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Branislav Brutovsky

Publications and source records attributed to Branislav Brutovsky.

7 recordsLinked to original sources
arXiv2019

In Silico Implementation of Evolutionary Paradigm in Therapy Design: Towards Anti-Cancer Therapy as Darwinian Process

In here presented in silico study we suggest a way how to implement the evolutionary principles into anti-cancer therapy design. We hypothesize that instead of its ongoing supervised adaptation, the therapy may be constructed as a self-sustaining evolutionary process in a dynamic fitness landscape established implicitly by evolving cancer cells, microenvironment and the therapy itself. For these purposes, we replace a unified therapy with the `therapy species', which is a population of heterogeneous elementary therapies, and propose a way how to turn the toxicity of the elementary therapy into its fitness in a way conforming to evolutionary causation. As a result, not only the therapies govern the evolution of different cell phenotypes, but the cells' resistances govern the evolution of the therapies as well. We illustrate the approach by the minimalistic ad hoc evolutionary model. Its results indicate that the resistant cells could bias the evolution towards more toxic elementary therapies by inhibiting the less toxic ones. As the evolutionary causation of cancer drug resistance has been intensively studied for a few decades, we refer to cancer as a special case to illustrate purely theoretical analysis.

q-bio.PE↗
arXiv2018

A New Conceptual Framework for the Therapy by Optimized Multidimensional Pulses of Therapeutic Activity. The case of Multiple Myeloma Model

We developed simulation methodology to assess eventual therapeutic efficiency of exogenous multiparametric changes in a four-component cellular system described by the system of ordinary differential equations. The method is numerically implemented to simulate the temporal behavior of a cellular system of multiple myeloma cells. The problem is conceived as an inverse optimization task where the alternative temporal changes of selected parameters of the ordinary differential equations represent candidate solutions and the objective function quantifies the goals of the therapy. The system under study consists of two main cellular components, tumor cells and their cellular environment, respectively. The subset of model parameters closely related to the environment is substituted by exogenous time dependencies - therapeutic pulses combining continuous functions and discrete parameters subordinated thereafter to the optimization. Synergistic interaction of temporal parametric changes has been observed and quantified whereby two or more dynamic parameters show effects that absent if either parameter is stimulated alone. We expect that the theoretical insight into unstable tumor growth provided by the sensitivity and optimization studies could, eventually, help in designing combination therapies.

q-bio.TO↗
arXiv2018

Toward understanding of the role of reversibility of phenotypic switching in the evolution of resistance to therapy

Reversibility of state transitions is intensively studied topic in many scientific disciplines over many years. In cell biology, it plays an important role in epigenetic variation of phenotypes, known as phenotypic plasticity. More interestingly, the cell state reversibility is probably crucial in the adaptation of population phenotypic heterogeneity to environmental fluctuations by evolving bet-hedging strategy, which might confer to cancer cells resistance to therapy. In this article, we propose a formalization of the evolution of highly reversible states in the environments of periodic variability. Two interrelated models of heterogeneous cell populations are proposed and their behavior is studied. The first model captures selection dynamics of the cell clones for the respective levels of phenotypic reversibility. The second model focuses on the interplay between reversibility and drug resistance in the particular case of cancer. Overall, our results show that the threshold dependencies are emergent features of the investigated model with eventual therapeutic relevance. Presented examples demonstrate importance of taking into account cell to cell heterogeneity within a system of clones with different reversibility quantified by appropriately chosen genetic and epigenetic entropy measures.

q-bio.PE↗
arXiv2015

Towards Inverse Modeling of Intratumoral Heterogeneity

Development of resistance limits efficiency of present anticancer therapies and preventing it remains big challenge in cancer research. It is accepted, at intuitive level, that the resistance emerges as a consequence of cancer cells heterogeneity at molecular, genetic and cellular levels. Produced by many sources, tumor heterogeneity is extremely complex time dependent statistical characteristics which may be quantified by the measures defined in many different ways, most of them coming from statistical mechanics. In the paper we apply Markovian framework to relate population heterogeneity with the statistics of environment. As, from the evolutionary viewpoint, therapy corresponds to a purposeful modification of the cells fitness landscape, we assume that understanding general relation between spatiotemporal statistics of tumor microenvironment and intratumor heterogeneity enables to conceive the therapy as the inverse problem and solve it by optimization techniques. To account for the inherent stochasticity of biological processes at cellular scale, the generalized distance-based concept was applied to express distances between probabilistically described cell states and environmental conditions, respectively.

q-bio.QM↗
arXiv2014

Self-organized manifold learning and heuristic charting via adaptive metrics

Classical metric and non-metric multidimensional scaling (MDS) variants are widely known manifold learning (ML) methods which enable construction of low dimensional representation (projections) of high dimensional data inputs. However, their use is crucially limited to the cases when data are inherently reducible to low dimensionality. In general, drawbacks and limitations of these, as well as pure, MDS variants become more apparent when the exploration (learning) is exposed to the structured data of high intrinsic dimension. As we demonstrate on artificial and real-world datasets, the over-determination problem can be solved by means of the hybrid and multi-component discrete-continuous multi-modal optimization heuristics. Its remarkable feature is, that projections onto 2D are constructed simultaneously with the data categorization (classification) compensating in part for the loss of original input information. We observed, that the optimization module integrated with ML modeling, metric learning and categorization leads to a nontrivial mechanism resulting in generation of patterns of categorical variables which can be interpreted as a heuristic charting. The method provides visual information in the form of non-convex clusters or separated regions. Furthermore, the ability to categorize the surfaces into back and front parts of the analyzed 3D data objects have been attained through self-organized structuring without supervising.

physics.data-an↗
arXiv2006

Simulation of Multicellular Tumor Spheroids Growth Dynamics

The inverse geometric approach to the modeling of the growth of circular objects revealing required features, such as the velocity of the growth and fractal behavior of their contours, is presented. It enables to reproduce some of the recent findings in morphometry of tumors with the possible implications for cancer research. The technique is based on cellular automata paradigm with the transition rules selected by optimization procedure performed by the genetic algorithms.

physics.med-ph↗