse statistical problems: from the inverse Ising problem to data science,
  H.C. Nguyen, R. Zecchina, and J. Berg, Advances in Physics, 66 (3), 197-261 (2017).

[2]   Mean-field theory for the inverse Ising problem at low temperatures,
  H.C. Nguyen and J. Berg, Phys. Rev. Lett. 109, 050602 (2012).

[3]   Bethe-Peierls approximation and the inverse Ising problem,
  H.C. Nguyen and J. Berg, J. Stat. Mech., P03004 (2012).

[4]   Significance analysis and statistical mechanics: an application to clustering,
  M. Łuksza, M. Lässig and J. Berg, Phys. Rev. Lett. 105, 220601 (2010).

Reconstruction error at different temperatures for model with a glassy low-T phase (SK-model) against inverse temperature and for different numbers of thermodynamic states. See [2] for details.

Statistical mechanics of bio-molecular networks

We have developed an equilibrium statistical mechanics of networks with local connectivity correlations [1], which links a network Hamiltonian with the corresponding statistical observables. This statistical approach forms the theoretical basis for identifying functional units in biological networks. Examples are repeated patterns in networks, called network motifs, which can be identified using Bayesian inference on models of correlations within networks [2]. The evolutionary dynamics of networks of related species can be traced by graph alignment, which is based on a stochastic evolution model for biological interactions and for DNA sequences [3,4]. We have used this approach to analyze coexpression networks of human and mouse, and the protein interaction networks of different herpesviruses. A software package called GraphAlignment is available for download.

[1]   Correlated random networks,
  J. Berg and M. Lässig, Phys. Rev. Lett. 89 (22), 228701 (2002).

[2]   Local graph alignment and motif search in biological networks,
  J. Berg and M. Lässig, PNAS 101(41), 14689-14694 (2004).

[3]   Cross-species analysis of biological networks by Bayesian alignment,
  J. Berg and M. Lässig, PNAS 103 (29), 10967-10972 (2006).

[4]   From protein interactions to functional annotation: Graph alignment in Herpes,
  M. Kolář, M. Lässig, and J. Berg, BMC Systems Biology, 2:90 (2008).

Alignment of the protein interaction networks of two different Herpes viruses [4]. Visualisation by Jörn Meier.