Johannes Berg

About Johannes Berg:

With a background in the statistical mechanics of disordered systems, my research focusses on the interface between statistical physics and molecular 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 population dynamics of regulatory DNA.

SEEK ID: https://fairdomhub.org/people/343

Location:  Germany

Expertise: Mathematical and statistical modeling, Statistical Physics

Tools: Dynamic modelling, stochastic methods, Statistical tools

ORCID: Not specified

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SysMO

SysMO is a European transnational funding and research initiative on "Systems Biology of Microorganisms".

The goal pursued by SysMO was to record and describe the dynamic molecular processes going on in unicellular microorganisms in a comprehensive way and to present these processes in the form of computerized mathematical models.

Systems biology will raise biomedical and biotechnological research to a new quality level and contribute markedly to progress in understanding. Pooling European research ...

Projects: BaCell-SysMO, COSMIC, SUMO, KOSMOBAC, SysMO-LAB, PSYSMO, SCaRAB, MOSES, TRANSLUCENT, STREAM, SulfoSys, SysMO DB, SysMO Funders, SilicoTryp, Noisy-Strep

Web page: http://sysmo.net/

Noisy-Strep

Bistable switches are the key elements of the regulatory networks governing cell development, differentiation and life-strategy decisions. Transcriptional noise is a main determinant that causes switching between different states in bistable systems. By using the human pathogen Streptococcus pneumoniae as a model bacterium, we will investigate how transcriptional fidelity and processivity influence (noisy) gene expression and participate in bistability. To study this question, we will use both ...

Programme: SysMO

Public web page: http://www.sysmo.net/index.php?index=163

What makes the lac-pathway switch: identifying the fluctuations that trigger phenotype switching in gene regulatory systems
Noisy-Strep

Abstract (Expand)

Multistable gene regulatory systems sustain different levels of gene expression under identical external conditions. Such multistability is used to encode phenotypic states in processes including … nutrient uptake and persistence in bacteria, fate selection in viral infection, cell-cycle control and development. Stochastic switching between different phenotypes can occur as the result of random fluctuations in molecular copy numbers of mRNA and proteins arising in transcription, translation, transport and binding. However, which component of a pathway triggers such a transition is generally not known. By linking single-cell experiments on the lactose-uptake pathway in E. coli to molecular simulations, we devise a general method to pinpoint the particular fluctuation driving phenotype switching and apply this method to the transition between the uninduced and induced states of the lac-genes. We find that the transition to the induced state is not caused only by the single event of lac-repressor unbinding, but depends crucially on the time period over which the repressor remains unbound from the lac-operon. We confirm this notion in strains with a high expression level of the lac-repressor (leading to shorter periods over which the lac-operon remains unbound), which show a reduced switching rate. Our techniques apply to multistable gene regulatory systems in general and allow to identify the molecular mechanisms behind stochastic transitions in gene regulatory circuits.

Editor:

Date Published: 24th Sep 2014

Publication Type: Not specified

GraphAlignment: Bayesian pairwise alignment of biological networks
Noisy-Strep

Abstract (Expand)

ABSTRACT: BACKGROUND: With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies … is the alignment of networks. RESULTS: We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Graemlin 2.0.On simulated data, GraphAlignment outperforms Graemlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Graemlin 2.0. It is faster than Graemlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N^2.6). On empirical bacterial protein-protein interaction networks (PIN) and gene co-expression networks, GraphAlignment outperforms Graemlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Graemlin 2.0 outperforms GraphAlignment. CONCLUSIONS: The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity.

Authors: Michal Kolar, Jörn Meier, Ville Mustonen, Michael Lässig,

Date Published: 21st Nov 2012

Publication Type: Not specified

Nonlinear fitness landscape of a molecular pathway
Noisy-Strep

Abstract (Expand)

Genes are regulated because their expression involves a fitness cost to the organism. The production of proteins by transcription and translation is a well-known cost factor, but the enzymatic activity … of the proteins produced can also reduce fitness, depending on the internal state and the environment of the cell. Here, we map the fitness costs of a key metabolic network, the lactose utilization pathway in Escherichia coli. We measure the growth of several regulatory lac operon mutants in different environments inducing expression of the lac genes. We find a strikingly nonlinear fitness landscape, which depends on the production rate and on the activity rate of the lac proteins. A simple fitness model of the lac pathway, based on elementary biophysical processes, predicts the growth rate of all observed strains. The nonlinearity of fitness is explained by a feedback loop: production and activity of the lac proteins reduce growth, but growth also affects the density of these molecules. This nonlinearity has important consequences for molecular function and evolution. It generates a cliff in the fitness landscape, beyond which populations cannot maintain growth. In viable populations, there is an expression barrier of the lac genes, which cannot be exceeded in any stationary growth process. Furthermore, the nonlinearity determines how the fitness of operon mutants depends on the inducer environment. We argue that fitness nonlinearities, expression barriers, and gene-environment interactions are generic features of fitness landscapes for metabolic pathways, and we discuss their implications for the evolution of regulation.

Authors: Lilia Perfeito, Stéphane Ghozzi, , Karin Schnetz, Michael Lässig

Date Published: 21st Jul 2011

Publication Type: Not specified

Significance analysis and statistical mechanics: an application to clustering
Noisy-Strep

Abstract (Expand)

This Letter addresses the statistical significance of structures in random data: given a set of vectors and a measure of mutual similarity, how likely is it that a subset of these vectors forms a cluster … with enhanced similarity among its elements? The computation of this cluster p value for randomly distributed vectors is mapped onto a well-defined problem of statistical mechanics. We solve this problem analytically, establishing a connection between the physics of quenched disorder and multiple-testing statistics in clustering and related problems. In an application to gene expression data, we find a remarkable link between the statistical significance of a cluster and the functional relationships between its genes.

Authors: Marta Łuksza, Michael Lässig,

Date Published: 27th Nov 2009

Publication Type: Not specified

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