Use of Interactive Simulations in Fundamentals of Biochemistry, a LibreText Online Educational Resource, to Promote Understanding of Dynamic Reactions

Biology is perhaps the most complex of the sciences, given the incredible variety of chemical species that are interconnected in spatial and temporal pathways that are daunting to understand. Their interconnections lead to emergent properties such as memory, consciousness, and recognition of self and non-self. To understand how these interconnected reactions lead to cellular life characterized by activation, inhibition, regulation, homeostasis, and adaptation, computational analyses and simulations are essential, a fact recognized by the biological communities. At the same time, students struggle to understand and apply binding and kinetic analyses for the simplest reactions such as the irreversible first-order conversion of a single reactant to a product. This likely results from cognitive difficulties in combining structural, chemical, mathematical, and textual descriptions of binding and catalytic reactions. To help students better understand dynamic reactions and their analyses, we have introduced two kinds of interactive graphs and simulations into the online educational resource, Fundamentals of Biochemistry, a multivolume biochemistry textbook that is part of the LibreText collection. One type is available for simple binding and kinetic reactions. The other displays progress curves (concentrations vs time) for both simple reactions and more complex metabolic and signal transduction pathways, including those available through databases using systems biology markup language (SBML) files. Users can move sliders to change dissociation and kinetic constants as well as initial concentrations and see instantaneous changes in the graphs. They can also export data into a spreadsheet for further processing, such as producing derivative Lineweaver-Burk and traditional Michaelis-Menten graphs of initial velocity (v0) vs substrate concentration.Comment: 17 pages, 2 tables, 8 figures. Submitted to Biochemistry and Molecular Biology Education. Funding: MiniSidewinder: NIH/NIGMS (Grant R01-GM123032-04) LibreText: Department of Education Open Textbook Pilot Project, the UC Davis Office of the Provost, the UC Davis Library, the California State University Affordable Learning Solutions Program, and Merlo

Contributions to an improved phenytoin monitoring and dosing in hospitalized patients

Phenytoin (PHT) is one of the mostly used and well established anticonvulsants for the treatment of epilepsy and a standard in the antiepileptic prophylaxis in adults with severe traumatic brain injuries before and after neurosurgical intervention. Its therapeutic use is challenging as PHT has a narrow therapeutic range and shows non-linear kinetics. It is extensively metabolized by a variety of CYP enzymes. PHT shows 85-95% binding to plasma proteins mostly albumin. This renders PHT also an important drug interaction candidate. Therefore, therapeutic drug monitoring is often required. A rational timing for good interpretation of the lab data translated in optimal individual dosing are necessary. Therapeutic guidance especially in teaching hospitals are needed and have to be implemented. Bayesian Forecasting (BF) versus conventional dosing (CD): a retrospective, long-term, single centre analysis In the hospital, medication management for effective antiepileptic therapy with PHT often needs rapid IV loading and subsequent dose adjustment according to TDM. To investigate PHT performance in reaching therapeutic target serum concentration, a BF regimen was compared to CD, according to the official summary of product characteristics. In a Swiss acute care teaching hospital (Kantonsspital Aarau), a retrospective, single centre, and long-term analysis was assessed by using all PHT serum tests from the central lab from 1997 to 2007. The BF regimen consisted of a guided, body weight-adapted rapid IV PHT loading over five days with pre-defined TDM time points. The CD was applied without written guidance. Assuming non-normally distributed data, non-parametric statistical methods were used. A total of 6’120 PHT serum levels (2’819 BF and 3’301 CD) from 2’589 patients (869 BF and 1’720 CD) were evaluated and compared. 63.6% of the PHT serum levels from the BF group were within the therapeutic range versus only 34.0% in the CD group (p<0.0001). The mean BF serum level was 52.0 ± 22.1 µmol/L (within target range), whereas the mean serum level of the CD was 39.8 ± 28.2 µmol/L (sub-target range). In the BF group, men had small but significantly lower PHT serum levels compared to women (p<0.0001). The CD group showed no significant gender difference (p=0.187). A comparative sub-analysis of age-related groups (children, adolescents, adults, seniors, and elderly) showed significant lower target levels (p<0.0001) for each group in the CD group, compared to BF. Comparing the two groups, BF showed significantly better performance in reaching therapeutic PHT serum levels. Free PHT assessment However, total serum drug levels of difficult-to-dose drugs like PHT are sometimes insufficient. The knowledge of the free fraction is necessary for correct dosing. In a subgroup analysis of the above BF vs. CD study we evaluated the suitability of the Sheiner-Tozer algorithm to calculate the free PHT fraction in hypoalbuminemic patients. Free PHT serum concentrations were calculated from total PHT concentration in hypoalbuminemic patients and compared with the measured free PHT. The patients were separated into two groups (a low albumin group; 35 ≤ albumin ≥ 25 g/L and a very low albumin group; albumin < 25 g/L). These two groups were compared and statistically analysed for the calculated and the measured free PHT concentration. The calculated (1.2 mg/L, SD=0.7) and the measured (1.1 mg/L, SD=0.5) free PHT concentration correlated. The mean difference in the low and the very low albumin group was 0.10 mg/L (SD=1.4, n=11) and 0.13 mg/L (SD=0.24, n=12), respectively. Although the variability of the data could be a bias, no statistically significant difference between the groups was found: t-test (p=0.78), the Passing-Bablok regression, the Spearman’s rank correlation coefficient of r=0.907 and p=0.00, and the Bland-Altman plot including the regression analysis between the calculated and the measured value (M=0.11, SD=0.28). We concluded that in absence of a free PHT serum concentration measurement also in hypoalbuminemic patients, the Sheiner-Tozer algorithm represents a useful tool to assist TDM to calculate or control free PHT by using total PHT and the albumin concentration. GC-MS Analysis of biological PHT samples To correlate PHT blood serum levels, with “brain PHT levels” (the site of action of PHT), extracellular fluid from microdialysates in neurosurgical patients could be analyzed for PHT by an appropriate quantifying analytical method. In this investigation we describe the development and validation of a sensitive gas chromatography–mass spectrometry (GC–MS) method to identify and quantitate PHT in brain microdialysate, saliva and blood from human samples. For sample clean-up a SPE was performed with a nonpolar C8-SCX column. The eluate was evaporated with nitrogen (50°C) and derivatized with trimethylsulfonium hydroxide before GC-MS analysis. 5-(p-methylphenyl)-5-phenylhydantoin was used as internal standard. The MS was run in scan mode and the identification was made with three ion fragment masses. All peaks were identified with MassLib. Spiked PHT samples showed recovery after SPE of ≥ 94%. The calibration curve (PHT 50 to 1’200 ng/ml, n=6 at six concentration levels) showed good linearity and correlation (r2 > 0.998). The limit of detection was 15 ng/mL, the limit of quantification was 50 ng/mL. Dried extracted samples were stable within a 15% deviation range for ≥ 4 weeks at room temperature. The method met International Organization for Standardization standards and was able to detect and quantify PHT in different biological matrices and patient samples. The GC-MS method with SPE is specific, sensitive, robust and well reproducible and therefore, an appropriate candidate for pharmacokinetic assessment of PHT concentrations in different biological samples of treated patients

Continuous Biochemical Processing: Investigating Novel Strategies to Produce Sustainable Fuels and Pharmaceuticals

Biochemical processing methods have been targeted as one of the potential renewable strategies for producing commodities currently dominated by the petrochemical industry. To design biochemical systems with the ability to compete with petrochemical facilities, inroads are needed to transition from traditional batch methods to continuous methods. Recent advancements in the areas of process systems and biochemical engineering have provided the tools necessary to study and design these continuous biochemical systems to maximize productivity and substrate utilization while reducing capital and operating costs. The first goal of this thesis is to propose a novel strategy for the continuous biochemical production of pharmaceuticals. The structural complexity of most pharmaceutical compounds makes chemical synthesis a difficult option, facilitating the need for their biological production. To this end, a continuous, multi-feed bioreactor system composed of multiple independently controlled feeds for substrate(s) and media is proposed to freely manipulate the bioreactor dilution rate and substrate concentrations. The optimal feed flow rates are determined through the solution to an optimal control problem where the kinetic models describing the time-variant system states are used as constraints. This new bioreactor paradigm is exemplified through the batch and continuous cultivation of β-carotene, a representative product of the mevalonate pathway, using Saccharomyces cerevisiae strain mutant SM14. The second goal of this thesis is to design continuous, biochemical processes capable of economically producing alternative liquid fuels. The large-scale, continuous production of ethanol via consolidated bioprocessing (CBP) is examined. Optimal process topologies for the CBP technology selected from a superstructure considering multiple biomass feeds, chosen from those available across the United States, and multiple prospective pretreatment technologies. Similarly, the production of butanol via acetone-butanol-ethanol (ABE) fermentation is explored using process intensification to improve process productivity and profitability. To overcome the inhibitory nature of the butanol product, the multi-feed bioreactor paradigm developed for pharmaceutical production is utilized with in situ gas stripping to simultaneously provide dilution effects and selectively remove the volatile ABE components. Optimal control and process synthesis techniques are utilized to determine the benefits of gas stripping and design a butanol production process guaranteed to be profitable

Dynamical Modeling in Cell Biology with Ordinary Differential Equations

Dynamical systems have been of interest to biologists and mathematicians alike. Many processes in biology lend themselves to dynamical study. Movement, change, and response to stimuli are dynamical characteristics that define what is \u27alive\u27. A scientific relationship between these two fields is therefore natural. In this thesis, I describe how my PhD research variously related to biological, mathematical, and computational problems in cell biology. In chapter 1 I introduce some of the current problems in the field. In chapter 2, my mathematical model of firefly luciferase in vivo shows the importance of dynamical models to understand systems. Data originally collected by other researchers led to apparently straight-forward conclusions based on experimental techniques. However, this is contradicted once a dynamical model is applied to the system. I show that interpretation of data that comes as a snapshot of a dynamical system is a dynamical modeling problem, even if one can fit a nice linear regression to that data. In chapters 2 and 3 I demonstrate the value of added complexity to mathematical models in firefly luciferase. Usually, a simple solution is considered best, but this may leave information behind. By expressing the simplified Michaelis-Menten model as a system of differential equations we are able to get valuable parameter estimates. These parameter estimates would be otherwise costly. In addition, the model allows us to quantify trends in the data that are visible but not interpretable by scientists without a mathematical framework. In chapter 4, a problem without experimental data is tackled regarding the plant cell cycle and its switch to endoreplication. In this case, much tedious hand-fitting is required to answer the research questions. Using this technique I was able to address biological questions, understand the validity of the model and the biological assumptions that went into that model. In chapter 5 I motivated the further development of educational tools to disseminate modeling and computational techniques to biologists. This type of training is necessary for the future of the field

FluxSimulator: An R Package to Simulate Isotopomer Distributions in Metabolic Networks

The representation of biochemical knowledge in terms of fluxes (transformation rates) in a metabolic network is often a crucial step in the development of new drugs and efficient bioreactors. Mass spectroscopy (MS) and nuclear magnetic resonance spectroscopy (NMRS) in combination with ^13C labeled substrates are experimental techniques resulting in data that may be used to quantify fluxes in the metabolic network underlying a process. The massive amount of data generated by spectroscopic experiments increasingly requires software which models the dynamics of the underlying biological system. In this work we present an approach to handle isotopomer distributions in metabolic networks using an object-oriented programming approach, implemented using S4 classes in R. The developed package is called FluxSimulator and provides a user friendly interface to specify the topological information of the metabolic network as well as carbon atom transitions in plain text files. The package automatically derives the mathematical representation of the formulated network, and assembles a set of ordinary differential equations (ODEs) describing the change of each isotopomer pool over time. These ODEs are subsequently solved numerically. In a case study FluxSimulator was applied to an example network. Our results indicate that the package is able to reproduce exact changes in isotopomer compositions of the metabolite pools over time at given flux rates.

A computational model of the line-1 retrotransposon life cycle and visualization of metabolic networks in 3-dimensions.

Computational modeling of metabolic reactions and cellular systems is evolving as a tool for quantitative prediction of metabolic parameters and reaction pathway analysis. In this work, the basics of computational cell biology are presented as well as a summary of physical processes within the cell, and the algorithmic methods used to find time dependent solutions. Protein-protein and enzyme-substrate interactions are mathematically represented via mass action kinetics to construct sets of linear differential equations that describe reaction rates and formation of protein complexes. Using mass action methods, examples of reaction networks and their solutions are presented within the Virtual Cell simulation package. A computational model capturing the life cycle of an ancient (typically dormant) parasitic genetic element called the long interspersed nuclear element type 1 (LINE-1) is developed and refined. When activated, the proteins encoded by LINE-1 function to produce copies of itself that are reinserted into the genome. Thus, activation of LINE-1 is associated with genomic instability, tumorigenesis, and cancer. The model tracks the copy number of LINE-1 associated proteins, mRNA, and DNA under conditions that simulate carcinogenic insults to the element’s epigenetic silencing mechanisms. Results show that proliferation of LINE-1 has a distinct threshold as a function of mRNA copy number and transcription rate. Above the threshold, the retrotransposon copy number enters a positive feedback loop that allows the cDNA copy number to grow exponentially. We also found that most of the LINE-1 RNA was degraded via the RNAase pathway and that neither ORF0 RNAi, nor the sequestration of LINE-1 products into granules and multivesicular structures, played a significant role in regulating the retrotransposon’s life cycle. Most systems in computational cell biology are represented as 2-dimensional graphs of nodes symbolizing reactions and chemical species. At even moderate complexity, however, these network maps become difficult to read and understand. Thus, a Python interface was developed which maps biological networks generated using the free Virtual Cell simulation package onto an impressive open source 3-D network visualization system called OpenGraphiti. By interfacing these two packages the software allows one to view reaction networks and solutions of simulations in a more intuitive way