Biology subjects

Komorowski, M.

Publications and source records attributed to Komorowski, M..

3 recordsLinked to original sources

bioRxiv2026

Single-cell profiling quantifies the dose gap between population-average and complete pY-STAT1/3 suppression by JAK inhibitors

Inhibitor potency is commonly assessed from population-average responses, but how much single-cell signaling remains under strong average suppression is less clear. We measured nuclear STAT1 and STAT3 tyrosine phosphorylation in two human monocytic cell lines stimulated with IFN-{gamma}; and IL-10 and treated with six JAK-pathway inhibitors. Across 24 profiles, we compared arithmetic-mean inhibition and inhibition of the mean of log10-transformed single-cell responses (log-mean) with the fractions of cells leaving the stimulated range (partial inhibition) or entering the non-stimulated range (complete inhibition). At the lowest tested doses where arithmetic-mean or log-mean inhibition reached at least 80%, median fractions remaining above the non-stimulated range were 76% and 37%, respectively. Thus, strong average suppression frequently left substantial fractions of cells with tangibly elevated pY-STAT responses. Log-mean inhibition generally approximated partial-inhibition occupancy, whereas the apparent IC50 for complete-inhibition occupancy was a median 2.4-fold higher than the log-mean IC50 and 7.4-fold higher than the arithmetic-mean IC50. These relationships provide an empirical guide for estimating cell fractions from conventional inhibition curves.

systems biology↗

bioRxiv2020

Fractional response analysis reveals logarithmic cytokine responses in cellular populations

Although we can now measure single-cell signaling responses with multivariate, high-throughput techniques our ability to interpret such measurements is still limited. Even interpretation of dose-response based on single-cell data is not straightforward: signaling responses can differ significantly between cells, encompass multiple signaling effectors, and have dynamic character. Here, we use probabilistic modeling and information-theory to introduce fractional response analysis (FRA), which quantifies changes in fractions of cells with given response levels. FRA can be universally performed for heterogeneous, multivariate, and dynamic measurements and, as we demonstrate, uncovers otherwise hidden patterns in single-cell data. In particular, we show that fractional responses to type I interferon in human peripheral blood mononuclear cells are very similar across different cell types, despite significant differences in mean or median responses and degrees of cell-to-cell heterogeneity. Further, we demonstrate that fractional responses to cytokines scale linearly with the log of the cytokine dose, which uncovers that cellular populations are sensitive to fold-changes in the dose, as opposed to additive changes.

systems biology↗

bioRxiv2019

Channel Capacity Computations for Unregulated and Autoregulated Gene Expression

How living cells can reliably process biochemical cues in the presence of molecular noise is not fully understood. Here we investigate the fidelity of information transfer in the expression of a single gene. We use the established model of gene expression to examine how precisely the protein levels can be controlled by two distinct mechanisms: (i) the transcription rate of the gene, or (ii) the translation rate for the corresponding mRNA. The fidelity of gene expression is quantified with the information-theoretic notion of information capacity. Derived information capacity formulae reveal that transcriptional control generally provides a tangibly higher capacity as compared to the translational control. We next introduce negative feedback regulation in gene expression, where the protein directly inhibits its own transcription. While negative feedback reduces noise in the level of the protein for a given input signal, it also decreases the input-to-output sensitivity. Our results show that the combined effect of these two opposing forces is a reduced capacity in the presence of feedback. In summary, our analysis presents the first analytical quantification of information transfer in simple gene expression models, which provides insight into the fidelity of basic gene expression control mechanisms.

systems biology↗