![]() ; ; Fouquier d'Hérouël, Aymeric ![]() in Biosystems (2016), 142 Progress in cell type reprogramming has revived the interest in Waddington’s concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington’s ... [more ▼] Progress in cell type reprogramming has revived the interest in Waddington’s concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington’s landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols. [less ▲] Detailed reference viewed: 126 (6 UL)![]() Simeonidis, Vangelis ![]() in Journal of Industrial Microbiology and Biotechnology (2015), 42(3), 327338 We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the ... [more ▼] We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information—an area which we expect will become increasingly important for metabolic engineering—and present recent developments in the field of metabolic and regulatory integration. [less ▲] Detailed reference viewed: 137 (3 UL)![]() Kolodkin, Alexey ![]() ![]() Poster (2014, December) Detailed reference viewed: 180 (14 UL)![]() Kolodkin, Alexey ![]() ![]() Poster (2014, October) Detailed reference viewed: 181 (17 UL)![]() Kolodkin, Alexey ![]() ![]() Poster (2014, June) Detailed reference viewed: 185 (11 UL)![]() Kolodkin, Alexey ![]() ![]() Poster (2014, June) Detailed reference viewed: 164 (16 UL)![]() Kolodkin, Alexey ![]() ![]() Poster (2013, September 27) In vivo evidence demonstrates three fundamental interconnected adaptive survival mechanisms , which protect against excessive ROS that is generated during mitochondrial dysfunction: (i) autophagy ... [more ▼] In vivo evidence demonstrates three fundamental interconnected adaptive survival mechanisms , which protect against excessive ROS that is generated during mitochondrial dysfunction: (i) autophagy/mitophagy, (ii) adaptive antioxidant response and (iii) NFkB signaling in cancer and neurodegeneration. We have been expanding a kinetic model which recapitulates the consensus understanding of the mechanisms responsible for cellular ROS – management system and performed modular analysis to analyze emergent behavior. We started with the simplest model and added stepwise new modules. We identify the qualitative role (certain emergent behavior) attributed to each module and thus understand the design principles of the system. We propose a detailed, mechanistic, kinetic model for studying how mutations relevant for diseases such as PD and cancer affect the emergent behavior of ROS management network. [less ▲] Detailed reference viewed: 317 (11 UL)![]() Kolodkin, Alexey ![]() ![]() ![]() Poster (2013, May) Reactive Oxygen Species (ROS) generation is an unavoidable background process in the normal functioning of the cell. The greatest contributor to ROS production is the electron transport chain (ETC) where ... [more ▼] Reactive Oxygen Species (ROS) generation is an unavoidable background process in the normal functioning of the cell. The greatest contributor to ROS production is the electron transport chain (ETC) where O2 is reduced to H2O. Some incompletely-reduced oxygen species escape and oxidize a variety of organic molecules (e.g. proteins and lipids in the mitochondrial membrane), leading to molecular dysfunction and initiating a positive feedback loop leading to generation of even more active radicals. Increased ROS concentration damages mitochondria and further increases ROS generation. Healthy cells manage ROS enzymatically with superoxide dismutase and other enzymes, various antioxidants, and ultimately through increased mitophagy of damaged mitochondria. The precise tuning of the latter mechanism is crucial for cell survival and is controlled in the cell by a ROS-induced regulatory network, which consists of many components such as Nrf2, Keap1, Parkin and p62 with a rather complicated cross-talk (Figure 1). In many diseases (cancer, Parkinson’s disease (PD), Huntington’s disease (HD), etc.), various components of the ROS management network are altered. Deconstructing the molecular mechanisms underlying or resulting from these alterations might contribute to better understanding of the dynamics of related pathophysiological processes. We have built a kinetics-based model which recapitulates the consensus understanding of the mechanism responsible for cellular ROS – managing system. [less ▲] Detailed reference viewed: 240 (10 UL) |
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