Novel domain expansion methods to improve the computational efficiency of the Chemical Master Equation solution for large biological networks

Background: Numerical solutions of the chemical master equation (CME) are important for understanding the stochasticity of biochemical systems. However, solving CMEs is a formidable task. This task is complicated due to the nonlinear nature of the reactions and the size of the networks which result in different realizations. Most importantly, the exponential growth of the size of the state-space, with respect to the number of different species in the system makes this a challenging assignment. When the biochemical system has a large number of variables, the CME solution becomes intractable. We introduce the intelligent state projection (ISP) method to use in the stochastic analysis of these systems. For any biochemical reaction network, it is important to capture more than one moment: this allows one to describe the system’s dynamic behaviour. ISP is based on a state-space search and the data structure standards of artificial intelligence (AI). It can be used to explore and update the states of a biochemical system. To support the expansion in ISP, we also develop a Bayesian likelihood node projection (BLNP) function to predict the likelihood of the states. Results: To demonstrate the acceptability and effectiveness of our method, we apply the ISP method to several biological models discussed in prior literature. The results of our computational experiments reveal that the ISP method is effective both in terms of the speed and accuracy of the expansion, and the accuracy of the solution. This method also provides a better understanding of the state-space of the system in terms of blueprint patterns. Conclusions: The ISP is the de-novo method which addresses both accuracy and performance problems for CME solutions. It systematically expands the projection space based on predefined inputs. This ensures accuracy in the approximation and an exact analytical solution for the time of interest. The ISP was more effective both in predicting the behavior of the state-space of the system and in performance management, which is a vital step towards modeling large biochemical systems

Modulation of the Mitogen Activated Protein Kinase Pathway Spatiotemporal Signalling Components: Influence on Pathway Activation Behaviour Using an Agent Based Model

The subtleties of how the Mitogen Activated Protein Kinase works (MAPK) biochemical signalling pathway works, its emergent oscillatory behaviour and sensitivity is explained though an analysis of a computational agent based model that takes into account the distribution of the MAPK cascade components into multiple compartment

Mathematical modelling of shear stress signalling in endothelial cells.

In recent years it has become clear that cell signalling pathways are not simple linear chains of events as was once thought but frequently diverge, converge and employ positive and negative feedback. As a result signals show a complex pattern of spatial and temporal activity that is difficult to explain despite a wealth of experimental data. Systems Biology attempts to predict and understand the behaviour of complex systems by integrating information from diverse sources and principles drawn from a large number of different scientific disciplines. The signalling pathways regulating endothelial responses to shear stress have been extensively studied, since perturbed fluid flow contributes significantly to the development of heart disease. Shear stress activates many signals in endothelial cells, from ion influxes to protein phosphorylation and gene expression, and induces changes in endothelial morphology. Here a modelling and Systems Biology approach was taken to investigate and understand better the endothelial signal transduction networks that convert fluid flow stimulation into biochemical signals. A static signal transduction network was built from integrin cell surface receptors to activation of the tyrosine kinases focal adhesion kinase (FAK) and Src. Parameters for each reaction in this network were collected from the literature or, when necessary, estimated. To model how fluid flow initiates signalling in this network, the shear stress-induced calcium influx and the viscoelastic response of transmembrane receptors such as integrins to mechanical force were examined by means of mathematical modelling, using ordinary differential equations. These effects were used as primary activators of the shear stress response in endothelial cells, allowing quantitative analysis of the intracellular signal transduction flow which propagates from integrin to paxillin, FAK and Src activation. The magnitude and dependencies of each influence were examined individually and in conjunction with each other. The model was used to investigate the role and dynamic regulation of previously unstudied molecules in the network and the simulated results were compared against experimental data in order to validate hypotheses and increase our understanding of the molecular processes underlying the shear stress response

Photophysics and light induced photobiology of antiviral and antitumor agents, hypericin and hypocrellin

The dissertation mainly focuses on the excited-state photophysics and the light-induced biological activity of hypericin and its analogs.;Femtosecond laser technology has provided the opportunity to investigate the rapid dynamics of these molecules. Fluorescence upconversion measurements, which monitor only emission from the fluorescent singlet state, demonstrate that hexamethoxy hypericin, which possesses no labile protons, has an instantaneous rise time for its transient response. On the other hand, hypericin shows a clear 10-ps rise time. This confirms excited-state H-atom transfer as the primary photophysical process in hypericin.;Femtosecond transient absorption spectroscopy technique is used to determine if excited state H-atom transfer is concerted. Previous studies using human serum albumin (HSA) and hypericin suggested that excited state H-atom transfer is concerted, but the results from the hypericin in reverse micelles show no evidence for a concerted hydrogen atom transfer mechanism. We are, however, unable to conclude if only one hydrogen atom is transferred or if two are transferred in a stepwise fashion.;By means of time-resolved infrared spectroscopy, ab initio quantum mechanical calculations, and synthetic organic chemistry, a region in the infrared spectrum, between 1400 and 1500 cm-1, of triplet hypericin has been found corresponding to translocation of the hydrogen atom between the enol and the keto oxygens, O &cdots; H &cdots; O. This result is discussed in the context of the photophysics of hypericin and of eventual measurements to observe directly the excited-state H-atom transfer.;Light-induced antiviral activity of hypericin and hypocrellin is compared in normoxic and hypoxic conditions. Although both molecules require oxygen to show full virucidal effects, hypericin is still effective at low oxygen level where hypocrellin is not. Since the singlet oxygen yield of hypericin is about half of that of hypocrellin, this result cannot be explained by a traditional Type II mechanism. We propose that the ejected proton upon illumination might enhance the activity of activated oxygen species.;A series of hypericin analogs were found to differ in their cytotoxic activity induced by ambient light levels. These analogs vary in their ability to partition into cells, to generate singlet oxygen as well as in other photophysical properties. The percent distribution of hypericin and its analogs in cells are measured using a steady state absorption technique. We attempt to find a relationship between those results and the exact localization of the drug at subcellular level

Flower abscission and fruit set on table grapes (Vitis vinifera L.): unraveling physiological and molecular mechanisms

Doutoramento em Engenharia Agronómica - Instituto Superior de Agronomia - ULDespite the importance of grapevine (Vitis vinifera L.) as one of the most cultivated species, the molecular events occurring during the critical period of fruit set, are far from elucidated. Aiming at providing a new insight on flower-to-fruit transition and flower abscission regulation, transcriptomic (RNA-Seq) and metabolomic analyzes were performed in the inflorescences and vine physiological alterations were investigated. Regarding flower-to-fruit transition regulation the results showed involvement of nutrient transport regulation and alterations on carbohydrates, secondary and hormone metabolism. In particular, induction of indole-3-acetic acid accumulation and activation of ethylene and sugar signaling were hypothesized to induce bioactive gibberellins biosynthesis, stimulating cell division within inflorescences. Assays with gibberellic acid (GAc) spraying and reduction of light interception during bloom allowed to promote flower abscission and suggested that growth regulator application and C-starvation resulted in distinct effects on inflorescence metabolism. GAc response involved stimulation of photosynthetic and respiratory machinery, nucleotide biosynthesis and carbon metabolism. Conversely, shading repressed photosynthesis, induced carbon/nitrogen imbalance and comprehensive alterations on hormone-related pathways, resulting in repression of cell division and induction of senescence. Candidates as common pathways leading to abscission were putrescine catabolism regulation, auxin biosynthesis induction, gibberellin biosynthesis repression and ROS signaling/detoxification although often through changes on specific transcripts and metabolites levels. Aiming at optimizing thinning methods, mandatory on table grapes production for guarantee bunch quality, GAc spray and shading during bloom were tested in seedless and seeded cultivars growing under field and greenhouse conditions. 'Thompson Seedless' showed to be sensitive to both thinning methods resulting in increased flower drop and reduced bunch compactness, but only GAc spray enhanced berry quality. Both treatments induced flower abscission in 'Black Magic' growing in late cycle on greenhouse production system, whereas during early cycle, only shade enhanced flower drop, bunch aspect and berry quality, resulting in an effective thinning metho

Hydrodynamics

The phenomena related to the flow of fluids are generally complex, and difficult to quantify. New approaches - considering points of view still not explored - may introduce useful tools in the study of Hydrodynamics and the related transport phenomena. The details of the flows and the properties of the fluids must be considered on a very small scale perspective. Consequently, new concepts and tools are generated to better describe the fluids and their properties. This volume presents conclusions about advanced topics of calculated and observed flows. It contains eighteen chapters, organized in five sections: 1) Mathematical Models in Fluid Mechanics, 2) Biological Applications and Biohydrodynamics, 3) Detailed Experimental Analyses of Fluids and Flows, 4) Radiation-, Electro-, Magnetohydrodynamics, and Magnetorheology, 5) Special Topics on Simulations and Experimental Data. These chapters present new points of view about methods and tools used in Hydrodynamics

Integration of thrombin-binding aptamers in point-of-care devices for continuous monitoring of thrombin in plasma

La thrombine est l'enzyme principale dans le processus d'hémostase. Les dérèglements de la concentration de thrombine clinique prédisposent les patients à des complications hémorragiques ou thromboemboliques. Le suivi en temps réel de la thrombine dans le sang est donc nécessaire pour améliorer le traitement de patients en état critique. Les aptamères, qui sont de courts nucléotides monobrins semblent constituer des candidats prometteurs pour la reconnaissance moléculaire dans les biocapteurs. L'objectif de ces travaux est l'étude de différentes solutions d'intégration des aptamères dans des dispositifs de diagnostic de type "point of care" pour le suivi en continu de la thrombine dans le plasma. La cinétique d'interaction des aptamères avec la thrombine et leur spécificité vis-à-vis de la prothrombine et des inhibiteurs de la thrombine ont été étudiés par résonance par plasmons de surface. Ces travaux ont démontré la faible spécificité de l'aptamère HD1 vis-à-vis de la thrombine, et la présence d'interactions non-spécifiques avec la prothrombine, les inhibiteurs naturels de la thrombine et l'albumine. Inversement, nous avons observé une bonne affinité de l'aptamère HD22 avec la même liste de cible. Parallèlement, nous avons évalué des stratégies d'intégration d'aptamères dans des dispositifs d'analyse. Le principe de reconnaissance a ensuite été validé et la possibilité de détecter la thrombine dans des gammes de concentration de 5 à 500nM a été démontrée. Enfin, afin d'augmenter la spécificité de la détection de la thrombine, nous avons proposé une nouvelle approche basée sur l'ingénierie de structures dimères interconnectant HD1 et HD22.Thrombin is the central enzyme in the process of hemostasis. Normally, in vivo concentration of thrombin is rigorously regulated; however, clinically impaired or unregulated thrombin generation predisposes patients either to hemorrhagic or thromboembolic complications. Monitoring thrombin in real-time is therefore needed to enable rapid and accurate determination of drug administration strategy for patients under vital threat. Aptamers, short single-stranded oligonucleotide ligands represent promising candidates as biorecognition elements for new-generation biosensors. The aim of this PhD work therefore is to investigate different solutions for the integration of thrombin-binding aptamers in point-of-care devices for continuous monitoring of thrombin in plasma. The kinetics of aptamer interaction with thrombin and specificity towards prothrombin and thrombin - inhibitor complexes was rigorously investigated using Surface Plasmon Resonance. These experiments unveiled the complex character of interaction of the HD1 with thrombin, confirming nonspecific interactions with prothrombin, natural inhibitors of thrombin, serum albumin whereas another 29-bp aptamer HD22 proved to be highly affine and specific towards thrombin. On the other hand we explored aptamer integration options. We validated the principle and at the same managed to detect different concentrations of thrombin (5-500 nM). We finally proposed a novel approach to increase sensitivity and specificity for thrombin detection based on the engineering of aptadimer structures bearing aptamers HD1and HD22 interconnected with a nucleic acid spacer

Sloppiness, Modeling, and Evolution in Biochemical Networks

The wonderful complexity of livings cells cannot be understood solely by studying one gene or protein at a time. Instead, we must consider their interactions and study the complex biochemical networks they function in. Quantitative computational models are important tools for understanding the dynamics of such biochemical networks, and we begin in Chapter 2 by showing that the sensitivities of such models to parameter changes are generically `sloppy', with eigenvalues roughly evenly spaced over many decades. This sloppiness has practical consequences for the modeling process. In particular, we argue that if one's goal is to make experimentally testable predictions, sloppiness suggests that collectively fitting model parameters to system-level data will often be much more efficient that directly measuring them. In Chapter 3 we apply some of the lessons of sloppiness to a specific modeling project involving in vitro experiments on the activation of the heterotrimeric G protein transducin. We explore how well time-series activation experiments can constrain model parameters, and we show quantitatively that the T177A mutant of transducin exhibits a much slower rate of rhodopsin-mediated activation than the wild-type. All the preceding biochemical modeling work is performed using the SloppyCell modeling environment, and Chapter 4 briefly introduces SloppyCell and some of the analyses it implements. Additionally, the two appendices of this thesis contain preliminary user and developer documentation for SloppyCell. Modelers tweak network parameters with their computers, and nature tweaks such parameters through evolution. We study evolution in Chapter 5 using a version of Fisher's geometrical model with minimal pleiotropy, appropriate for the evolution of biochemical parameters. The model predicts a striking pattern of cusps in the distribution of fitness effects of fixed mutations, and using extreme value theory we show that the consequences of these cusps should be observable in feasible experiments. Finally, this thesis closes in Chapter 6 by briefly considering several topics: sloppiness in two non-biochemical models, two technical issues with building models, and the effect of sloppiness on evolution beyond the first fixed mutation

Parameter Estimation of Complex Systems from Sparse and Noisy Data

Mathematical modeling is a key component of various disciplines in science and engineering. A mathematical model which represents important behavior of a real system can be used as a substitute for the real process for many analysis and synthesis tasks. The performance of model based techniques, e.g. system analysis, computer simulation, controller design, sensor development, state filtering, product monitoring, and process optimization, is highly dependent on the quality of the model used. Therefore, it is very important to be able to develop an accurate model from available experimental data. Parameter estimation is usually formulated as an optimization problem where the parameter estimate is computed by minimizing the discrepancy between the model prediction and the experimental data. If a simple model and a large amount of data are available then the estimation problem is frequently well-posed and a small error in data fitting automatically results in an accurate model. However, this is not always the case. If the model is complex and only sparse and noisy data are available, then the estimation problem is often ill-conditioned and good data fitting does not ensure accurate model predictions. Many challenges that can often be neglected for estimation involving simple models need to be carefully considered for estimation problems involving complex models. To obtain a reliable and accurate estimate from sparse and noisy data, a set of techniques is developed by addressing the challenges encountered in estimation of complex models, including (1) model analysis and simplification which identifies the important sources of uncertainty and reduces the model complexity; (2) experimental design for collecting information-rich data by setting optimal experimental conditions; (3) regularization of estimation problem which solves the ill-conditioned large-scale optimization problem by reducing the number of parameters; (4) nonlinear estimation and filtering which fits the data by various estimation and filtering algorithms; (5) model verification by applying statistical hypothesis test to the prediction error. The developed methods are applied to different types of models ranging from models found in the process industries to biochemical networks, some of which are described by ordinary differential equations with dozens of state variables and more than a hundred parameters