@article{PulkkinenMetzler2015,
  author    = {Pulkkinen, Otto and Metzler, Ralf},
  title     = {Variance-corrected Michaelis-Menten equation predicts transient rates of single-enzyme reactions and response times in bacterial gene-regulation},
  series = {Scientific reports},
  volume    = {5},
  journal   = {Scientific reports},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/srep17820},
  pages     = {11},
  year      = {2015},
  abstract  = {Many chemical reactions in biological cells occur at very low concentrations of constituent molecules. Thus, transcriptional gene-regulation is often controlled by poorly expressed transcription-factors, such as E. coli lac repressor with few tens of copies. Here we study the effects of inherent concentration fluctuations of substrate-molecules on the seminal Michaelis-Menten scheme of biochemical reactions. We present a universal correction to the Michaelis-Menten equation for the reaction-rates. The relevance and validity of this correction for enzymatic reactions and intracellular gene-regulation is demonstrated. Our analytical theory and simulation results confirm that the proposed variance-corrected Michaelis-Menten equation predicts the rate of reactions with remarkable accuracy even in the presence of large non-equilibrium concentration fluctuations. The major advantage of our approach is that it involves only the mean and variance of the substrate-molecule concentration. Our theory is therefore accessible to experiments and not specific to the exact source of the concentration fluctuations.},
  language  = {en}
}
@article{PulkkinenMetzler2015,
  author    = {Pulkkinen, Otto and Metzler, Ralf},
  title     = {Variance-corrected Michaelis-Menten equation predicts transient rates of single-enzyme reactions and response times in bacterial gene-regulation},
  series = {Scientific reports},
  journal   = {Scientific reports},
  number    = {5},
  publisher = {Nature Publishing Group},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/srep17820},
  year      = {2015},
  abstract  = {Many chemical reactions in biological cells occur at very low concentrations of constituent molecules. Thus, transcriptional gene-regulation is often controlled by poorly expressed transcription-factors, such as E.coli lac repressor with few tens of copies. Here we study the effects of inherent concentration fluctuations of substrate-molecules on the seminal Michaelis-Menten scheme of biochemical reactions. We present a universal correction to the Michaelis-Menten equation for the reaction-rates. The relevance and validity of this correction for enzymatic reactions and intracellular gene-regulation is demonstrated. Our analytical theory and simulation results confirm that the proposed variance-corrected Michaelis-Menten equation predicts the rate of reactions with remarkable accuracy even in the presence of large non-equilibrium concentration fluctuations. The major advantage of our approach is that it involves only the mean and variance of the substrate-molecule concentration. Our theory is therefore accessible to experiments and not specific to the exact source of the concentration fluctuations.},
  language  = {en}
}
@article{PulkkinenMetzler2013,
  author    = {Pulkkinen, Otto and Metzler, Ralf},
  title     = {Distance matters the impact of gene proximity in bacterial gene regulation},
  series = {Physical review letters},
  volume    = {110},
  journal   = {Physical review letters},
  number    = {19},
  publisher = {American Physical Society},
  address   = {College Park},
  issn      = {0031-9007},
  doi       = {10.1103/PhysRevLett.110.198101},
  pages     = {5},
  year      = {2013},
  abstract  = {Following recent discoveries of colocalization of downstream-regulating genes in living cells, the impact of the spatial distance between such genes on the kinetics of gene product formation is increasingly recognized. We here show from analytical and numerical analysis that the distance between a transcription factor (TF) gene and its target gene drastically affects the speed and reliability of transcriptional regulation in bacterial cells. For an explicit model system, we develop a general theory for the interactions between a TF and a transcription unit. The observed variations in regulation efficiency are linked to the magnitude of the variation of the TF concentration peaks as a function of the binding site distance from the signal source. Our results support the role of rapid binding site search for gene colocalization and emphasize the role of local concentration differences.},
  language  = {en}
}
@article{KindlerPulkkinenCherstvyetal.2019,
  author    = {Kindler, Oliver and Pulkkinen, Otto and Cherstvy, Andrey G. and Metzler, Ralf},
  title     = {Burst Statistics in an Early Biofilm Quorum Sensing Mode},
  series = {Scientific Reports},
  volume    = {9},
  journal   = {Scientific Reports},
  publisher = {Macmillan Publishers Limited part of Springer Nature},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-019-48525-2},
  pages     = {19},
  year      = {2019},
  abstract  = {Quorum-sensing bacteria in a growing colony of cells send out signalling molecules (so-called "autoinducers") and themselves sense the autoinducer concentration in their vicinity. Once—due to increased local cell density inside a "cluster" of the growing colony—the concentration of autoinducers exceeds a threshold value, cells in this clusters get "induced" into a communal, multi-cell biofilm-forming mode in a cluster-wide burst event. We analyse quantitatively the influence of spatial disorder, the local heterogeneity of the spatial distribution of cells in the colony, and additional physical parameters such as the autoinducer signal range on the induction dynamics of the cell colony. Spatial inhomogeneity with higher local cell concentrations in clusters leads to earlier but more localised induction events, while homogeneous distributions lead to comparatively delayed but more concerted induction of the cell colony, and, thus, a behaviour close to the mean-field dynamics. We quantify the induction dynamics with quantifiers such as the time series of induction events and burst sizes, the grouping into induction families, and the mean autoinducer concentration levels. Consequences for different scenarios of biofilm growth are discussed, providing possible cues for biofilm control in both health care and biotechnology.},
  language  = {en}
}