Bulletin of Mathematical Biology

Marino, Simeone; Ganguli, Suman; Joseph, Ian; Kirschner, Denise

doi: 10.1016/S0092-8240(03)00046-6pmid: 14607284

In this work we re-examine an existing model of gastric acid secretion. The model is a 2-compartment model of the human stomach accounting for regions where relevant cells (D, G, ECL and parietal cells) and proteins and acid they secrete (somatostatin, gastrin, histamine, and gastric acid, respectively) are found. These proteins compose a positive and negative feedback system that controls the secretion of gastric acid by parietal cells. The original model consists of 18 ordinary differential equations and yields a stable 3-period limit cycle solution. We modify the existing model by introducing a delay into the system and assuming that the cell populations are in steady state over a short-time window (<300 h) and are able to reduce the system to an 8-equation delay differential equation model. In addition to demonstrating congruency between the two models, we also show that a similar stability is only reproducible when the delay in gastrin transport is approximately 30 min. This suggests that gastric acid secretion homeostasis likely depends strongly on the delay in gastrin transport from the antrum to the corpus.

González Díaz, Humberto; Armas, Ronal; Molina, Reinaldo

doi: 10.1016/S0092-8240(03)00064-8pmid: 14607285

The design of novel anti-HIV compounds has now become a crucial area for scientists working in numerous interrelated fields of science such as molecular biology, medicinal chemistry, mathematical biology, molecular modelling and bioinformatics. In this context, the development of simple but physically meaningful mathematical models to represent the interaction between anti-HIV drugs and their biological targets is of major interest. One such area currently under investigation involves the targets in the HIV-RNA-packaging region. In the work described here, we applied Markov chain theory in an attempt to describe the interaction between the antibiotic paromomycin and the packaging region of the RNA in Type-1 HIV. In this model, a nucleic acid squeezed graph is used. The vertices of the graph represent the nucleotides while the edges are the phosphodiester bonds. A stochastic (Markovian) matrix was subsequently defined on this graph, an operation that codifies the probabilities of interaction between specific nucleotides of HIV-RNA and the antibiotic. The strength of these local interactions can be calculated through an inelastic vibrational model. The successive power of this matrix codifies the probabilities with which the vibrations after drug-RNA interactions vanish along the polynucleotide main chain. The sums of self-return probabilities in the k-vicinity of each nucleotide represent physically meaningful descriptors. A linear discriminant function was developed and gave rise to excellent discrimination in 80.8% of interacting and footprinted nucleotides. The Jackknife method was employed to assess the stability and predictability of the model. On the other hand, a linear regression model predicted the local binding affinity constants between a specific nucleotide and the antibiotic (R 2 = 0.91, Q 2 = 0.86). These kinds of models could play an important role either in the discovery of new anti-HIV compounds or the study of their mode of action.

Coombs, Daniel; Gilchrist, Michael; Percus, Jerome; Perelson, Alan

doi: 10.1016/S0092-8240(03)00056-9pmid: 14607286

Viruses reproduce by multiplying within host cells. The reproductive fitness of a virus is proportional to the number of offspring it can produce during the lifetime of the cell it infects. If viral production rates are independent of cell death rate, then one expects natural selection will favor viruses that maximize their production rates. However, if increases in the viral production rate lead to an increase in the cell death rate, then the viral production rate that maximizes fitness may be less than the maximum. Here we pose the question of how fast should a virus replicate in order to maximize the number of progeny virions that it produces. We present a general mathematical framework for studying problems of this type, which may be adapted to many host-parasite systems, and use it to examine the optimal virus production scheduling problem from the perspective of the virus.

Devloo, Vincent; Hansen, Pierre; Labbé, Martine

doi: 10.1016/S0092-8240(03)00061-2pmid: 14607287

The goal of generalized logical analysis is to model complex biological systems, especially so-called regulatory systems, such as genetic networks. This theory is mainly characterized by its capacity to find all the steady states of a given system and the functional positive and negative circuits, which generate multistationarity and a cycle in the state sequence graph, respectively. So far, this has been achieved by exhaustive enumeration, which severely limits the size of the systems that can be analysed. In this paper, we introduce a mathematical function, called image function, which allows the calculation of the value of the logical parameter associated with a logical variable depending on the state of the system. Thus the state table of the system is represented analytically. We then show how all steady states can be derived as solutions to a system of steady-state equations. Constraint programming, a recent method for solving constraint satisfaction problems, is applied for that purpose. To illustrate the potential of our approach, we present results from computer experiments carried out on very large randomly-generated systems (graphs) with hundreds, or even thousands, of interacting components, and show that these systems can be solved using moderate computing time. Moreover, we illustrate the approach through two published applications, one of which concerns the computation times of all steady states for a large genetic network.

Chopp, D.; Kirisits, M.; Moran, B.; Parsek, M.

doi: 10.1016/S0092-8240(03)00057-0pmid: 14607288

In a process called quorum sensing, bacteria monitor their population density via extracellular signaling molecules and modulate gene expression accordingly. In this paper, a one-dimensional model of a growing Pseudomonas aeruginosa biofilm is examined. Quorum sensing has been included in the model through equations describing the production, degradation, and diffusion of the signaling molecules, acyl-homoserine lactones, in the biofilm. From this model, we are able to make some important observations about quorum sensing. First, in order for quorum sensing to initiate near the substratum, in accordance with experimental observations, the model suggests that cells in oxygen-deficient regions of the biofilm must still be synthesizing the signal compound. Second, the induction of quorum sensing is related to a critical biofilm depth; once the biofilm grows to the critical depth, quorum sensing is induced. Third, the critical biofilm depth varies with the pH of the surrounding fluid. Of particular interest is the prediction of a critical pH threshold, above which quorum sensing is not possible at any depth. These results highlight the importance of careful study of the relationship among metabolic activity of the bacterium, signal synthesis, and the chemistry of the surrounding environment.

Bonneuil, Noël

doi: 10.1016/S0092-8240(03)00060-0pmid: 14607289

Viability conditions permit to characterize all processes compatible with given constraints, notably of available food, so that there exists at least one possibility for the system to perpetuate itself forever. The concept of contingent cone to a set of constraints permits to identify two classes of corrections to apply to equations of natural growth. Usual basic models convey those corrections only in certain regions of the parameter space. A general model-building stemming from the constraints is presented. Experimental populations from historical case studies highlight the mathematical concept of viability corrections.

Heyd, Andrew; Drew, Donald

doi: 10.1016/S0092-8240(03)00076-4pmid: 14607290

A mathematical model is presented for the steps in the elongation process, and the steady-state elongation rate as a function of the amino acid concentrations is found. In addition, the reset sub-process of the elongation process is modeled. The rate of elongation of peptide chains is found to be a function of the concentration of the amino acid to be bound and the concentration of all other amino acids. In addition, the overall elongation rate depends on the concentrations of elongation factors.

Tzafriri, A.

doi: 10.1016/S0092-8240(03)00059-4pmid: 14607291

The total quasi-steady state approximation (tQSSA) for the irreversible Michaelis-Menten scheme is derived in a consistent manner. It is found that self-consistency of the initial transient guarantees the uniform validity of the tQSSA, but does not guarantee the validity of the linearization in the original derivation of Borghans et al. (1996, Bull. Math. Biol., 58, 43–63). Moreover, the present rederivation yielded the noteworthy result that the tQSSA is at least roughly valid for any substrate and enzyme concentrations. This reinforces and extends the original assertion that the parameter domain for which the tQSSA is valid overlaps the domain of validity of the standard quasi-steady state approximation and includes the limit of high enzyme concentrations. The criteria for the uniform validity of the original (linearized) tQSSA are corrected, and are used to derive approximate solutions that are uniformly valid in time. These approximations overlap and extend the domains of validity of the standard and reverse quasi-steady state approximations.

Gorfine, Malka; Freedman, Laurence; Shahaf, Gitit; Mehr, Ramit

doi: 10.1016/S0092-8240(03)00062-4pmid: 14607292

In this paper we introduce a simple framework which provides a basis for estimating parameters and testing statistical hypotheses in complex models. The only assumption that is made in the model describing the process under study, is that the deviations of the observations from the model have a multivariate normal distribution. The application of the statistical techniques presented in this paper may have considerable utility in the analysis of a wide variety of complex biological and epidemiological models. To our knowledge, the model and methods described here have not previously been published in the area of theoretical immunology.

Smith, Nicolas

doi: 10.1016/S0092-8240(03)00063-6pmid: 14607293

Two classes of mathematical framework have previously been developed to model active tension generation in contracting muscle. Cross-bridge models of muscle are biophysically based but computationally expensive to solve, and thus unsuitable for embedding in spatially distributed continuum representations. Fading memory models are computationally efficient but provide limited biophysical insight. In this study a novel computational method is proposed for coupling these two frameworks such that biophysical events can be determined and computational tractability maintained. Within the cross-bridge model, the functional forms of the distribution of cross-bridges, as a function of strain in each state, are approximated using the distribution moment approach. Using the variables of area, mean and standard deviation of each distribution, analytic expressions are developed to calculate the temporal dynamics of stiffness, tension and energy. A root finding method is employed to adjust the variables such that the temporal dynamics of the cross-bridge model match those of an equivalent fading memory model. The method is demonstrated for sinusoidal perturbations in length at two frequencies, with an approximate 30-fold increase in computational efficiency over a conventional technique for finding a solution to the cross-bridge model.