Pharmacology ... Laboratory manual: notes on comparative physiological and biochemical processes in pharmacology. [Spring term, 1961]

Mode of access: Internet.

Microsensors technology used to measure productivity and other biochemical processes in the upper regions of aquatic sediments

Carbonate sediments are dynamic three-dimensional environments where the surface layers are constantly moving and mixing due to the energy of the water column. It is also an environment of dynamic biological, chemical and physical interaction and modification. The biological community can actively influence changes to sediment characteristics and associated biochemistry. Bioturbation resulting from macrofaunal activity disrupts sediment structure and biochemical arrangements and reduces the critical shear forces required to move sediment particles, adding to the dynamic and complex physical and biogeochemical nature of the sediment. Laboratory studies using both planner optodes and glass needle microsensors were used to measure abiotic sediment characteristics such as the depth distribution and concentrations of PAR. The biochemical nature of coral reef sediment were also investigated, specifically the quantification and the distribution of dissolved oxygen within coarse and fine-grained sediments under regimes of light and darkness. Results highlighted the significant contribution microalgal productivity and bioturbation has on distribution of dissolved oxygen in the upper sediment layers. On the reef flat a shallow water lander system was employed to measure concentrations of O2, pH, S, Ca and temperature over periods of 24 to 48 hours in coarse and fine-grained sediments. Similarities between laboratory and in situ results where evident, however the in situ environment was more dynamic and the distribution and concentrations of dissolved oxygen were more complex and correlated to irradiance, temperature and biological activity. Microsensor technology provides us with the opportunity to study, at very high resolutions, the upper irradiated; photosynthetically active regions of aquatic sediments along with anoxic processes deeper in sub-euphotic regions of the sediments.

Primed, constant infusion with [H-2(3)]serine allows in vivo kinetic measurement of serine turnover, homocysteine remethylation, and transsulfuration processes in human one-carbon metabolism

Background: One-carbon metabolism involves both mitochondrial and cytosolic forms of folate-dependent enzymes in mammalian cells, but few in vivo data exist to characterize the biochemical processes involved.

Objective: We conducted a stable-isotopic investigation to determine the fates of exogenous serine and serine-derived one carbon units in homocysteine remethylation in hepatic and whole-body metabolism.

Design: A healthy man aged 23 y was administered [2,3,3 H-2(3)]serine and [5,5,5-H-2(3)]leucine by intravenous primed, constant infusion. Serial plasma samples were analyzed to determine the isotopic enrichment of free glycine, serine, leucine, methionine, and cystathionine. VLDL apolipoprotein B-100 served as an index of liver free amino acid labeling.

Results: [H-2(1)]Methionine and [H-2(2)]methionine were labeled through homocysteine remethylation. We propose that [H-2(2)]methionine occurs by remethylation with [H-2(2)]methyl groups (as 5-methyltetrahydrofolate) formed only from cytosolic processing of [H-2(3)]serine, whereas [H-2(1)]methionine is formed with labeled one-carbon units from mitochondrial oxidation of C-3 serine to [H-2(1)]formate to yield cytosolic [H-2(1)]methyl groups. The labeling pattern of cystathionine formed from homocysteine and labeled serine suggests that cystathionine is derived mainly from a serine pool different from that used in apolipoprotein B-100 synthesis.

Conclusions: The appearance of both [H-2(1)]- and [H-2(2)]methionine forms indicates that both cytosolic and mitochondrial metabolism of exogenous serine generates carbon units in vivo for methyl group production and homocysteine remethylation. This study also showed the utility of serine infusion and indicated functional roles of cytosolic and mitochondrial compartments in one-carbon metabolism.

Biochemical effects of cadmium, salinity and temperature on the catabolism of proteins and purines in oreochromis mossambicus (Peters)

In the present investigation, three important stressors: cadmium ion (Cd++), salinity and temperature were selected to study their effects on protein and purine catabolism of O. mossambicus. Cadmium (Cd) is a biologically nonessential metal that can be toxic to aquatic animals. Cadmium is a trace element which is a common constituent of industrial effluents. It is a non-nutrient metal and toxic to fish even at low concentrations. Cadmium ions accumulate in sensitive organs like gills, liver, and kidney of fish in an unregulated manner . Thus; the toxic effects of cadmium are related to changes in natural physiological and biochemical processes in organism. The mechanics of osmoregulation (i.e. total solute and water regulation) are reasonably well understood (Evans, 1984, 1993), and most researchers agree that salinities that differ from the internal osmotic concentration of the fish must impose energetic regulatory costs for active ion transport. There is limited information on protein and purine catabolism of euryhaline fish during salinity adaptation. Within a range of non-lethal temperatures, fishes are generally able to cope with gradual temperature changes that are common in natural systems. However, rapid increases or decreases in ambient temperature may result in sub lethal physiological and behavioral responses. The catabolic pathways of proteins and purines are important biochemical processes. The results obtained signifies that O. mossambicus when exposed to different levels of cadmium ion, salinity and temperature show great variation in the catabolism of proteins and purines. The organism is trying to attain homeostasis in the presence of stressors by increasing or decreasing the activity of certain enzymes. The present study revealed that the protein and purine catabolism in O. mossambicus is sensitive to environmental stressors.

The underlying pathway structure of biochemical reaction networks

Bioinformatics is yielding extensive, and in some cases complete, genetic and biochemical information about individual cell types and cellular processes, providing the composition of living cells and the molecular structure of its components. These components together perform integrated cellular functions that now need to be analyzed. In particular, the functional definition of biochemical pathways and their role in the context of the whole cell is lacking. In this study, we show how the mass balance constraints that govern the function of biochemical reaction networks lead to the translation of this problem into the realm of linear algebra. The functional capabilities of biochemical reaction networks, and thus the choices that cells can make, are reflected in the null space of their stoichiometric matrix. The null space is spanned by a finite number of basis vectors. We present an algorithm for the synthesis of a set of basis vectors for spanning the null space of the stoichiometric matrix, in which these basis vectors represent the underlying biochemical pathways that are fundamental to the corresponding biochemical reaction network. In other words, all possible flux distributions achievable by a defined set of biochemical reactions are represented by a linear combination of these basis pathways. These basis pathways thus represent the underlying pathway structure of the defined biochemical reaction network. This development is significant from a fundamental and conceptual standpoint because it yields a holistic definition of biochemical pathways in contrast to definitions that have arisen from the historical development of our knowledge about biochemical processes. Additionally, this new conceptual framework will be important in defining, characterizing, and studying biochemical pathways from the rapidly growing information on cellular function.

I. Quantal effects in biochemical cooperativity and a proposed mechanism for the differentiation of calcium signaling in synaptic plasticity. II. Evolutionary algorithms for the optimization of methods in computational chemistry

In Part 1 of this thesis, we propose that biochemical cooperativity is a fundamentally non-ideal process. We show quantal effects underlying biochemical cooperativity and highlight apparent ergodic breaking at small volumes. The apparent ergodic breaking manifests itself in a divergence of deterministic and stochastic models. We further predict that this divergence of deterministic and stochastic results is a failure of the deterministic methods rather than an issue of stochastic simulations.

Ergodic breaking at small volumes may allow these molecular complexes to function as switches to a greater degree than has previously been shown. We propose that this ergodic breaking is a phenomenon that the synapse might exploit to differentiate Ca$^{2+}$ signaling that would lead to either the strengthening or weakening of a synapse. Techniques such as lattice-based statistics and rule-based modeling are tools that allow us to directly confront this non-ideality. A natural next step to understanding the chemical physics that underlies these processes is to consider \textit{in silico} specifically atomistic simulation methods that might augment our modeling efforts.

In the second part of this thesis, we use evolutionary algorithms to optimize \textit{in silico} methods that might be used to describe biochemical processes at the subcellular and molecular levels. While we have applied evolutionary algorithms to several methods, this thesis will focus on the optimization of charge equilibration methods. Accurate charges are essential to understanding the electrostatic interactions that are involved in ligand binding, as frequently discussed in the first part of this thesis.

SELECTED CHEMOAUTOTROPHIC PROCESSES IN WASTEWATER TREATMENT: LOW TEMPERATURE NITRIFICATION INHIBITION BY AN AZO DYE AND BIOFILTRATION FOR ODOR CONTROL OF HYDROGEN SULFIDE

Biochemical processes by chemoautotrophs such as nitrifiers and sulfide and iron oxidizers are used extensively in wastewater treatment. The research described in this dissertation involved the study of two selected biological processes utilized in wastewater treatment mediated by chemoautotrophic bacteria: nitrification (biological removal of ammonia and nitrogen) and hydrogen sulfide (H2S) removal from odorous air using biofiltration. A municipal wastewater treatment plant (WWTP) receiving industrial dyeing discharge containing the azo dye, acid black 1 (AB1) failed to meet discharge limits, especially during the winter. Dyeing discharge mixed with domestic sewage was fed to sequencing batch reactors at 22oC and 7oC. Complete nitrification failure occurred at 7oC with more rapid nitrification failure as the dye concentration increased; slight nitrification inhibition occurred at 22oC. Dye-bearing wastewater reduced chemical oxygen demand (COD) removal at 7oC and 22oC, increased i effluent total suspended solids (TSS) at 7oC, and reduced activated sludge quality at 7oC. Decreasing AB1 loading resulted in partial nitrification recovery. Eliminating the dye-bearing discharge to the full-scale WWTP led to improved performance bringing the WWTP into regulatory compliance. BiofilterTM, a dynamic model describing the biofiltration processes for hydrogen sulfide removal from odorous air emissions, was calibrated and validated using pilot- and full-scale biofilter data. In addition, the model predicted the trend of the measured data under field conditions of changing input concentration and low effluent concentrations. The model demonstrated that increasing gas residence time and temperature and decreasing influent concentration decreases effluent concentration. Model simulations also showed that longer residence times are required to treat loading spikes. BiofilterTM was also used in the preliminary design of a full-scale biofilter for the removal of H2S from odorous air. Model simulations illustrated that plots of effluent concentration as a function of residence time or bed area were useful to characterize and design biofilters. Also, decreasing temperature significantly increased the effluent concentration. Model simulations showed that at a given temperature, a biofilter cannot reduce H2S emissions below a minimum value, no matter how large the biofilter.

Probability distributed time delays: integrating spatial effects into temporal models

Background In order to provide insights into the complex biochemical processes inside a cell, modelling approaches must find a balance between achieving an adequate representation of the physical phenomena and keeping the associated computational cost within reasonable limits. This issue is particularly stressed when spatial inhomogeneities have a significant effect on system's behaviour. In such cases, a spatially-resolved stochastic method can better portray the biological reality, but the corresponding computer simulations can in turn be prohibitively expensive. Results We present a method that incorporates spatial information by means of tailored, probability distributed time-delays. These distributions can be directly obtained by single in silico or a suitable set of in vitro experiments and are subsequently fed into a delay stochastic simulation algorithm (DSSA), achieving a good compromise between computational costs and a much more accurate representation of spatial processes such as molecular diffusion and translocation between cell compartments. Additionally, we present a novel alternative approach based on delay differential equations (DDE) that can be used in scenarios of high molecular concentrations and low noise propagation. Conclusions Our proposed methodologies accurately capture and incorporate certain spatial processes into temporal stochastic and deterministic simulations, increasing their accuracy at low computational costs. This is of particular importance given that time spans of cellular processes are generally larger (possibly by several orders of magnitude) than those achievable by current spatially-resolved stochastic simulators. Hence, our methodology allows users to explore cellular scenarios under the effects of diffusion and stochasticity in time spans that were, until now, simply unfeasible. Our methodologies are supported by theoretical considerations on the different modelling regimes, i.e. spatial vs. delay-temporal, as indicated by the corresponding Master Equations and presented elsewhere.

Modeling intrinsic noise and delays in chemical kinetics of coupled autoregulated oscillating cells

Delays are an important feature in temporal models of genetic regulation due to slow biochemical processes, such as transcription and translation. In this paper, we show how to model intrinsic noise effects in a delayed setting by either using a delay stochastic simulation algorithm (DSSA) or, for larger and more complex systems, a generalized Binomial τ-leap method (Bτ-DSSA). As a particular application, we apply these ideas to modeling somite segmentation in zebra fish across a number of cells in which two linked oscillatory genes (her1 and her7) are synchronized via Notch signaling between the cells.

Hydrothermal technologies for the production of fuels and chemicals from biomass

Biomass and non-food crop residues are seen as relatively low cost and abundant renewable sources capable of making a large contribution to the world’s future energy and chemicals supply. Signifi cant quantities of ethanol are currently produced from biomass via biochemical processes, but thermochemical conversion processes offer greater potential to utilize the entire biomass source to produce a range of products. This chapter will review thermochemical gasifi cation and pyrolysis methods with a focus on hydrothermal liquefaction processes. Hydrothermal liquefaction is the most energetically advantageous thermochemical biomass conversion process. If the target is to produce sustainable liquid fuels and chemicals and reduce the impact of global warming as a result of carbon dioxide, nitrous oxide, and methane emissions (i.e., protect the natural environment), the use of “green” solvents, biocatalysts and heterogeneous catalysts must be the main R&D initiatives. As the biocrude produced from hydrothermal liquefaction is a complex mixture which is relatively viscous, corrosive, and unstable to oxidation (due to the presence of water and oxygenated compounds), additional upgrading processes are required to produce suitable biofuels and chemicals.

A novel bioreactor for ligament tissue engineering

Bioreactors are defined as devices in which biological and/or biochemical processes develop under closely monitored and tightly controlled environmental and operating conditions (e.g. pH, temperature, mechanical conditions, nutrient supply and waste removal). In functional tissue engineering of musculoskeletal tissues, a bioreactor capable of controlling dynamic loading plays a determinant role. It has been shown that mechanical stretching promotes the expression of type I and III collagens, fibronectin, tenascin-C in cultured ligament fibroblasts (J.C.-H. Goh et al., Tissue Eng. 9 (2003), S31) and that human bone marrow mesenchymal stem cells (hBMMSC) – even in the absence of biochemical regulators – could be induced to differentiate into ligament-like fibroblast by the application of physiologically relevant cyclic strains (G. Vunjak-Novakovic et al., Ann. Rev. Biomed. Eng. 6 (2004), 131; H.A. Awad et al., Tissue Eng. 5 (1999), 267; R.G. Young et al., J. Orthop. Res. 16 (1998), 406). Different bioreactors are commercially available but they are too generic to be used for a given tissue, each tissue showing specific mechanical loading properties. In the case of ligament tissue engineering, the design of a bioreactor is still an open question. Our group proposes a bioreactor allowing cyclic traction–torsion on a scaffold seeded with stem cells.

A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php