Deuxième principe de la thermodynamique

Le deuxième principe de la thermodynamique (également connu sous le nom de deuxième loi de la thermodynamique ou principe de Carnot) établit l'irréversibilité des phénomènes physiques, en particulier lors des échanges thermiques. C'est un principe d'évolution qui fut énoncé pour la première fois par Sadi Carnot en 1824. Il a depuis fait l'objet de nombreuses généralisations et formulations successives par Clapeyron (1834), Clausius (1850), Lord Kelvin, Ludwig Boltzmann en 1873 et Max Planck (voir Histoire de la thermodynamique et de la mécanique statistique), tout au long du et au-delà jusqu'à nos jours. Le second principe introduit la fonction d'état entropie : S, usuellement assimilée à la notion de désordre qui ne peut que croître au cours d'une transformation réelle. Énoncé de la loi Énoncé Le deuxième principe de la thermodynamique énonce que : Toute transformation d'un système thermodynamique s'effectue avec augmentation de l'entropie globa

Determining The Potential Of Salinity Gradient Energy Source Using An Exergy Analysis

Arash Emdadi

Mixing of fresh (river) water and salty water (seawater or saline brine) in a control fashion would produces an electrical energy known as salinity gradient energy (SGE). Two main conversion technologies of SGE are membrane-based processes; pressure retarded osmosis (PRO) and reverse electrodialysis (RED). In PRO, semipermeable membranes placed between the two streams of solutions allow the transport of water from low-pressure diluted solution to high-pressure concentrated solution. RED requires two alternating semipermeable membranes that allow the diffusion of the ions but not the flow of H2O. Lifetime and power density of the semipermeable membrane are two main factors affecting on deployment of PRO and RED. Semipermeable membranes with lifetime greater than 10 years and power density higher than 5 W/m(2) would lead to faster development of this conversion technology. An exergy analysis of an SGE system of sea-river can be applied to calculate the maximum potential power for electricity generation. Seawater is taken as reference environment (global dead state) for calculating the exergy of water since the seawater is the final reservoir. Once the fresh water is mixed with water of the sea or lake it becomes unuseful for human, agricultural or industrial uses loses all its exergy. Aqueous sodium chloride solution model is used in this study to calculate the thermodynamic properties of seawater. This model does not consider seawater as an ideal model and provides accurate thermodynamics properties of sodium chloride solution. As a case study, exergy calculation of Iran's Urmia Lake- GadarChay River system. The chemical exergy analysis considers sodium chloride (NaCl) as main salt in the water of Lake Urmia. The sodium chloride concentration is more than 200 g/L in recent years. Based on the exergy results the potential power of this system is 329 MW. This results indicates a high potential for constructing power plant for salinity gradient energy conversion.

Amer Soc Mechanical Engineers2016

Analysis of cellular and drug action mechanisms in the malaria parasites using systems biology approaches

Anush Chiappino-Pepe

Malaria puts at risk of infection half of the world population and still kills around half a million people every year. The rapid and worrisome rise of drug-resistant malaria parasites hinders the treatment of malaria and calls for a global collaboration to eradicate this disease. The technological advances have allowed sequencing the genome and measuring metabolites, proteins, and mRNAs of the parasites at a high-throughput, which has provided insights into the biological processes of these organisms. However, many aspects of the biology of the malaria parasites remain hidden and hinder the development of effective and selective antimalarial therapies. The understanding of the cell function as a whole rather than the study of its components in isolation will enable the development of strategies to engineer and target the cell function. Only computational tools can provide a comprehensive understanding of the cellular function in different contexts and upon perturbation. Moreover, computational methods provide testable hypotheses that can guide and accelerate experimental discoveries. Metabolism is to date the most characterized biological process in the cell and is an attractive source of drug targets. Metabolic networks encompass all available information on the biochemistry of an organism and represent an attractive scaffold to integrate omics data and analyze complex metabolic processes. In this thesis, we present the metabolic models of two malaria parasites, i.e., Plasmodium falciparum and Plasmodium berghei, and a set of mathematical and computational methods for the comprehensive and unbiased analysis of metabolic networks. We focus on the bioenergetics-based and metabolic control analysis of the malaria parasitesâ metabolism to find drug targets and investigate drug action mechanisms. Firstly, we develop computational methods that follow principles from network thermodynamics to identify essential metabolic functions, thermodynamic bottlenecks, and nutritional requirements of P. falciparum. Secondly, we define a mathematical framework for Phenotype Mapping (PhenoMapping), which associates a cellular phenotype with the underlying cellular processes and conditions. We applied PhenoMapping to investigate the PlasmoGEM phenotypes obtained for P. berghei in the blood stages of its life cycle. This analysis enables the identification of non-conditionally and conditionally essential metabolic functions and the development of context-specific metabolic models. Thirdly, we present a Mixed Integer Synthetic Lethal Enumeration (MISyLE) method for the optimal identification of essential and all redundant (synthetic lethal) sets of reactions and genes for a cellular phenotype. Fourthly, we investigate the effect of antimalarial-based therapies with one or multiple antifolates in the metabolism of P. falciparum in the blood stages, and we examine new drug targeting strategies that might maximize the impact on the intraerythrocytic development of the parasites. Fifthly, we discuss a systems biology approach to accelerate our understanding of biological systems based on the disagreements between in silico and in vivo observations. Finally, we suggest strategies to use the models and computational frameworks developed in this thesis to further investigate host-pathogen interactions, drug action mechanisms, and dormancy in the malaria parasites and other organisms.

EPFL2018

Data, Parameters & Nonlinearities: Development and Applications of Large-scale Dynamic Models of Metabolism

Anirikh Chakrabarti, Vassily Hatzimanikatis, Ljubisa Miskovic, Keng Cher Soh

Dynamic nonlinear models of metabolism offer a significant advantage as compared to constraint-based stoichiometric descriptions. However, progress in the development of large-scale nonlinear models has been hindered by both structural and quantitative uncertainties. In particular, the knowledge about kinetic rate laws and their parameters is till today still very limited when compared to the number of stoichiometric reactions known to be present in a large-scale metabolic model. In addition, strategies to systematically identify and implement large-scale dynamic models for metabolism are still lacking. In this contribution, we propose a novel methodology for development of dynamic nonlinear models for metabolism. Using the ORACLE (Optimization and Risk Analysis of Complex Living Entities) framework, we integrate thermodynamics and available omics and kinetic data into a large-scale stoichiometric model. The resulting set of log-linear kinetic models is used to compute kinetic parameters of the involved enzymatic reactions such as the maximal velocities and Michaelis constants. These kinetic parameters are in turn used to compute populations of stable, nonlinear, dynamic models sharing the same stable steady-state as the log-linear ones. The computed models offer unprecedented possibilities for system analysis, e.g. to study the responses of metabolism upon large perturbations; to investigate time course evolutions in and around the steady state; and to identify multiple steady-states and their basins of attraction. We illustrate the features of the generated models in the case of optimally grown E. coli, where our analysis of the estimated maximal reaction rates highlights the significance of network thermodynamics in constraining the variability of these quantities.

2013