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PubMed2003

From biophysics to behavior: Catacomb2 and the design of biologically-plausible models for spatial navigation.

A variety of approaches are available for using computational models to help understand neural processes over many levels of description, from sub-cellular processes to behavior. Alongside purely deductive bottom-up or top-down modeling, a systems design strategy has the advantage of providing a clear goal for the behavior of a complex model. The order in which biological details are added is dictated by functional requirements in terms of the tasks that the model should perform. Ideas from engineering can be mixed with those from biology to build systems in which some constituents are modeled in detail using biologically-realistic components, while others are implemented directly in software. This allows the areas of most interest to be studied within the context of a behaving system in which each component is constrained both by the biology it is intended to represent as well as the task it is required to perform within the system. The Catacomb2 modeling package has been developed to allow rapid and flexible design and study of complex multi-level systems ranging in scale from ion channels to whole animal behavior. The methodology, internal architecture, and capabilities of the system are described. Its use is illustrated by a modeling case study in which hypotheses about how parahippocampal and hippocampal structures may be involved in spatial navigation tasks are implemented in a model of a virtual rat navigating through a virtual environment in search of a food reward. The model incorporates theta oscillations to separate encoding from retrieval and yields testable predictions about the phase relations of spiking activity to theta oscillations in different parts of the hippocampal formation at various stages of the behavioral task.

Algorithms↗
PubMed2004

The importance of being exhaustive. Optimization of bridging structural water molecules and water networks in models of biological systems.

Algorithms and protocols are described for the optimization for H-bonding of isolated singular H2O molecules and entire networks of H2O molecules. Unlike other approaches that are prone to being trapped in local energy minima, these methods rely on exhaustive searches of orientation space for the H2O molecules. The results are scored with the HINT hydropathic interaction model, but the algorithms should be general for any energy-scoring computation. Two examples are provided: 1) the tightly-bound H2O molecule 301 of HIV-1 protease is shown to be more reasonably oriented in terms of forming H-bonds with this method than with a molecular mechanics energy minimization method; and 2) the H2O network surrounding carbonmonoxymyoglobin is constructed and analyzed for a 1.80-A neutron-diffraction structure. The H-atom positions calculated with this method show a somewhat better agreement with the experimental results than do the H-atom positions calculated with molecular mechanics, and both are considerably better than random.

Hydrogen↗
PubMed2001

Global identifiability of nonlinear models of biological systems.

A prerequisite for well-posedness of parameter estimation of biological and physiological systems is a priori global identifiability, a property which concerns uniqueness of the solution for the unknown model parameters. Assessing a priori global identifiability is particularly difficult for nonlinear dynamic models. Various approaches have been proposed in the literature but no solution exists in the general case. In this paper, we present a new algorithm for testing global identifiability of nonlinear dynamic models, based on differential algebra. The characteristic set associated to the dynamic equations is calculated in an efficient way and computer algebra techniques are used to solve the resulting set of nonlinear algebraic equations. The algorithm is capable of handling many features arising in biological system models, including zero initial conditions and time-varying parameters. Examples of usage of the algorithm for analyzing a priori global identifiability of nonlinear models of biological and physiological systems are presented.

Algorithms↗
PubMed1993

Incorporating biologically based models into assessments of risk from chemical contaminants.

The general approach to assessment of risk from chemical contaminants in drinking water involves three steps: hazard identification, exposure assessment, and dose-response assessment. Traditionally, the risks to humans associated with different levels of a chemical have been derived from the toxic responses observed in animals. It is becoming increasingly clear, however, that further information is needed if risks to humans are to be assessed accurately. Biologically based models help clarify the dose-response relationship and reduce uncertainty.

Animals↗
PubMed1991

Bifurcating spatially heterogeneous solutions in a chemotaxis model for biological pattern generation.

We consider a simple cell-chemotaxis model for spatial pattern formation on two-dimensional domains proposed by Oster and Murray (1989, J. exp. Zool. 251, 186-202). We determine finite-amplitude, steady-state, spatially heterogeneous solutions and study the effect of domain growth on the resulting patterns. We also investigate in-depth bifurcating solutions as the chemotactic parameter varies. This numerical study shows that this deceptively simple-chemotaxis model can produce a surprisingly rich spectrum of complex spatial patterns.

Animals↗
PubMed2004

Plenty more fish in the sea: comparative and functional genomics using teleost models.

Biology has collaborated with evolution to create an enormous repertoire of animal variation. This in turn has provided experimental biologists with models that can be used in the lab to simulate more complex systems. Amongst the organisms that have been used in this way are fish, where a large number of species have been utilised in a variety of different ways. Fish possess the smallest genomes of any vertebrate, making them ideal as models for genome analysis and gene discovery. Fish are also easy to maintain in a laboratory environment and can be bred easily. Fish often have well-defined physiology and respond well to many experimental procedures. Finally, fish are of great economic importance in their own right, as one of the world's largest sources of protein. In this review, the relationship between fish species is examined along with the role of different fish models in a wide range of biological disciplines.

Animals↗
PubMed2001

Different types of aortic stenosis and simulation of their morphological-hydrodynamic interdependence--in vitro study with allografts and stenotic valve models.

Biological valves display a dependence of valve resistance and valve area on flow and a phase shift between systolic flow through the valves and pressure difference across the valves. The pressure-flow relations of stenosed valves raise questions about the "best measure of stenosis". There is a need for quantitative evaluation of the hydrodynamic performance of homografts and allografts. In the present paper, we report on in vitro studies of the hydrodynamic behavior of homografts from human donors, allografts from different animal species as well as three valve models. Valve model I was designed to simulate flow-dependence of valve area, valve model II was designed to simulate restricted valve opening independent of flow, and valve model III was designed to simulate a flow-dependent movement of valve root in flow direction. Among other aspects, the effect of increased viscosity of the test fluid on the pressure difference and the effects of water absorption by valve tissue on valve characteristics were investigated. The results of the present studies clearly indicate that any biological valve may be modelled as a serial connection of a model I type valve and a model II type valve. From the results, the dependence of the characteristic pressure-flow relationship of a valve on valve size and valve distensibility can be clearly seen and the clinical significance of the characteristic coefficients of the pressure-flow relationship of a valve can be elucidated. Further, it was shown that the characteristic phase shift between flow and pressure difference displayed by biological valves is due to their movable valve plane similar to that of valve model III.

Animals↗
PubMed1993

Entropic analysis of biological growth models.

Using the concept of isomorphism, we provide a quantitative description of the thermodynamic characteristics of the Logistic, Bertalanffy, and Gompertz biological growth models. With the help of the entropy expressions derived from the isomorphic probabilistic models, we show that the Logistic, Bertalanffy, and Gompertz growth models have distinct thermodynamic characteristics, though they have similar sigmoid trajectories in the time domain. The entropic analysis further reveals that the Gompertz model corresponds to a completely open system, in which cells receive adequate nutrition and competition among cells can be neglected. We have also applied the present entropic analysis to the modeling of tumor growth. Our results suggest that the entropic expression provides a theoretical justification for using the Gompertz model for describing the development of tumor cell populations. The entropic analysis may play an important role in studying the mechanisms of different biological growth processes.

Animals↗
PubMed2002

Using a biology-based model (DEBtox) to analyze bioassays in ecotoxicology: opportunities and recommendations.

The conventional analysis of bioassays does not account for biological significance. However, mathematical models do exist that are realistic from a biological point of view and describe toxicokinetics and effects on test organisms of chemical compounds. Here we studied a biology-based model (DEBtox) that provides an estimate of a no-effect concentration, and we demonstrated the ability of such a model to adapt to different situations. We showed that the basic model can be extended to deal with problems usually faced during bioassays like time-varying concentrations or unsuitable choices of initial concentrations. To reach this goal, we report experimental data from Daphnia magna exposed to zinc. These data also showed the potential benefit of the model in understanding the influence of food on toxicity. We finally make some recommendations about the choice of initial concentrations, and we propose a test with a depuration period to check the relevance and the predictive capacity of the DEBtox model. In our experiments, the model performed well and proved its usefulness as a tool in risk assessment.

Animals↗
PubMed2004

Studies of radon-exposed miner cohorts using a biologically based model: comparison of current Czech and French data with historic data from China and Colorado.

The biologically based two-stage clonal expansion (TSCE) model is used to analyze lung cancer in several miners studies, two new ones (Czech, French) and two historic ones (Chinese, Colorado). In all cases, the model assumptions are identical. An action of radiation on initiation, promotion, and transformation is allowed. While all four studies indicate a highly significant action of radiation on promotion, the action on initiation is not significant in the French cohort, and barely significant in the Colorado miners cohort. No action on transformation is found in the Colorado miners, while the other data sets indicate a borderline significance. The model can describe all the data sets adequately, with different model parameters. The observed patterns in exposure, time since beginning of exposure, birth year, age and calendar year are reproduced well. The doubling exposure rate for initiation is about 3.5 WLM/year in the new data sets, while it is higher in the historic data sets. For transformation the doubling rate is about 20 WLM/year for the new data sets, while again the historic data give higher estimates. The action of radiation on promotion is quite different in the four data sets. These differences also induce different risk estimates at low exposures. The larger power of the new studies at these low exposures, compared to the historic data requires less extrapolation when the risk at very low exposures is estimated.

Age Distribution↗
PubMed2006

Interactions of a fungistatic antibiotic, griseofulvin, with phospholipid monolayers used as models of biological membranes.

Griseofulvin (GF) is an oral antibiotic for widely occurring superficial mycosis in man and animals caused by dermaphyte fungi; it is also used in agriculture as a fungicide. The mechanism of the biological activity of GF is poorly understood. Here, the interactions of griseofulvin with lipid membranes were studied using 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC), and 1,2-myristoyl-sn-glycero-3-phosphoethanolamine (DMPE) monolayers spread at the air/water interface. Surface pressure (Pi), electric surface potential (Delta V), grazing incidence X-ray diffraction (GIXD), and Brewster angle microscopy (BAM) were used for studying pure phospholipid monolayers spread on GF aqueous solutions, as well as mixed phospholipid/GF monolayers spread on pure water subphase. Moreover, phospholipase A2 (PLA2) activity toward DLPC monolayers and molecular modeling of the GF surface and lipophilic properties were used to get more insight into the mechanisms of GF-membrane interactions. The results obtained show that GF has a meaningful impact on the film properties; we propose that nonpolar interactions are by and large responsible for GF retention in the monolayers. The modification of membrane properties can be detected using both physicochemical and enzymatic methods. The results obtained may be relevant for elaborating GF preparations with increased bioavailability.

Air↗
PubMed2001

A biologically based model of growth and senescence of Syrian hamster embryo (SHE) cells after exposure to arsenic.

We modified the two-stage Moolgavkar-Venzon-Knudson (MVK) model for use with Syrian hamster embryo (SHE) cell neoplastic progression. Five phenotypic stages are proposed in this model: Normal cells can either become senescent or mutate into immortal cells followed by anchorage-independent growth and tumorigenic stages. The growth of normal SHE cells was controlled by their division, death, and senescence rates, and all senescent cells were converted from normal cells. In this report, we tested the modeling of cell kinetics of the first two phenotypic stages against experimental data evaluating the effects of arsenic on SHE cells. We assessed cell division and death rates using flow cytometry and correlated cell division rates to the degree of confluence of cell cultures. The mean cell death rate was approximately equal to 1% of the average division rate. Arsenic did not induce immortalization or further mutations of SHE cells at concentrations of 2 microM and below, and chromium (3.6 microM) and lead (100 microM) had similar negative results. However, the growth of SHE cells was inhibited by 5.4 microM arsenic after a 2-day exposure, with cells becoming senescent after only 16 population doublings. In contrast, normal cells and cells exposed to lower arsenic concentrations grew normally for at least 30 population doublings. The biologically based model successfully predicted the growth of normal and arsenic-treated cells, as well as the senescence rates. Mechanisms responsible for inducing cellular senescence in SHE cells exposed to arsenic may help explain the apparent inability of arsenic to induce neoplasia in experimental animals.

Animals↗
PubMed1986

Molecular dynamics simulation study of a two-dimensional fluid mixture system: a model for biological membranes.

The computer simulation technique of molecular dynamics was applied to a model two-dimensional fluid mixture system to examine the short-range ordering of lipid and protein molecules in biological membranes. The model system consists of small disks (lipids) and large disks (proteins) with a radius ratio of 6, constrained to move in a plane. The particles interact with pairwise additive repulsive short range potentials, so as to simulate hard disks. Periodic boundary conditions are assumed in order to minimize boundary effects. For values of the number density of the small disks and of the temperature appropriate for a lipid membrane, the fraction, f, of small disks 'next to' at least one large disk was computed by molecular dynamics. This was done as a function of concentration and for several definitions of 'next to'. The molecular dynamics results show that, at moderately low mole fractions of the large disks, the calculated values of f deviate noticeably from the linear relation which would be expected in the absence of protein-protein proximity effects. The results are discussed in terms of current models of lipid-protein ordering in biological membranes.

Computers↗
PubMed2003

Neural-network-based parameter estimation in S-system models of biological networks.

The genomic and post-genomic eras have been blessing us with overwhelming amounts of data that are of increasing quality. The challenge is that most of these data alone are mere snapshots of the functioning organism and do not reveal the organizational structure of which the particular genes and metabolites are contributors. To gain an appreciation of their roles and functions within cells and organisms, genomic and metabolic data need to be integrated in systems models that allow the testing of hypotheses, generate experimentally testable predictions, and ultimately lead to true explanations. One type of data that is particularly well suited for such integration consists of time profiles, which show gene activities, metabolite concentrations, or protein prevalences at dense series of time points. We show with a specific example how such time series can be analyzed and evaluated, if some structural information about the data is available, even if this information is incomplete. The method consists of three components. The first is a particularly suitable mathematical modeling framework, namely Biochemical Systems Theory, in which parameters are direct indicators of the organization of the underlying phenomenon, the second is the training of an artificial neural network for data smoothing and complementation, and the third is a technique for reinterpreting differential equations in a fashion that facilitates parameter estimation. A prototype webtool for these analyses is available at https://bioinformatics.musc.edu/webmetabol/.

Biology↗
PubMedDate not supplied

Application of a systems biology approach to developmental neurotoxicology.

Systems biology can be applied to enhance the understanding of complex biological processes such as apoptosis in the developing brain. Systems biology, as applied to toxicology, provides a structure to arrange information in the form of a biological model. The approach allows for the subsequent and iterative perturbation of the initial model with the use of toxicants, and the comparison of the resulting data against the proposed biological model. It is postulated that the exposure of the developing rat to NMDA antagonists, e.g., ketamine or phencyclidine (PCP), causes a compensatory up-regulation of NMDA receptors, thereby making cells bearing these receptors more vulnerable to excitotoxic effects of endogenous glutamate. Although comprehensive gene expression/proteomic studies and mathematical modeling remain to be accomplished, a biological model has been established and perturbed in an iterative manner to allow confirmation of the biological pathway for NMDA antagonist-induced brain cell death in the developing rat.

Animals↗