This dissertation is concerned with the potential multistability of protein concentrations in the cell that can arise in biochemical networks. That is, situations where one, or a family of, proteins may sit at one of two or more different steady state concentrations in otherwise identical cells, and in spite of them being in the same environment. Models of multisite protein phosphorylation have shown that this mechanism is able to exhibit unlimited multistability. Nevertheless, these models have not considered enzyme docking, the binding of the enzymes to one or more substrate docking sites, which are separate from the motif that is chemically modified. Enzyme docking is, however, increasingly being recognised as a method to achieve specificity in protein phosphorylation and dephosphorylation cycles. Most models in the literature for these systems are deterministic i.e. based on Ordinary Differential Equations, despite the fact that these are accurate only in the limit of large molecule numbers. For small molecule numbers, a discrete probabilistic, stochastic, approach is more suitable. However, when compared to the tools available in the deterministic framework, the tools available for stochastic analysis offer inadequate visualisation and intuition. We firstly try to bridge that gap, by developing three tools: a) a discrete `nullclines' construct applicable to stochastic systems - an analogue to the ODE nullcines, b) a stochastic tool based on a Weakly Chained Diagonally Dominant M-matrix formulation of the Chemical Master Equation and c) an algorithm that is able to construct non-reversible Markov chains with desired stationary probability distributions. We subsequently prove that, for multisite protein phosphorylation and similar models, in the deterministic domain, enzyme docking and the consequent substrate enzyme-sequestration must inevitably limit the extent of multistability, ultimately to one steady state. In contrast, bimodality can be obtained in the stochastic domain even in situations where bistability is not possible for large molecule numbers. We finally extend our results to cases where we have an autophosphorylating kinase, as for example is the case with $Ca^{2+}$/calmodulin-dependent protein kinase II (CaMKII), a key enzyme in synaptic plasticity.

"Laser flash spectroscopy of photosystem II" Photosystem II (PS II) of plants and cyanobacteria oxidizes water in a light-powered reaction. Thereby, this protein is the ultimate source of the atmospheric oxygen. The capacity to oxidize water is owed to two properties of PS II: (i) The midpoint potential of the oxidizing chlorophyll moiety is increased by 0.6 V compared to photosystem I or photochemical reaction centers of anoxygenic bacteria, and (ii) the energy requirements of the four steps needed for the tetravalent oxidation of water are adapted to the energy of red light quanta. This thesis deals with two particular aspects, namely: 1. The coupling of the electron transfer from tyrosine Z (YZ) to the primary donor (P680+) to proton transfer, and an inquiry on the role of a positive charge on YZox (plus base cluster) in increasing the oxidizing potential at the catalytic site. 2. The localization of the electron hole, P680+, among the excitonically coupled four inner chlorophyll a molecules, and an estimation of the midpoint potential differences between them. Electron-proton-coupling by YZ This study was carried out with PS II core complexes from spinach or pea with a deactivated (removed) manganese cluster. The reduction of P680+ was investigated as a function of pH by detecting the laser flash induced absorption changes with nanosecond resolution. Two kinetic components were found with different pH-dependence and activation energies. The alteration of kinetic parameters by H/D isotope substitutions or by addition of divalent cations implied two different types of YZ-oxidation: At acidic pH the electron transfer was coupled with proton transfer, whereas in the alkaline region it was more rapid and no longer controlled by proton transfer. The conversion between both mechanisms occured at pH 7.4. This value corresponds either to the apparent pK of YZ itself (i.e. of the hydroxy group of the phenol ring) or to the pK of an acid-base-cluster, which includes YZ. Independent measurements of pH-transients by following the absorption changes of hydrophilic proton indicators corroborated this notion. The data were interpreted as indicating that the phenolic proton of YZ was released into the medium at acidic, but not at alkaline pH. The electron transfer and proton release characteristics of intact, oxygen-evolving PS II resembled those in deactivated samples kept at alkaline pH. We concluded that the electron transfer from YZ to P680+ in the native system was not coupled with proton transfer into the bulk. This has shed doubt on a popular hypothesis on the role of YZ as 'hydrogen abstractor' from bound water. On the other hand, the energetic constraints of water oxidation could be eased by the positive upcharging during oxidation of YZox plus its base cluster. On the localization of the electron hole of P680+ Photooxidation of PS II oxidizes the set of four innermost chlorophyll a molecules giving rise to the only spectroscopically defined species P680+. The deconvolution of difference spectra into bands of pigments is ambiguous. By using photoselective excitation of antennae, i.e. chl a molecules with site specific energies at the long wavelength border of the mean Qy-band, and by polarized detection, it was possible to tag P680+QA-/P680QA and 3P680/P680 difference spectra with a further parameter, the (wavelength-dependent) anisotropy r. Results obtained at liquid nitrogen temperature (77 K) can be clearly interpreted in terms of two chl a monomer bands. The two main components of the P680+QA-/P680QA difference spectrum were marked by two distinct values of the anisotropy and could be interpreted in a straightforward manner: the bleaching of a band at 675 nm belonging to the charged species (chl a+) and an electrochromic blue-shift of a nearby chl a from 684 to 682 nm. The main bleaching band of the 3P680/P680 spectrum (at 77 K) can be apparently attributed to a third (or several) chl a component(s). The analysis of the P680+QA-/P680QA spectrum at cryogenic temperature is compatible with monomeric chl a bands. On the other hand, one could assume a system of excitonically coupled core pigments, as it was recently introduced in the literature on the basis of energy transfer studies ('multimer model'). However, in view of the clear indications for an electrochromic band shift and the location of the bleaching band, which absorbs in a wavelength region of monomeric chl a, one assumption of the 'multimer model' should be questioned. Presumably, the excitonic couplings are rather weak, in particular between each of the two central chl a-molecules (PA/PB) and its respective accessory chl a (BA/BB), because of (i) the distances and (ii) different site energies of the monomeric chromophores. At room temperature, the absorption difference and anisotropy spectra of P680+QA-/P680QA were clearly altered. The anisotropy data indicated that the changes could no longer exclusively be ascribed to thermal broadening of individual bands. The localization of the positive charge on one pigment, analogous to the situation at 77 K, was now unlikely. Hence, the midpoint potential differences between the inner four chlorophyll a molecules were small and were estimated as approximately 15 meV.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis is aimed at creating a pipeline for analyzing DNA methylation epigenetics data and creating a data model structured well enough to store the analysis results of the pipeline. In addition to storing the results, the model is also designed to hold information which will help researchers to decipher a meaningful epigenetics sense from the results made available. Current major epigenetics resources such as PubMeth, MethyCancer, MethDB and NCBI’s Epigenomics database fail to provide holistic view of epigenetics. They provide datasets produced from different analysis techniques which raises an important issue of data integration. The resources also fail to include numerous factors defining the epigenetic nature of a gene. Some of the resources are also struggling to keep the data stored in their databases up-to-date. This has diminished their validity and coverage of epigenetics data. In this thesis we have tackled a major branch of epigenetics: DNA methylation. As a case study to prove the effectiveness of our pipeline, we have used stage-wise DNA methylation and expression raw data for Lung adenocarcinoma (LUAD) from TCGA data repository. The pipeline helped us to identify progressive methylation patterns across different stages of LUAD. It also identified some key targets which have a potential for being a drug target. Along with the results from methylation data analysis pipeline we combined data from various online data reserves such as KEGG database, GO database, UCSC database and BioGRID database which helped us to overcome the shortcomings of existing data collections and present a resource as complete solution for studying DNA methylation epigenetics data.

In forensic autopsies, accurately estimating the postmortem interval (PMI) is crucial. Traditional methods, relying on physical parameters and police data, often lack precision, particularly after approximately two days have passed since the person's death. New methods are increasingly focusing on analyzing postmortem metabolomics in biological systems, acting as a 'fingerprint' of ongoing processes influenced by internal and external molecules. By carefully analyzing these metabolomic profiles, which span a diverse range of information from events preceding death to postmortem changes, there is potential to provide more accurate estimates of the PMI. The limitation of available real human data has hindered comprehensive investigation until recently. Large-scale metabolomic data collected by the National Board of Forensic Medicine (RMV, Rättsmedicinalverket) presents a unique opportunity for predictive analysis in forensic science, enabling innovative approaches for improving  PMI estimation. However, the metabolomic data appears to be large, complex, and potentially nonlinear, making it difficult to interpret. This underscores the importance of effectively employing machine learning algorithms to manage metabolomic data for the purpose of PMI predictions, the primary focus of this project.  In this study, a dataset consisting of 4,866 human samples and 2,304 metabolites from the RMV was utilized to train a model capable of predicting the PMI. Random Forest (RF) and Artificial Neural Network (ANN) models were then employed for PMI prediction. Furthermore, feature selection and incorporating sex and age into the model were explored to improve the neural network's performance.  This master's thesis shows that ANN consistently outperforms RF in PMI estimation, achieving an R2 of 0.68 and an MAE of 1.51 days compared to RF's R2 of 0.43 and MAE of 2.0 days across the entire PMI-interval. Additionally, feature selection indicates that only 35% of total metabolites are necessary for comparable results with maintained predictive accuracy. Furthermore, Principal Component Analysis (PCA) reveals that these informative metabolites are primarily located within a specific cluster on the first and second principal components (PC), suggesting a need for further research into the biological context of these metabolites.  In conclusion, the dataset has proven valuable for predicting PMI. This indicates significant potential for employing machine learning models in PMI estimation, thereby assisting forensic pathologists in determining the time of death. Notably, the model shows promise in surpassing current methods and filling crucial gaps in the field, representing an important step towards achieving accurate PMI estimations in forensic practice. This project suggests that machine learning will play a central role in assisting with determining time since death in the future.