Karol Nienałtowski, M.Sc., Eng.

Department of Biosystems and Soft Matter (ZBiMM)
Division of Modelling in Biology and Medicine (PMBM)
position: doctoral student
telephone: (+48) 22 826 12 81 ext.: 468
room: 309
e-mail: knien

Recent publications
1.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
2.Wronowska W., Charzyńska A., Nienałtowski K., Gambin A., Computational modeling of sphingolipid metabolism, BMC SYSTEMS BIOLOGY, ISSN: 1752-0509, DOI: 10.1186/s12918-015-0176-9, Vol.9, pp.47-1-16, 2015
Abstract:

Background
As suggested by the origin of the word, sphingolipids are mysterious molecules with various roles in antagonistic cellular processes such as autophagy, apoptosis, proliferation and differentiation. Moreover, sphingolipids have recently been recognized as important messengers in cellular signaling pathways. Notably, sphingolipid metabolism disorders have been observed in various pathological conditions such as cancer and neurodegeneration.

Results
The existing formal models of sphingolipid metabolism focus mainly on de novo ceramide synthesis or are limited to biochemical transformations of particular subspecies. Here, we propose the first comprehensive computational model of sphingolipid metabolism in human tissue. Contrary to the previous approaches, we use a model that reflects cell compartmentalization thereby highlighting the differences among individual organelles.

Conclusions
The model that we present here was validated using recently proposed methods of model analysis, allowing to detect the most sensitive and experimentally non-identifiable parameters and determine the main sources of model variance. Moreover, we demonstrate the usefulness of our model in the study of molecular processes underlying Alzheimer’s disease, which are associated with sphingolipid metabolism.

Keywords:

Sphingolipid metabolism, Kinetic model, Sensitivity analysis

Affiliations:
Wronowska W.-other affiliation
Charzyńska A.-University of Warsaw (PL)
Nienałtowski K.-IPPT PAN
Gambin A.-other affiliation

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