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Kraikivski, P.

Publications and source records attributed to Kraikivski, P..

7 recordsLinked to original sources
bioRxiv2026

Stochastic Boolean Model of Death Signaling in MCF7:5C Predicts Cell Death Inducers and Inhibitors

Disrupted cell death signaling contributes significantly to the inappropriate survival of cancer cells, enabling them to evade the apoptotic processes that normally eliminate damaged or abnormal cells. To assess the impact of disrupted cell death signaling on cancer cell survival, we developed a continuous-time stochastic Boolean model of apoptosis signaling in MCF7:5C human breast cancer cells that are resistant to estrogen deprivation and undergo apoptosis in response to estrogen. The model was calibrated to replicate the dynamics of key apoptosis regulators in MCF7:5C cells before and after 17{beta}-estradiol (E2) treatment. Subsequently, the calibrated model was used to predict both the single and double perturbations that inhibit cell death in estradiol-treated MCF7:5C cells, and additional single interventions capable of reinducing apoptosis. For example, strains with single-gene deletion of PERK, eIF2, CHOP, or ATF4 enabled E2-treated MCF7:5C cells to evade apoptosis. However, additional inhibition of SRC, PI3K, ESR1, CEBPB, or MAPK8 restored apoptotic responses in these resistant cells. We identified 30 resistant cell strains with double mutations and 506 drug-target combinations capable of inducing four distinct modes of cell death: direct CASP7 activation, extrinsic apoptosis, intrinsic apoptosis, and combined intrinsic/extrinsic apoptosis. Overall, our model successfully explains the drug response behavior of MCF7:5C cells, predicts mechanisms of drug resistance, and suggests treatment strategies to overcome this resistance.

cancer biology↗
bioRxiv2024

WEE1 inhibition delays resistance to CDK4/6 inhibitor and antiestrogen treatment in estrogen receptor-positive breast cancer

Although endocrine therapies and Cdk4/6 inhibitors have produced significantly improved outcomes for patients with estrogen receptor positive (ER+) breast cancer, continuous application of these drugs often results in resistance. We hypothesized that cancer cells acquiring drug resistance might increase their dependency on negative regulators of the cell cycle. Therefore, we investigated the effect of inhibiting WEE1 on delaying the development of resistance to palbociclib and fulvestrant. We treated ER+ MCF7 breast cancer cells with palbociclib alternating with a combination of fulvestrant and a WEE1 inhibitor AZD1775 for 12 months. We found that the alternating treatment prevented the development of drug resistance to palbociclib and fulvestrant compared to monotherapies. Furthermore, we developed a mathematical model that can simulate cell proliferation under monotherapy, combination or alternating drug treatments. Finally, we showed that the mathematical model can be used to minimize the number of fulvestrant plus AZD1775 treatment periods while maintaining its efficacy.

systems biology↗
bioRxiv2024

Normal and Aberrant Cell Cycles Characterized by a Continuous-Time Stochastic Boolean Model of Cell Cycle Regulation in Budding Yeast

The cell cycle of budding yeast is governed by an intricate protein regulatory network whose dysregulation can lead to lethal mistakes or aberrant cell division cycles. In this work, we model this network in a Boolean framework for stochastic simulations. Our model is sufficiently detailed to account for the phenotypes of 41 mutant yeast strains (85% of the experimentally characterized strains that we simulated) and also to simulate an endoreplicating strain (multiple rounds of DNA synthesis without mitosis) and a strain that exhibits Cdc14 endocycles (periodic transitions between metaphase and anaphase). Because our model successfully replicates the observed properties of both wild-type yeast cells and many mutant strains, it provides a reasonable, validated starting point for more comprehensive stochastic-Boolean models of cell cycle controls. Such models may provide a better understanding of cell cycle anomalies in budding yeast and ultimately in mammalian cells.

systems biology↗
bioRxiv2023

A Perturbation Approach for Refining Boolean Models of Cell Cycle Regulation

Considerable effort is required to build mathematical models of large protein regulatory networks. Utilizing computational algorithms that guide model development can significantly streamline the process and enhance the reliability of the resulting models. In this article we present a perturbation approach for developing data-centric Boolean models of cell cycle regulation. We assign a score to a network based on the steady states of the network-dynamics, and the dynamical trajectories corresponding to the initial conditions. Then, perturbation analysis is used to find new networks with lower scores, in which dynamical trajectories traverse through the correct cell cycle path with high frequency. We apply this method to refine Boolean models of cell cycle regulation in budding yeast and mammalian cells.

systems biology↗
bioRxiv2020

Modeling and Analysis of the Macronutrient Signaling Network in Budding Yeast

In eukaryotes, distinct nutrient signals are relayed by specific plasma membrane receptors to signal transduction pathways that are interconnected in complex information-processing networks. The function of these networks is to govern robust cellular responses to unpredictable changes in the nutritional environment of the cell. In the budding yeast, Saccharomyces cerevisiae, these nutrient signaling pathways and their interconnections have been well characterized. However the complexity of the signaling network confounds the interpretation of the overall regulatory logic of the control system. Here, we propose a literature-curated molecular mechanism of the integrated nutrient signaling network in budding yeast, focusing on early temporal responses to carbon and nitrogen signaling. We build a computational model of this network to reconcile literature-curated quantitative experimental data with our proposed molecular mechanism. We evaluate the robustness of our estimates of the models kinetic parameter values. We test the model by comparing predictions made in mutant strains with qualitative experimental observations made in the same strains. Finally, we use the model to predict nutrient-responsive transcription factor activities in a number of mutant strains undergoing complex nutrient shifts.

systems biology↗
bioRxiv2019

Genetic interactions derived from high-throughput phenotyping of 7,350 yeast cell cycle mutants

Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.\n\nAuthor SummaryThe process of cell division, also called the cell cycle, is controlled by a highly complex network of interconnected genes. If this process goes awry, diseases such as cancer can result. In order to unravel the complex interactions within the cell cycle control network, computational biologists have developed mathematical models that describe how different cell cycle genes are related. These models are built using large datasets describing the effect of mutating one or more genes within the network. In this manuscript, we present a novel method for producing such datasets. Using our method, we generate 7,350 yeast mutants to explore the interactions between key cell cycle genes. We measure the effect of the mutations by monitoring the growth rate of the yeast mutants under different environmental conditions. We use our mutants to revise an existing model of the yeast cell cycle and present a dataset of [~]44,000 gene by environment combinations as a resource to the yeast genetics and modeling communities.

systems biology↗