Evaluation of rate law approximations in bottom-up kinetic models of metabolism.

BackgroundThe mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.ResultsIn this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.ConclusionsOverall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches

NCCN Guidelines Insights: Survivorship, Version 2.2019.

The NCCN Guidelines for Survivorship provide screening, evaluation, and treatment recommendations for consequences of cancer and cancer treatment to aid healthcare professionals who work with survivors of adult-onset cancer. Guidance is also provided to help promote physical activity, weight management, and proper immunizations in survivors and to facilitate care coordination to ensure that all needs are addressed. These NCCN Insights summarize some of the topics discussed by the NCCN Survivorship Panel during the 2019 update of the guidelines, including the survivorship population addressed, ways to improve care coordination, and pain management

Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units

The Gillespie τ-Leaping Method is an approximate algorithm that is faster than the exact Direct Method (DM) due to the progression of the simulation with larger time steps. However, the procedure to compute the time leap τ is quite expensive. In this paper, we explore the acceleration of the τ-Leaping Method using Graphics Processing Unit (GPUs) for ultra-large networks ( reaction channels). We have developed data structures and algorithms that take advantage of the unique hardware architecture and available libraries. Our results show that we obtain a performance gain of over 60x when compared with the best conventional implementations

NCCN Guidelines Insights: Survivorship, Version 2.2020.

The NCCN Guidelines for Survivorship provide screening, evaluation, and treatment recommendations for consequences of adult-onset cancer and its treatment, with the goal of helping healthcare professionals who work with survivors, including those in primary care. The guidelines also provide recommendations to help clinicians promote physical activity, weight management, and proper immunizations in survivors and facilitate care coordination to ensure that all of the survivors needs are addressed. These NCCN Guidelines Insights summarize additions and changes made to the guidelines in 2020 regarding cardiovascular disease risk assessment and screening for subsequent primary malignancies

Delayed Differentiation Makes Many Models Compatible with Data for CD8+ T Cell Differentiation

Upon antigen stimulation, naïve CD8+ T cells differentiate into short-lived effectors and longer-lived memory T cells. The kinetics of expansion of antigen-specific CD8+ T cells is highly reproducible at the population level, but the fate of individual naïve cells is stochastic, as individual naïve CD8+ T cells produce different numbers of effector and memory cells. Using mathematical models to analyse experimental data on tracing the fate of individual naïve T cells, it was previously shown that a linear model where naïve CD8+ T cells first differentiate into memory precursors that subsequently differentiate into effector cells describes the data best. However, this ‘memory first’ linear model assumed that the proliferation and differentiation events were distributed exponentially, whereas several studies indicate that differentiation of CD8+ T cell subsets need not follow an exponential distribution. Here we investigate the effect of delayed differentiation by adding intermediate compartments and use similar ordinary differential equations and Gillespie simulations to evaluate alternate models of CD8+ T cell differentiation. Models where a substantial fraction of the naïve CD8+ T cells directly differentiate into effector cells, without going through a memory phase, exhibit population dynamics that are very similar to the original ‘memory first’ linear model. Because alternate models with delayed differentiation perform better than those without a delay, we conclude that non-exponential forms of cellular differentiation need to be considered when comparing models. Hence the exact pathway for the differentiation of naïve CD8+ T cells into effector and memory T cells remains an open question

A Complete Pathway Model for Lipid A Biosynthesis in Escherichia coli.

Lipid A is a highly conserved component of lipopolysaccharide (LPS), itself a major component of the outer membrane of Gram-negative bacteria. Lipid A is essential to cells and elicits a strong immune response from humans and other animals. We developed a quantitative model of the nine enzyme-catalyzed steps of Escherichia coli lipid A biosynthesis, drawing parameters from the experimental literature. This model accounts for biosynthesis regulation, which occurs through regulated degradation of the LpxC and WaaA (also called KdtA) enzymes. The LpxC degradation signal appears to arise from the lipid A disaccharide concentration, which we deduced from prior results, model results, and new LpxK overexpression results. The model agrees reasonably well with many experimental findings, including the lipid A production rate, the behaviors of mutants with defective LpxA enzymes, correlations between LpxC half-lives and cell generation times, and the effects of LpxK overexpression on LpxC concentrations. Its predictions also differ from some experimental results, which suggest modifications to the current understanding of the lipid A pathway, such as the possibility that LpxD can replace LpxA and that there may be metabolic channeling between LpxH and LpxB. The model shows that WaaA regulation may serve to regulate the lipid A production rate when the 3-deoxy-D-manno-oct-2-ulosonic acid (KDO) concentration is low and/or to control the number of KDO residues that get attached to lipid A. Computation of flux control coefficients showed that LpxC is the rate-limiting enzyme if pathway regulation is ignored, but that LpxK is the rate-limiting enzyme if pathway regulation is present, as it is in real cells. Control also shifts to other enzymes if the pathway substrate concentrations are not in excess. Based on these results, we suggest that LpxK may be a much better drug target than LpxC, which has been pursued most often

Intrinsic Noise Analyzer: A Software Package for the Exploration of Stochastic Biochemical Kinetics Using the System Size Expansion

The accepted stochastic descriptions of biochemical dynamics under well-mixed conditions are given by the Chemical Master Equation and the Stochastic Simulation Algorithm, which are equivalent. The latter is a Monte-Carlo method, which, despite enjoying broad availability in a large number of existing software packages, is computationally expensive due to the huge amounts of ensemble averaging required for obtaining accurate statistical information. The former is a set of coupled differential-difference equations for the probability of the system being in any one of the possible mesoscopic states; these equations are typically computationally intractable because of the inherently large state space. Here we introduce the software package intrinsic Noise Analyzer (iNA), which allows for systematic analysis of stochastic biochemical kinetics by means of van Kampen’s system size expansion of the Chemical Master Equation. iNA is platform independent and supports the popular SBML format natively. The present implementation is the first to adopt a complementary approach that combines state-of-the-art analysis tools using the computer algebra system Ginac with traditional methods of stochastic simulation. iNA integrates two approximation methods based on the system size expansion, the Linear Noise Approximation and effective mesoscopic rate equations, which to-date have not been available to non-expert users, into an easy-to-use graphical user interface. In particular, the present methods allow for quick approximate analysis of time-dependent mean concentrations, variances, covariances and correlations coefficients, which typically outperforms stochastic simulations. These analytical tools are complemented by automated multi-core stochastic simulations with direct statistical evaluation and visualization. We showcase iNA’s performance by using it to explore the stochastic properties of cooperative and non-cooperative enzyme kinetics and a gene network associated with circadian rhythms. The software iNA is freely available as executable binaries for Linux, MacOSX and Microsoft Windows, as well as the full source code under an open source license