Sergii Domanskyi, PH.D.
Department of Physics and Astronomy
Michigan State University
Research Associate in Systems Biology and data-driven modeling in the Department of Physics and Astronomy at Michigan State University.
Advisor: Professor Carlo Piermarocchi.
Current research project
Data-driven models of hematological cell fate decision and differentiation
This project is a collaborative effort between scientists with complementary expertise in mathematics and biomedical sciences. The project has three aims:
(i) Develop a mathematical signaling model able to reproduce blood lineage differentiation, using associative memories to represent single cell states. The model will be able to make predictions on the effect on differentiation of specific combinations of receptor ligands and drugs.
(ii) Develop a mathematical model for an ensemble of different hematological cells, under co-culture conditions. The model will describe the dynamics of cells as interacting attractors.
(iii) Verify the predictions of the mathematical modeling using in vitro experiments to detect markers of differentiation, to assess cellular differentiation by flow cytometry, and by performing RNA-seq on pools of cells and on single cells. Cells will be studied as pure populations and in co-culture conditions.
The rapidly increasing availability of gene expression data of different types of cells has created new opportunities for integrating these datasets into mathematical models to make experimentally verifiable predictions. The proposed model will capture the multistable nonlinear dynamics in complex cell signaling networks regulating cell differentiation. This will be realized by using RNA-seq data on pooled cell samples and single cells. The model will make predictions on combinations of transcription factors or receptor ligands that could induce a specific cell lineage. By comparison with our planned experiments, the model will clarify the role of specific receptor ligands in cell fate decision of single cells or a population of cells. The proposed methodology will enhance our general understanding of biological processes and diseases where cell differentiation plays a key role. In particular, this project could provide new biomedical insight in stem cell biology, immunology, hematology, and human development. This project will provide new biomedical insight relevant to biological systems and diseases where crosstalk among cells and cell differentiation play a key role. The project could increases our chances of finding new cures for many pathologies, including immunological, hematological, and developmental diseases.
During 2012 - 2018 I have worked on modelling of several types of materials and biochemical systems with utilized statistical mechanical and physiochemical kinetics approaches and techniques. The topics of interest include multi-input biomolecular signals processes used for actuation, drug delivery and release and other applications, percolation-type modeling of autonomous self-damaging in “smart” composite materials, Monte Carlo and mean-field simulation of certain polymerization processes, reaction-diffusion equations modelling of surface erosion of highly crosslinked polymers, analytical modeling of diffusion of oligonucleotides and other large molecules from hydrogel beads, modeling of progression of viral infection in cell culture resulting in cell apoptosis and necrosis, and cell culture dynamics under stress, etc.
My Thesis included:
Multi-input biomolecular signals processes used for actuation, drug delivery and release and other applications
Percolation-type modeling of autonomous self-damaging in “smart” composite materials
Monte Carlo and mean-field simulation of certain polymerization processes
Reaction-diffusion equations modelling of surface erosion of highly crosslinked polyanhydrides
Analytical modeling of diffusion of oligonucleotides and other large molecules from hydrogel
Modeling of progression of viral infection in cell culture resulting in cell apoptosis and necrosis
Cell cultures dynamics under stress
Also worked on:
Quantum Chemistry methods. Computational techniques in non-adiabatic Molecular Dynamics
Visiting graduate student researcher at Los Alamos National Laboratory, Los Alamos, NM. Center for Nonlinear Studies. Fall semester of 2015. Supervisor: Dr. Dmitry Mozyrsky.
Research Topic: Non-Adiabatic Molecular Dynamics in Multi-Crossing Systems
Non-adiabatic molecular dynamics is important in the study of many photophysical and photochemical reactions. Various approximations are used to calculate transitions between electronic states in large systems such as real molecules. Many of the approaches either have the desired accuracy only in certain scenarios or lead to high computational costs. The novel accelerated Semi-classical Monte Carlo technique based on recently developed algorithm, has features of Surface hopping methods but accounts for quantum coherence effects, while neglecting some nuclear quantum effects such as tunneling, etc. We apply it to the test problems which demonstrate breakdown of common approximations, comparing the solution to that of existing approaches such as Ehrenfest method, Fewest Switches Surface Hopping method and exact quantum solution.
Visiting graduate student researcher at Los Alamos National Laboratory, Los Alamos, NM. Center for Nonlinear Studies. Summer of 2016. Supervisor: Dr. Dmitry Mozyrsky.
Research Topic: Thawed Gaussian Propagation with Recurrent Basis Expansions and Matching-Pursuit Basis Reduction for Simulations of Quantum Dynamics
Thawed Gaussian Approximation that is typically used in non-adiabatic quantum/semi-classical dynamics in multidimensional systems fails after relatively short propagation. Recurrent basis expansions into proper basis functions would lead to a rapid growth of the basis making computation unfeasible. Over the last decade, the Batista group has developed Matching-Pursuit method, along with Split-operator Fourier-transform (MP/SOFT) for basis reduction. Utilizing this method we show how an arbitrary wavefunction is presented as a superposition of Thawed Gaussian Wavepackets (TGWP) and propagated along an arbitrary adiabatic potential energy surfaces, e.g. Morse potential, double-well potential and Tully’s test problems, for desired time duration. With a proper description of a wavefunction on a single adiabatic surface we aim to study non-adiabatic semi-classical dynamics, specifically, a scattering problem.