publications

2024

1. MBE

   Polymorphism-Aware Models in RevBayes: Species Trees, Disentangling Balancing Selection, and GC-Biased Gene Conversion

   Svitlana Braichenko, Rui Borges, and Carolin Kosiol

   Molecular Biology and Evolution, Jul 2024

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   The role of balancing selection is a long-standing evolutionary puzzle. Balancing selection is a crucial evolutionary process that maintains genetic variation (polymorphism) over extended periods of time; however, detecting it poses a significant challenge. Building upon the Polymorphism-aware phylogenetic Models (PoMos) framework rooted in the Moran model, we introduce a PoMoBalance model. This novel approach is designed to disentangle the interplay of mutation, genetic drift, and directional selection (GC-biased gene conversion), along with the previously unexplored balancing selection pressures on ultra-long timescales comparable with species divergence times by analyzing multi-individual genomic and phylogenetic divergence data. Implemented in the open-source RevBayes Bayesian framework, PoMoBalance offers a versatile tool for inferring phylogenetic trees as well as quantifying various selective pressures. The novel aspect of our approach in studying balancing selection lies in polymorphism-aware phylogenetic models’ ability to account for ancestral polymorphisms and incorporate parameters that measure frequency-dependent selection, allowing us to determine the strength of the effect and exact frequencies under selection. We implemented validation tests and assessed the model on the data simulated with SLiM and a custom Moran model simulator. Real sequence analysis of Drosophila populations reveals insights into the evolutionary dynamics of regions subject to frequency-dependent balancing selection, particularly in the context of sex-limited color dimorphism in Drosophila erecta.

2022

1. Springer

   Bayesian Learning of Effective Chemical Master Equations in Crowded Intracellular Conditions

   Svitlana Braichenko, Ramon Grima, and Guido Sanguinetti

   In Computational Methods in Systems Biology, Jul 2022

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   Biochemical reactions inside living cells often occur in the presence of crowders - molecules that do not participate in the reactions but influence the reaction rates through excluded volume effects. However the standard approach to modelling stochastic intracellular reaction kinetics is based on the chemical master equation (CME) whose propensities are derived assuming no crowding effects. Here, we propose a machine learning strategy based on Bayesian Optimisation utilising synthetic data obtained from spatial cellular automata (CA) simulations (that explicitly model volume-exclusion effects) to learn effective propensity functions for CMEs. The predictions from a small CA training data set can then be extended to the whole range of parameter space describing physiologically relevant levels of crowding by means of Gaussian Process regression. We demonstrate the method on an enzyme-catalyzed reaction and a genetic feedback loop, showing good agreement between the time-dependent distributions of molecule numbers predicted by the effective CME and CA simulations.

2021

1. J R Soc Interface

   Distinguishing between Models of Mammalian Gene Expression: Telegraph-like Models versus Mechanistic Models

   Svitlana Braichenko, James Holehouse, and Ramon Grima

   Journal of The Royal Society Interface, Oct 2021

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   Two-state models (telegraph-like models) have a successful history of predicting distributions of cellular and nascent mRNA numbers that can well fit experimental data. These models exclude key rate limiting steps, and hence it is unclear why they are able to accurately predict the number distributions. To answer this question, here we compare these models to a novel stochastic mechanistic model of transcription in mammalian cells that presents a unified description of transcriptional factor, polymerase and mature mRNA dynamics. We show that there is a large region of parameter space where the first, second and third moments of the distributions of the waiting times between two consecutively produced transcripts (nascent or mature) of two-state and mechanistic models exactly match. In this region: (i) one can uniquely express the two-state model parameters in terms of those of the mechanistic model, (ii) the models are practically indistinguishable by comparison of their transcript numbers distributions, and (iii) they are distinguishable from the shape of their waiting time distributions. Our results clarify the relationship between different gene expression models and identify a means to select between them from experimental data.

2018

1. PRE

   Phenomenological Cluster-Based Model of Ca2+ Waves and Oscillations for Inositol 1,4,5-Trisphosphate Receptor IP3 Channels

   Svitlana Braichenko, Atul Bhaskar, and Srinandan Dasmahapatra

   Physical Review E, Sep 2018

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   Clusters of IP3 receptor channels in the membranes of the endoplasmic reticulum of many nonexcitable cells release calcium ions in a cooperative manner giving rise to dynamical patterns such as Ca2+ puffs, waves, and oscillations that occur on multiple spatial and temporal scales. We introduce a minimal yet descriptive reaction-diffusion model of IP3 receptors for a saturating concentration of IP3 using a principled reduction of a detailed Markov chain description of individual channels. A dynamical systems analysis reveals the possibility of excitable, bistable, and oscillatory dynamics of this model that correspond to three types of observed patterns of calcium release: puffs, waves, and oscillations, respectively. We explain the emergence of these patterns via a bifurcation analysis of a coupled two-cluster model, compute the phase diagram, and quantify the speed of the waves and period of oscillations in terms of system parameters. We connect the termination of large-scale Ca2+ release events to IP3 unbinding or stochasticity.

2. Nonlinear Model of Calcium Excitations in Biomembranes

   S. I. Braichenko, and A. N. Vasilev

   Ukrainian Journal of Physics, Jun 2018

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   A model is proposed to describe the calcium redistribution in biological substances. The model takes into consideration that calcium can be located inside or outside a cell. Calcium is redistributed due to its transport from the cell volume into the outer space and backward. The model makes allowance for the calcium diffusion into the outer space. It is shown that there are two modes of functioning of the system. In one of them, the initial perturbation of the calcium concentration in the extracellular space monotonically vanishes in time. In the other mode, this perturbation first grows, but afterward decreases to the zero value. The calcium concentration in the intracellular space is shown to be a critical parameter that governs the system operation mode.

2012

1. Modeling of Postsynaptic Membrane Activation

   S. I. Braichenko, and A. N. Vasilev

   Journal of Physical Studies, Jun 2012