Krzysztof Psiuk-Maksymowicz

CELL CYCLE AS AN OBJECT OF CONTROL

This chapter is devoted to models of cancer growth and anticancer therapies that put special emphasis on the dependence of therapy efficiency on cell cycle. First, biological background is introduced and detailed description of a cell cycle is given, based on the review of biological literature. It is supplemented with information about chosen chemotherapeutic drugs and their efficacy with respect to the cell cycle. Next, pharmacokinetic and pharmacodynamics aspects of chemotherapeutics are briefly described. They along with cell cycle specificity of drugs may lead to various phenomena of resonances and aftereffects that need to be taken into account in therapy and synchronization of treatment protocols. These issues are mentioned in a separate section of this chapter. Finally, models that incorporate evolution of drug resistance are presented. For all models, the problem of finding a suitable treatment protocol is formulated as a problem of control optimization and some results of application of optimization theory to solve these problems are presented.

Integrated System Supporting Research on Environment Related Cancers

There are many impediments to progress in cancer research. Insufficient or low quality data and computational tools that are dispersed among various sites are one of them. In this paper we present an integrated system that combines all stages of cancer studies, from gathering of clinical data, through elaborate patient questionnaires and bioinformatics tools, to data warehousing and preparation of analysis reports.

A Holistic Approach to Testing Biomedical Hypotheses and Analysis of Biomedical Data

Testing biomedical hypotheses is performed based on advanced and usually many-step analysis of biomedical data. This requires sophisticated analytical methods and data structures that allow to store intermediate results, which are needed in the subsequent steps. However, biomedical data, especially reference data, often change in time and new analytical methods are created every year. This causes the necessity to repeat the iterative analyses with new methods and new reference data sets, which in turn causes frequent changes of the underlying data structures. Such instability of data structures can be mitigated by the use of the idea of data lake, instead of traditional database systems.

Scalability of a Genomic Data Analysis in the BioTest Platform

BioTest platform is dedicated for the processing of biomedical data that originate from various measurement techniques. This includes next-generation sequencing (NGS), that focuses the attention of researchers all of the world due to its broad possibilities in determining the structure of the DNA and RNA. However, the analysis of data provided by NGS requires large disk space, and is time-consuming, becoming a challenge for the data processing systems. In this paper, we have analyzed the possibility of scaling the BioTest platform in terms of genomic data analysis and platform architecture. Scalability tests were carried out using next-generation sequencing data and relied on methods for detection of somatic mutations and polymorphisms in the human DNA. Our results show that the platform is scalable, allowing to significantly reduce the execution time of performed calculations. However, the scalability capabilities depend on the experiment methodology and homogeneity of resources required by each task, which in NGS studies can be highly variable.

Functional Interpretation of Gene Sets: Semantic-Based Clustering of Gene Ontology Terms on the BioTest Platform

Modern high-throughput technologies based on genome, transcriptome or proteome profiling provide abundance of data that needs to be processed, analyzed and, finally, interpreted. Effective and efficient analysis of data coming from molecular profiling is crucial for a detailed diagnosis, prognosis, and prediction of therapy outcome. Meaningful conclusions can be drawn only by the use of sophisticated methods for biomedical and molecular data analysis and interpretation. In this study we present the approach for functional interpretation of gene or protein sets with clusters of Gene Ontology terms. We analyze transcription profiles of human cell line K562 and we show that clustering allows grouping functionally related GO terms and therefore obtaining more concise and comprehensive description. By applying cluster-specific data aggregation tool we are able to calculate statistics for the individual clusters of GO terms and compare the number of differentially expressed genes between two sample pairs. The presented tool is implemented as a part of annotation module available on the BioTest remote platform for hypothesis testing and analysis of biomedical data.

Rigid and Non-rigid Registration Algorithm Evaluation in MRI for Breast Cancer Therapy Monitoring

One of the most common methods in breast cancer radiotherapy planning is Magnetic Resonance Imaging (MRI). It is also used for patient evaluation during treatment because of its sensitivity and lack of ionizing radiation. During each imaging session a patient position can be different and inaccuracies can occur. In this case it is very difficult to compare two image sets originating from different patient examination. The main goals of this work were to implement an algorithm, based on affine transformation with Mutual Information as the quality factor of images match and the method based on the Navier-Lame equation for elastic image co-registration. The rigid transformation is used for the preliminary processing, and the non-rigid transformation allows for successful co-registration of both image sets. Our results were evaluated visually, and the MI indices were calculated. These algorithms allowed for image co-registration in different imaging sessions during the course of treatment.

Comparison of k-means related clustering methods for nuclear medicine images segmentation

In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

Structured Models and Their Use in Modeling Anticancer Therapies

Tumorigenesis is a very complex pathological process, evolving through different parallel pathways. The list of hallmark capabilities which cancer has to acquire was presented in two famous review papers by Hanahan and Weinberg (Cell 100:57–70, 2000; Cell 144:646–674, 2011). Following recent biological discoveries, especially those in molecular biology, mathematicians try to create models adequate to knowledge in the biomedical field, oriented on specific aspects of tumor development. They apply various modeling techniques in order to perform this task. Among these techniques one can distinguish models based on partial differential equations, single-cell-based models, cellular automata, and others. This chapter is devoted to models with structure. Structure may have different meanings, it may refer to the space where cells develop, cellular level of differentiation, or some other physiological feature of the cell, it may also refer to mutual relations among the elements forming the whole system, as it is in agent-based models.

Signaling Pathways Dynamics and Cancer Treatment

The rapid development of the biological research techniques in recent years provides more and more high quality data. Current technology allows to observe not only the whole body, tissue, or cell but also what happens inside the single cell, for example the time change (dynamics) of the number and location of biomolecules such as proteins, and their properties such as phosphorylation. With this knowledge it becomes obvious that the intracellular interactions between various molecules are not straightforward but complex with many mutual dependencies and feedback loops. It becomes clear that for a better understanding of the networks and their dynamics a complex approach is required. This approach can be based on the methodology of system engineering. It involves construction of mathematical models of observed phenomena and then their analysis. Mathematical models may be deterministic, based on ordinary differential equations (ODEs) or stochastic based on reaction propensities. They cover the network of interactions of intracellular species called the signaling pathways. In this chapter we define signaling pathways, describe main reaction types and corresponding equations, describe the main numerical methods for simulation of deterministic and stochastic models, and discuss the basics of the signaling pathways model analysis including stability, sensitivity, and bifurcation analysis. At the end we present an example of the p53 signaling pathway model and discuss possible anticancer therapies using available control signals.

Therapy Optimization in Population Dynamics Models

Cell subpopulations dealt with in the preceding chapter were the result of compartmentalizing the model. In this chapter we focus on growth of cancer cells and the associated vascular system and interactions with the immune system. Therefore, though the variables will still describe the amount of cells of different type, there will be no flux from one type to another. We start with introduction of standard models of population dynamics, with exponential, Gompertzian, and logistic growth. The difference between stochastic and deterministic approach to model population size is explained, a point often misinterpreted in various sources. Then, the angiogenic aspects of cancer growth are discussed, followed by several models of cancer growth including vascularization. Consequently, the problem of optimization of antiangiogenic and combined therapies is analyzed. Finally, models of gene and immunotherapy in cancer treatments are briefly reviewed. In addition to discussion of optimal treatment protocols, the focus of this chapter is on the formal, system engineering-based analysis of dynamical properties of systems under investigation, such as stability and controllability.