Michał Komorowski, Ph.D.

Department of Biosystems and Soft Matter (ZBiMM)
Division of Modelling in Biology and Medicine (PMBM)
position: assistant professor
telephone: (+48) 22 826 12 81 ext.: 161
room: 311
e-mail: mkomor
personal site: http://bluebox.ippt.pan.pl/~mkomor/

Doctoral thesis
2009-11-30Statistical methods for estimation of biochemical kinetic parameters  (DS,UW)
supervisor -- Bärbel Finkenstädt, DS,UW
supervisor -- David A. Rand, DS,UW
1242
 
Recent publications
1.Martincuks A., Andryka K., Küster A., Schmitz-Van de Leur H., Komorowski M., Müller-Newen G., Nuclear translocation of STAT3 and NF-κB are independent of each other but NF-κB supports expression and activation of STAT3, Cellular Signalling, ISSN: 0898-6568, DOI: 10.1016/j.cellsig.2017.01.006, Vol.32, pp.36-47, 2017
Abstract:

NF-κB and STAT3 are essential transcription factors in immunity and act at the interface of the transition from chronic inflammation to cancer. Different functional crosstalks between NF-κB and STAT3 have been recently described arguing for a direct interaction of both proteins. During a systematic analysis of NF-κB/STAT3 crosstalk we observed that appearance of the subcellular distribution of NF-κB and STAT3 in immunofluorescence heavily depends on the fixation procedure. Therefore, we established an optimized fixation protocol for the reliable simultaneous analysis of the subcellular distributions of both transcription factors. Using this protocol we found that cytokine-induced nuclear accumulation of NF-κB or STAT3 did not alter the subcellular distribution of the other transcription factor. Both knockout and overexpression of STAT3 does not have any major effect on canonical TNFα-NF-κB signalling in MEF or HeLa cells. Similarly, knockout of p65 did not alter nuclear accumulation of STAT3 in response to IL-6. However, p65 expression correlates with elevated total cellular levels of STAT3 and STAT1 and supports activation of these transcription factors. Our findings in MEF cells argue against a direct physical interaction of free cellular NF-κB and STAT3 but point to more intricate functional interactions.

Keywords:

STAT3, NF-κB, Signal transduction, Nuclear translocation, Crosstalk

Affiliations:
Martincuks A.-RWTH Aachen University (DE)
Andryka K.-IPPT PAN
Küster A.-RWTH Aachen University (DE)
Schmitz-Van de Leur H.-RWTH Aachen University (DE)
Komorowski M.-IPPT PAN
Müller-Newen G.-RWTH Aachen University (DE)
2.Nienałtowski K., Włodarczyk M., Lipniacki T., Komorowski M., Clustering reveals limits of parameter identifiability in multi-parameter models of biochemical dynamics, BMC SYSTEMS BIOLOGY, ISSN: 1752-0509, DOI: 10.1186/s12918-015-0205-8, Vol.9, pp.65-1-9, 2015
Abstract:

Background
Compared to engineering or physics problems, dynamical models in quantitative biology typically depend on a relatively large number of parameters. Progress in developing mathematics to manipulate such multi-parameter models and so enable their efficient interplay with experiments has been slow. Existing solutions are significantly limited by model size.

Results
In order to simplify analysis of multi-parameter models a method for clustering of model parameters is proposed. It is based on a derived statistically meaningful measure of similarity between groups of parameters. The measure quantifies to what extend changes in values of some parameters can be compensated by changes in values of other parameters. The proposed methodology provides a natural mathematical language to precisely communicate and visualise effects resulting from compensatory changes in values of parameters. As a results, a relevant insight into identifiability analysis and experimental planning can be obtained. Analysis of NF- κB and MAPK pathway models shows that highly compensative parameters constitute clusters consistent with the network topology. The method applied to examine an exceptionally rich set of published experiments on the NF- κB dynamics reveals that the experiments jointly ensure identifiability of only 60 % of model parameters. The method indicates which further experiments should be performed in order to increase the number of identifiable parameters.

Conclusions
We currently lack methods that simplify broadly understood analysis of multi-parameter models. The introduced tools depict mutually compensative effects between parameters to provide insight regarding role of individual parameters, identifiability and experimental design. The method can also find applications in related methodological areas of model simplification and parameters estimation.

Affiliations:
Nienałtowski K.-IPPT PAN
Włodarczyk M.-other affiliation
Lipniacki T.-IPPT PAN
Komorowski M.-IPPT PAN
3.Jetka T., Charzyńska A., Gambin A., Stumpf M.P.H., Komorowski M., StochDecomp—Matlab package for noise decomposition in stochastic biochemical systems, BIOINFORMATICS, ISSN: 1367-4803, DOI: 10.1093/bioinformatics/btt631, Vol.30, No.1, pp.137-138, 2014
Abstract:

Motivation: Stochasticity is an indispensable aspect of biochemical processes at the cellular level. Studies on how the noise enters and propagates in biochemical systems provided us with non-trivial insights into the origins of stochasticity, in total, however, they constitute a patchwork of different theoretical analyses.

Results: Here we present a flexible and widely applicable noise decomposition tool that allows us to calculate contributions of individual reactions to the total variability of a system’s output. With the package it is, therefore, possible to quantify how the noise enters and propagates in biochemical systems. We also demonstrate and exemplify using the JAK-STAT signalling pathway that the noise contributions resulting from individual reactions can be inferred from data experimental data along with Bayesian parameter inference. The method is based on the linear noise approximation, which is assumed to provide a reasonable representation of analyzed systems.

Affiliations:
Jetka T.-IPPT PAN
Charzyńska A.-University of Warsaw (PL)
Gambin A.-other affiliation
Stumpf M.P.H.-Imperial College London (GB)
Komorowski M.-IPPT PAN
4.Woodcock D.J., Vance K.W., Komorowski M., Koentges G., Finkenstädt B., Rand D.A., A hierarchical model of transcriptional dynamics allows robust estimation of transcription rates in populations of single cells with variable gene copy number, BIOINFORMATICS, ISSN: 1367-4803, DOI: 10.1093/bioinformatics/btt201, Vol.29, pp.1519-1525, 2013
Abstract:

Motivation: cis-regulatory DNA sequence elements, such as enhancers and silencers, function to control the spatial and temporal expression of their target genes. Although the overall levels of gene expression in large cell populations seem to be precisely controlled, transcription of individual genes in single cells is extremely variable in real time. It is, therefore, important to understand how these cis-regulatory elements function to dynamically control transcription at single-cell resolution. Recently, statistical methods have been proposed to back calculate the rates involved in mRNA transcription using parameter estimation of a mathematical model of transcription and translation. However, a major complication in these approaches is that some of the parameters, particularly those corresponding to the gene copy number and transcription rate, cannot be distinguished; therefore, these methods cannot be used when the copy number is unknown.

Results: Here, we develop a hierarchical Bayesian model to estimate biokinetic parameters from live cell enhancer–promoter reporter measurements performed on a population of single cells. This allows us to investigate transcriptional dynamics when the copy number is variable across the population. We validate our method using synthetic data and then apply it to quantify the function of two known developmental enhancers in real time and in single cells.

Affiliations:
Woodcock D.J.-University of Warwick (GB)
Vance K.W.-University of Warwick (GB)
Komorowski M.-IPPT PAN
Koentges G.-University of Warwick (GB)
Finkenstädt B.-University of Warwick (GB)
Rand D.A.-University of Warwick (GB)
5.Liepe J., Filippi S., Komorowski M., Stumpf M.P.H., Maximising the information content of experiments in systems biology, PLOS COMPUTATIONAL BIOLOGY, ISSN: 1553-734X, DOI: 10.1371/journal.pcbi.1002888, Vol.9, No.1, pp.e1002888-1-13, 2013
Abstract:

Our understanding of most biological systems is in its infancy. Learning their structure and intricacies is fraught with challenges, and often side-stepped in favour of studying the function of different gene products in isolation from their physiological context. Constructing and inferring global mathematical models from experimental data is, however, central to systems biology. Different experimental setups provide different insights into such systems. Here we show how we can combine concepts from Bayesian inference and information theory in order to identify experiments that maximize the information content of the resulting data. This approach allows us to incorporate preliminary information; it is global and not constrained to some local neighbourhood in parameter space and it readily yields information on parameter robustness and confidence. Here we develop the theoretical framework and apply it to a range of exemplary problems that highlight how we can improve experimental investigations into the structure and dynamics of biological systems and their behavior.

Affiliations:
Liepe J.-Imperial College London (GB)
Filippi S.-Imperial College London (GB)
Komorowski M.-IPPT PAN
Stumpf M.P.H.-Imperial College London (GB)
6.Finkenstädt B., Woodcock D.J., Komorowski M., Harper C.V., Davis J.R.E., White M.R.H., Rand D.A., Quantifying intrinsic and extrinsic noise in gene transcription using the linear noise approximation: An application to single cell data, Annals of Applied Statistics, ISSN: 1932-6157, DOI: 10.1214/13-AOAS669, Vol.7, No.4, pp.1960-1982, 2013
Abstract:

A central challenge in computational modeling of dynamic biological systems is parameter inference from experimental time course measurements. However, one would not only like to infer kinetic parameters but also study their variability from cell to cell. Here we focus on the case where single-cell fluorescent protein imaging time series data are available for a population of cells. Based on van Kampen’s linear noise approximation, we derive a dynamic state space model for molecular populations which is then extended to a hierarchical model. This model has potential to address the sources of variability relevant to single-cell data, namely, intrinsic noise due to the stochastic nature of the birth and death processes involved in reactions and extrinsic noise arising from the cell-to-cell variation of kinetic parameters. In order to infer such a model from experimental data, one must also quantify the measurement process where one has to allow for nonmeasurable molecular species as well as measurement noise of unknown level and variance. The availability of multiple single-cell time series data here provides a unique testbed to fit such a model and quantify these different sources of variation from experimental data.

Keywords:

Linear noise approximation, kinetic parameter estimation, intrinsic and extrinsic noise, state space model and Kalman filter, Bayesian hierarchical modeling

Affiliations:
Finkenstädt B.-University of Warwick (GB)
Woodcock D.J.-University of Warwick (GB)
Komorowski M.-IPPT PAN
Harper C.V.-University of Manchester (GB)
Davis J.R.E.-University of Manchester (GB)
White M.R.H.-University of Manchester (GB)
Rand D.A.-University of Warwick (GB)
7.Schumacher J., Behrends V., Pan Z., Brown D.R., Heydenreich F., Lewis M.R., Bennett M.H., Razzaghi B., Komorowski M., Barahona M., Stumpf M.P.H., Wigneshweraraj S., Bundy J.G., Buck M., Nitrogen and carbon status are integrated at the transcriptional level by the nitrogen regulator NtrC in vivo, mBio, ISSN: 2150-7511, DOI: 10.1128/mBio.00881-13, Vol.4, pp.00881-1-13, 2013
Abstract:

Nitrogen regulation in Escherichia coli is a model system for gene regulation in bacteria. Growth on glutamine as a sole nitrogen source is assumed to be nitrogen limiting, inferred from slow growth and strong NtrB/NtrC-dependent gene activation. However, we show that under these conditions, the intracellular glutamine concentration is not limiting but 5.6-fold higher than in ammonium-replete conditions; in addition, α-ketoglutarate concentrations are elevated. We address this glutamine paradox from a systems perspective. We show that the dominant role of NtrC is to regulate glnA transcription and its own expression, indicating that the glutamine paradox is not due to NtrC-independent gene regulation. The absolute intracellular NtrC and GS concentrations reveal molecular control parameters, where NtrC-specific activities were highest in nitrogen-starved cells, while under glutamine growth, NtrC showed intermediate specific activity. We propose an in vivo model in which α-ketoglutarate can derepress nitrogen regulation despite nitrogen sufficiency.

Affiliations:
Schumacher J.-Imperial College London (GB)
Behrends V.-Imperial College London (GB)
Stumpf M.P.H.-Imperial College London (GB)
Wigneshweraraj S.-Imperial College London (GB)
Bundy J.G.-Imperial College London (GB)
Buck M.-Imperial College London (GB)
Pan Z.-Imperial College London (GB)
Brown D.R.-Imperial College London (GB)
Heydenreich F.-Imperial College London (GB)
Lewis M.R.-Imperial College London (GB)
Bennett M.H.-Imperial College London (GB)
Razzaghi B.-Imperial College London (GB)
Komorowski M.-IPPT PAN
Barahona M.-Imperial College London (GB)
8.Komorowski M., Miękisz J., Stumpf M.P.H., Decomposing Noise in Biochemical Signalling Systems Highlights the Role of Protein Degradation, BIOPHYSICAL JOURNAL, ISSN: 0006-3495, DOI: 10.1016/j.bpj.2013.02.027, Vol.104, pp.1783-1793, 2013
Abstract:

Stochasticity is an essential aspect of biochemical processes at the cellular level. We now know that living cells take advantage of stochasticity in some cases and counteract stochastic effects in others. Here we propose a method that allows us to calculate contributions of individual reactions to the total variability of a system’s output. We demonstrate that reactions differ significantly in their relative impact on the total noise and we illustrate the importance of protein degradation on the overall variability for a range of molecular processes and signaling systems. With our flexible and generally applicable noise decomposition method, we are able to shed new, to our knowledge, light on the sources and propagation of noise in biochemical reaction networks; in particular, we are able to show how regulated protein degradation can be employed to reduce the noise in biochemical systems.

Affiliations:
Komorowski M.-IPPT PAN
Miękisz J.-University of Warsaw (PL)
Stumpf M.P.H.-Imperial College London (GB)
9.Komorowski M., Zurauskiene J., Stumpf M.P.H., StochSens - matlab package for sensitivity analysis of stochastic chemical systems, BIOINFORMATICS, ISSN: 1367-4803, DOI: 10.1093/bioinformatics/btr714, Vol.28, No.5, pp.731-733, 2012
Abstract:

Motivation: The growing interest in the role of stochasticity in biochemical systems drives the demand for tools to analyse stochastic dynamical models of chemical reactions. One powerful tool to elucidate performance of dynamical systems is sensitivity analysis. Traditionally, however, the concept of sensitivity has mainly been applied to deterministic systems, and the difficulty to generalize these concepts for stochastic systems results from necessity of extensive Monte Carlo simulations.

Results: Here we present a Matlab package, StochSens, that implements sensitivity analysis for stochastic chemical systems using the concept of the Fisher Information Matrix (FIM). It uses the linear noise approximation to represent the FIM in terms of solutions of ordinary differential equations. This is the first computational tool that allows for quick computation of the Information Matrix for stochastic systems without the need for Monte Carlo simulations.

Affiliations:
Komorowski M.-other affiliation
Zurauskiene J.-other affiliation
Stumpf M.P.H.-Imperial College London (GB)
10.Harrington H.A., Komorowski M., Beguerisse Díaz M., Ratto G.M., Stumpf M.P.H., Mathematical modeling reveals the functional implications of the different nuclear shuttling rates of Erk1 and Erk2, PHYSICAL BIOLOGY, ISSN: 1478-3967, DOI: 10.1088/1478-3975/9/3/036001, Vol.9, pp.036001-1-12, 2012
Abstract:

The mitogen-activated protein kinase (MAPK) family of proteins is involved in regulating cellular fates such as proliferation, differentiation and apoptosis. In particular, the dynamics of the Erk/Mek system, which has become the canonical example for MAPK signaling systems, have attracted considerable attention. Erk is encoded by two genes, Erk1 and Erk2, that until recently had been considered equivalent as they differ only subtly at the sequence level. However, these proteins exhibit radically different trafficking between cytoplasm and nucleus and this fact may have functional implications. Here we use spatially resolved data on Erk1/2 to develop and analyze spatio-temporal models of these cascades, and we discuss how sensitivity analysis can be used to discriminate between mechanisms. Our models elucidate some of the factors governing the interplay between signaling processes and the Erk1/2 localization in different cellular compartments, including competition between Erk1 and Erk2. Our approach is applicable to a wide range of signaling systems, such as activation cascades, where translocation of molecules occurs. Our study provides a first model of Erk1 and Erk2 activation and their nuclear shuttling dynamics, revealing a role in the regulation of the efficiency of nuclear signaling.

Affiliations:
Harrington H.A.-other affiliation
Komorowski M.-other affiliation
Beguerisse Díaz M.-other affiliation
Ratto G.M.-other affiliation
Stumpf M.P.H.-Imperial College London (GB)
11.Komorowski M., Costa M.J., Rand D.A., Stumpf M.P.H., Sensitivity, robustness, and identifiability in stochastic chemical kinetics models, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, ISSN: 0027-8424, DOI: 10.1073/pnas.1015814108, Vol.108, No.21, pp.8645-8650, 2011
Abstract:

We present a novel and simple method to numerically calculate Fisher information matrices for stochastic chemical kinetics models. The linear noise approximation is used to derive model equations and a likelihood function that leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher information matrix to solving a set of ordinary differential equations. This is the first method to compute Fisher information for stochastic chemical kinetics models without the need for Monte Carlo simulations. This methodology is then used to study sensitivity, robustness, and parameter identifiability in stochastic chemical kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.

Affiliations:
Komorowski M.-other affiliation
Costa M.J.-University of Warwick (GB)
Rand D.A.-University of Warwick (GB)
Stumpf M.P.H.-Imperial College London (GB)
12.Komorowski M., Finkenstädt B., Rand D., Using a single fluorescent reporter gene to infer half-life of extrinsic noise and other parameters of gene expression, BIOPHYSICAL JOURNAL, ISSN: 0006-3495, DOI: 10.1016/j.bpj.2010.03.032, Vol.98, pp.2759-2769, 2010
Abstract:

Fluorescent and luminescent proteins are often used as reporters of transcriptional activity. Given the prevalence of noise in biochemical systems, the time-series data arising from these is of significant interest in efforts to calibrate stochastic models of gene expression and obtain information about sources of nongenetic variability. We present a statistical inference framework that can be used to estimate kinetic parameters of gene expression, as well as the strength and half-life of extrinsic noise from single fluorescent-reporter-gene time-series data. The method takes into account stochastic variability in a fluorescent signal resulting from intrinsic noise of gene expression, kinetics of fluorescent protein maturation, and extrinsic noise, which is assumed to arise at transcriptional level. We use the linear noise approximation and derive an explicit formula for the likelihood of observed fluorescent data. The method is embedded in a Bayesian paradigm, so that certain parameters can be informed from other experiments allowing portability of results across different studies. Inference is performed using Markov chain Monte Carlo. Fluorescent reporters are primary tools to observe dynamics of gene expression and the correct interpretation of fluorescent data is crucial to investigating these fundamental processes of cellular life. As both magnitude and frequency of the noise may have a dramatic effect on the cell fitness, the quantification of stochastic fluctuation is essential to the understanding of how genes are regulated. Our method provides a framework that addresses this important question.

Affiliations:
Komorowski M.-other affiliation
Finkenstädt B.-University of Warwick (GB)
Rand D.-University of Warwick (GB)

Conference abstracts
1.Andryka K., Głów E., Nienałtowski K., Jetka T., Komorowski M., Sensing accuracy of interferons' concentrations, Cytokine, ISSN: 1043-4666, DOI: 10.1016/j.cyto.2015.08.238, Vol.76, pp.108, 2015
Abstract:

Interferons exhibit their key role of immune modulators through activation of the Jak-Stat signalling pathway. We know substantial amount of molecular details regarding functioning of the pathway. However, to what extend the action of the pathway is dose dependent at the single cell level remains largely unclear. Specifically it is not know if single cells respond in a digital fashion or their output is continuously dependent on the stimulant’s concentration. We have combined an information-theoretic framework with high-throughput confocal imaging of mouse embryonic fibroblasts to provide a thorough, single-cell analysis of the Jak-Stat signalling in response to interferon beta and interferon gamma. We showed that in a baseline state single cells have information hardly sufficient to distinguish between presence or absence of interferons. However they can be put in an alert state by an action of interferons, which allows them to respond more in an analogous fashion. Our results show that the accuracy with which signalling pathways transmit information is not fixed but can be modulated on the contextual basis.

Affiliations:
Andryka K.-IPPT PAN
Głów E.-IPPT PAN
Nienałtowski K.-IPPT PAN
Jetka T.-IPPT PAN
Komorowski M.-IPPT PAN
2.Jetka T., Komorowski M., How can we quantify ligand sensitivity for single-cell heterogenous dynamical responses?, FEBS Journal, ISSN: 1742-464X, Vol.281, No.Supplement: 1, Special Issue: SI, pp.625, 2014
Keywords:

Information Processing, Sensitivity Analysis, Signal Transduction

Affiliations:
Jetka T.-IPPT PAN
Komorowski M.-IPPT PAN
3.Głów E., Jetka T., Komorowski M., Sensitisation in the IFN-alpha/b, IFN-gamma crosstalk reveals mechanisms for enhanced information processing capacity of the STAT1, STAT2 signalling pathway, FEBS Journal, ISSN: 1742-464X, Vol.281, No.Supplement: 1, Special Issue: SI, pp.640, 2014
Keywords:

Interferon, Jak/Stat, Stimulation

Affiliations:
Głów E.-IPPT PAN
Jetka T.-IPPT PAN
Komorowski M.-IPPT PAN