Joanna Stachowska-Pietka, PhD - Head of Lab, ORCID
Prof. Jacek Waniewski, PhD, DSc, ORCID
Prof. Leon Bobrowski, PhD, DSc, ORCID
Assoc. Prof. Małgorzata Dębowska, PhD, DSc, ORCID
Assoc. Prof. Jan Poleszczuk, PhD, DSc, ORCID
Elżbieta Olejarczyk, PhD, DSc, ORCID
Mauro Pietribiasi, PhD, ORCID
Leszek Pstraś, PhD, ORCID
Julia Grajek, MSc (PhD student), ORCID
Kamil Wołos, MSc (PhD student), ORCID
Urszula Bialonczyk MSc (PhD student), ORCID
Research profile
Our mission is to create new mathematical, computational and informatics tools for advanced analysis of clinical and biomedical findings, and to improve diagnosis and optimization of internal illness therapy. Our research is focused on broadly understood mathematical modeling to the description of (patho-) physiological processes in order to support diagnosis and optimization of therapy.
In particular, our research is related to clinical aspects of extracorporeal blood purification in patients with renal insufficiency (undergoing peritoneal dialysis and hemodialysis), functions of cardiovascular system in dialysis patients, water and solute transport in whole body and locally through the tissue. We are looking for a new, and work on optimization of existing, cancer therapies. We are also interested in data mining aimed at discovering useful patterns in multidimensional data sets.
The work is carried out in collaboration with foreign and national research centres such as Department of Nephrology, Medical University of Lublin, Military Institute of Medicine in Warszawie, Karolinska Institutet in Stockholm and University of Gothenburg (Sweden), Centro Medico Nacional Siglo XXI in Mexico City (Mexico), Keele University (UK), Hospital Universitari in Barcelona (Spain), National Academy of Sciences of Ukraine, National Institute of Health (USA).
Selected research activities
Assessment of hemodialysis and peritoneal dialysis effectiveness and physiological interpretation of parameters, as well as the description of water and solutes transport processes occurring during dialysis. In particular, prediction of peritoneal dialysis effectiveness, and water and solute transport determination based on individual patient transport parameters. Moreover, the extension of the standard analysis of the hemodialysis coefficients on phosphate and calcium transport and mathematical modeling of water transport between compartments of the body, caused by rapid removal of water during hemodialysis, by taking into account the kinetics of selected substances present in the blood, such as albumin, sodium and potassium.
Modeling of the impact of hemodialysis on the cardiovascular system. In particular, analysis of the phenomenon of blood pressure drop during the treatment based on a model that takes into account the mechanisms of transport of water and selected solutes between body compartments and mechanisms of blood pressure regulation within the circulatory system. Moreover, investigation the role of various abnormalities in HD patients on the propagation of pulse wave using mathematical modeling of the whole arterial tree with arteriovenous fistula and compare these predictions to the respective clinical measurements. The models of pulse wave propagation describe 1D flow in compliant arterial vessels using two variables: blood flow velocity and blood pressure (or equivalently the cross-sectional area of compliant vessel), and were previously shown to be in agreement with rich set of physiological data.
Modeling of water and solute transport processes at the tissue level based on so-called distributed modeling, which takes into account local tissue physiology and its variability (hydration, elasticity, transport parameters). In this approach, the equation describing transport of water and solutes (of various molecular size and charge) are derived from the local physiology, taking into account transport through the porous blood capillary wall due to the Starling's forces, local tissue lymphatic absorption and transport through the tissue and cells. In particular, the analysis of various aspects of peritoneal transport and the estimation of local transport parameters based on clinical and experimental data using models of peritoneal fluid reabsorption, models of osmotic transport, models of bidirectional transport based on two-phase structure of the interstitium. In addition, modeling of transport processes in tumour tissues with particular emphasis on the process of drug penetration during intraperitoneal chemotherapy. In this therapy, applied in case of small tumours and/or metastases located in tissues surrounding the peritoneal cavity, peritoneal cavity is used to deliver the drug to the tumour cells. The distributed approach can be applied, taking into account the differences in local physiology of the cancer tissue, for the description of water and solute transport as well as for the prediction of solutes penetration depth according to their concentration in the fluid administered to the peritoneal cavity.
Development a quantitative mathematical framework that predicts systemic response of metastatic tumors to focal radiotherapy – either alone or in combination with immunotherapy. The ultimate aim of this project will be to propose such framework as a clinical decision support system to derive optimal radiation fractionation protocols and irradiation site for immune activation on a per patient basis.
Data mining aimed at discovering useful patterns in multidimensional data sets. Data mining tools based on minimization of the convex and piecewise linear (CPL) criterion functions are developed and used by us for many years. The considered family of the CPL criterion functions can be linked to the concept of linear separability of multivariate data sets and to the perceptron theory. Using the CPL criterion functions in the processing and analysis of large, multidimensional data sets is based on computational techniques, which are called the basis exchange algorithms. The optimal parameters found as a result of minimizing CPL criterion functions have been used among others, for the selection of discriminative features subsets by using the relaxed linear separability (RLS) and flat patterns discovering and modeling of features interactions. The RLS method of feature selection has been recently applied to real clinical data set MIA (containing genetic and phenotypic features) describing inflammatory status of patients on dialysis. The work demonstrates, among others, the complementary roles of genetic and environmental features in prognostic modeling of a given patient condition.
Application of various bioinformatics methods and extensive statistical analysis to determine factors associated with treatment outcomes of dialysis and clinical complications. In particular, in patients with chronic kidney disease (CKD) analysis of various data type: anthropometric, demographic, diet, blood biomarkers, genotype (single nucleotide polymorphism, SNP) physiological-based parameters estimated by mathematical modeling, in order to determine the factors affecting the effectiveness of the therapy as well as to link them with the co-occurring diseases.
Key words: mathematical modelling, computer simulations, physiology, hemodialysis, peritoneal dialysis, dialysis adequacy, peritoneal transport, compartment models, kinetic modelling, diffusion, convection, cancer, immune system, radiotherapy, chemotherapy, pulse wave propagation modeling, cardio-vascular system, data mining
External funding:
Honours and Awards (recent):
Selected publications
Nałęcz Institute of Biocybernetics and Biomedical Engineering PAS, Ks. Trojdena 4 st., 02-109 Warsaw, POLAND
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