Vitamin B complex suppresses neuroinflammation in activated microglia: in vitro and in silico approach combined with dynamical modeling.

Activated microglia is critically involved in the regulation of neuroinflammation/neurodegradation. Hereby, the anti-inflammatory effects of the vitamin B complex (VBC - B1, B2, B3, B5, B6, and B12) on the function and phenotype of lipopolysaccharide (LPS)-stimulated BV2 microglial cells were examined in vitro. Additionally, VBC-treated microglia supernatants were evaluated on SH-SY5Y cells to investigate the effects on neurons' viability. Further, anti-inflammatory mechanisms of VBC were examined by molecular dockingstudies to determine the binding affinity of each VBC component to Toll-like receptor 4 (TLR4) signalling pathway proteins and inducible nitric oxide synthase. In addition, the dynamical model which simulates VBC inhibition of TLR4 signalling pathway proteins activated by LPS has been constructed and excellent agreement with experimental data has been observed (adjR2 = 0.9715 and 0.9909 for TNF-α and IL-6, respectively). The obtained data demonstrated that VBC treatment reduced the inflammatory mediators secreted by LPS-stimulated microglia, diminished their neurotoxic effects against neurons, and induced changes in phenotype profile toward M2 microglia type. Finally, the constructed dynamical model provides deeper insight into the involvement of each VBC component on the VBC inhibitory potential toward the TLR4 signalling pathway and enables optimization of novel VBC formulations as well as inhibitory potential of new putative inhibitors.

Interplay of disorder and type of driving in disordered ferromagnetic systems.

We investigate the effects of adiabatic, quasistatic, and finite-rate types of driving on the evolution of disordered three-dimensional ferromagnetic systems, studied within the frame of the nonequilibrium athermal random field Ising model. The effects were examined in all three domains of disorder (low, high, and transitional) for all types of driving, and in a wide range of driving rates for quasistatic and finite-rate driving, providing an extensive overview and comparison of the joint effects that the disorder, type of driving, and rate regime have on the system's behavior.

Mechanism of subcritical avalanche propagation in three-dimensional disordered systems.

We present a numerical study on necessary conditions for the appearance of infinite avalanche below the critical point in disordered systems that evolve throughout metastable states. The representative of those systems is the nonequilibrium athermal random-field Ising model. We investigate the impact on propagation of infinite avalanche of both the interface of flipped spins at the avalanche's starting point and the number of independent islands of flipped spins in the system at the moment when the avalanche starts. To deduce what effects are originated due to finite system's size, and to distinguish them from the real necessary conditions for the appearance of the infinite avalanche, we examined lattices of different sizes as well as other key parameters for the avalanche propagation.

Avalanche properties in striplike ferromagnetic systems.

We present numerical findings on the behavior of the athermal nonequilibrium random-field Ising model of spins at the thin striplike L_{1}×L_{2}×L_{3} cubic lattices with L_{1}

From biophysics to 'omics and systems biology.

Recent decades brought a revolution to biology, driven mainly by exponentially increasing amounts of data coming from "'omics" sciences. To handle these data, bioinformatics often has to combine biologically heterogeneous signals, for which methods from statistics and engineering (e.g. machine learning) are often used. While such an approach is sometimes necessary, it effectively treats the underlying biological processes as a black box. Similarly, systems biology deals with inherently complex systems, characterized by a large number of degrees of freedom, and interactions that are highly non-linear. To deal with this complexity, the underlying physical interactions are often (over)simplified, such as in Boolean modelling of network dynamics. In this review, we argue for the utility of applying a biophysical approach in bioinformatics and systems biology, including discussion of two examples from our research which address sequence analysis and understanding intracellular gene expression dynamics.

Effects of Population Dynamics on Establishment of a Restriction-Modification System in a Bacterial Host.

In vivo dynamics of protein levels in bacterial cells depend on both intracellular regulation and relevant population dynamics. Such population dynamics effects, e.g., interplay between cell and plasmid division rates, are, however, often neglected in modeling gene expression regulation. Including them in a model introduces additional parameters shared by the dynamical equations, which can significantly increase dimensionality of the parameter inference. We here analyse the importance of these effects, on a case of bacterial restriction-modification (R-M) system. We redevelop our earlier minimal model of this system gene expression regulation, based on a thermodynamic and dynamic system modeling framework, to include the population dynamics effects. To resolve the problem of effective coupling of the dynamical equations, we propose a "mean-field-like" procedure, which allows determining only part of the parameters at a time, by separately fitting them to expression dynamics data of individual molecular species. We show that including the interplay between kinetics of cell division and plasmid replication is necessary to explain the experimental measurements. Moreover, neglecting population dynamics effects can lead to falsely identifying non-existent regulatory mechanisms. Our results call for advanced methods to reverse-engineer intracellular regulation from dynamical data, which would also take into account the population dynamics effects.