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At least 433 records · Page 24Linked to original sources
PubMed2006

Constructing explanatory process models from biological data and knowledge.

OBJECTIVE: We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. METHODS: We cast both models and background knowledge in terms of processes that interact to account for behavior. We also describe IPM, an algorithm for inducing quantitative process models from such input. RESULTS: We demonstrate IPM's use both on photosynthesis and on a second domain, biochemical kinetics, reporting the models induced and their fit to observations. CONCLUSION: We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.

Algorithms↗
PubMed1994

Hidden Markov models of biological primary sequence information.

Hidden Markov model (HMM) techniques are used to model families of biological sequences. A smooth and convergent algorithm is introduced to iteratively adapt the transition and emission parameters of the models from the examples in a given family. The HMM approach is applied to three protein families: globins, immunoglobulins, and kinases. In all cases, the models derived capture the important statistical characteristics of the family and can be used for a number of tasks, including multiple alignments, motif detection, and classification. For K sequences of average length N, this approach yields an effective multiple-alignment algorithm which requires O(KN2) operations, linear in the number of sequences.

Algorithms↗
PubMed1995

Biologically-based models of dioxin pharmacokinetics.

Significant advances have been made in the development of physiologically-based models of dioxin pharmacokinetics (PBPK) in the last 5-6 years. These models incorporate explicit descriptions of biological factors which determine tissue dosimetry of dioxin and include some description of dioxin-mediated pharmacodynamic events. Biological determinants of dioxin disposition include fat solubility, specific and inducible binding in the liver, diffusion-limited tissue distribution and metabolic elimination. PBPK models have been successfully used to predict the dose and time-dependent chemical disposition and protein induction properties of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) over a wide variety of experimental data sets with rodents. The models have also been extended to describe the disposition of a brominated dioxin, 2,3,7,8-tetrabromodibenzo-p-dioxin. As these quantitative descriptions of disposition are more fully refined, particularly with regard to pharmacodynamic descriptions of dioxin-mediated alterations in gene expression, more accurate predictions of tissue dosimetry and tissue responses will be performed across dose, species and related polyhalogenated aromatic hydrocarbons. Accurate, mechanistic dosimetry models will facilitate biologically-based approaches to the human risk assessment of these important and ubiquitous environmental contaminants.

Animals↗
PubMed2002

The use of biologically based modeling in risk assessment.

Technological advances are rapidly leading toxicology and risk assessment to an age where there are advantages of routine use of biologically based modeling as an adjunct to laboratory experiments will be compelling. The biologically based model, a formal representation of the system being studied, serves both the experimentalist and the risk assessor. Benefits of its development include identification of data gaps, optimization of experimental design and, with sufficient validation, quantitative extrapolations between routes of exposure, species and doses. This article focuses on the role of biologically based modeling in support of the dose-response assessment component of risk assessment. The problem of uncertainty in dose-response assessment and the role of mechanistic studies combined with modeling to efficiently reduce uncertainty are discussed. The need for close cooperation between experimentalists, modelers, and risk assessors to achieve this efficiency is emphasized. A model for formaldehyde is briefly described to illustrate key issues.

Animals↗
PubMed1984

Biological age and its estimation. III. Introduction of a correction to the multiple regression model of biological age in cross-sectional and longitudinal studies.

In order to eliminate systematic error inherent in assessment of an individual biological age (BA), a correction is suggested for the multiple regression model of BA. The larger the difference between an individual's chronological age and middle age of the sample, the greater the correction value. BA estimates of apparently healthy people of 60 to 100 years were assessed using measurements of three physiological indices: hand grip strength, short-time memory, and vibrotactile sensitivity. Introduction of the correction allowed comparison of BA estimates of the same individual obtained from repeated observations. Preliminary results show there are at least four types of the BA changes with advancing age.

Aged↗
PubMed1976

Coupling of Gaussian electromagnetic pulse into a muscle-bone model of biological structure.

The effect of angle of incidence on the transmission electromagnetic pulse with Gaussion character in biological material is studied. The model assumed is a layer of soft tissue over a semi-infinite medium of boney structure governed by alpha dispersion. The numerical results demonstrate that the transmitted pulse strength is the greatest when the pulse is incident normally on the air-tissue interface. The coupling efficiency for a one microsecond pulse is three times as big as that for a ten microsecond pulse.

Bone and Bones↗
PubMed1976

Models for biological ion exchangers. I. Proton magnetic resonance studies of water structure in Bio-Rex 70.

Bio-Rex 70, a carboxylic acid cation exchanger, is studied as a biological ion-exchanger resin model for cellular cytoplasm. High-resolution proton magnetic resonance spectra of 1l ionic forms of Bio-Rex 70 are determined. From measured cation exchange capacities, water contents, and chemical shifts for the resin-phase water protons, the dependence of the chemical shift on the counter ion is calculated. The observed chemical shifts (Hz/geq/kg internal water, referred to the Ba2+ form) for each ionic form are: H+, --0.5; Li+, --0.2; Na+, 1.2; K+, 2; Rb+, 2.1; Ag+, --0.4; NH4+, --2.0; NMe4+, 1.2; NEt4+, 1.8; Mg2+, --2.0; Ca2+, --1.5; Sr2+, --0.6; Ba2+, 0.0 Zn2+, --2.3; Cd2+, --4.7; and La3+, --3.3. The results are in good agreement with earlier studies on Dowex 50, indicating that the carboxylate ion exchanger behaves like a concentrated polyelectrolyte. The widths at half-height for the internal water peaks of the polyvalent forms are quite large, ranging from 40 to 100 Hz.

Cation Exchange Resins↗
PubMed2004

From a discrete to a continuous model of biological cell movement.

The process by which one may take a discrete model of a biophysical process and construct a continuous model based upon it is of mathematical interest as well as being of practical use. In this work, we take the extended Potts model applied to biological cell movement to its continuous limit. Beginning with a single cell moving in one dimension on a lattice and obeying Potts model rules of movement we develop an expression for the diffusion coefficient of a collection of noninteracting cells which depends explicitly on the Potts model parameters. We show how this coefficient varies when the Potts parameters for cell membrane elasticity and cell-medium adhesion are varied, and perform computer simulations which support our theoretical result. We explain the relationship between the probability of occupancy of lattice points and the density profile in the continuous limit, and extend our analysis by including interactions between the cells. In so doing we are able to develop a set of coupled ordinary differential equations showing the evolution of a density profile in the presence of significant cell-cell adhesion, and show how increases in the strength of this adhesion modulates diffusion. In so doing we develop some insights into how continuous models of physical systems can be based upon discrete models which describe the same system.

Animals↗
PubMed1989

Biologically motivated models for chemical risk assessment.

Assessing the risk associated with human exposure to environmental chemicals depends to a large extent on the ability to extrapolate from a particular range of exposure conditions in the test animal species to a very different range of exposure conditions in the human. One of the more promising tools for accomplishing this extrapolation is the biologically motivated pharmacokinetic/pharmacodynamic model. In a biologically motivated model, the structure is based on the physiological and biochemical structure of the animal system being described. This paper provides an overview of the biologically motivated modeling approach. Examples of models for styrene and methylene chloride are discussed in relation to their ability to predict human kinetics for these chemicals and their use in estimating the risk of chemicals to exposed humans. Finally, the use of a biologically motivated model to analyze the mechanistic basis of chemical carcinogenesis is discussed.

Animals↗
PubMed1996

Ontological foundations for biology knowledge models.

This paper analyzes the ontological requirements for representing biology knowledge, and identifies several areas where current knowledge representation (KR) paradigms need to be extended. We focus on the representation of experimental materials and methods, and the reasoning task of intelligent information retrieval; however, the ontological issues we raise apply to biology (and experimental sciences) in general. We have identified two important concept types in molecular biology that cause problems for standard knowledge models: 1) complex substances such as mixtures and nucleic acid sequences; 2) transformations (such as biochemical reactions) that convert one substances into another. We describe these problems, propose solutions for some of them, and given examples of the need for such knowledge representations in intelligent information retrieval.

Models, Biological↗
PubMed2004

Deconstructing milk yield and composition during lactation using biologically based lactation models.

A recently developed biological model of lactation described changes in daily milk yield throughout lactation as the result of 3 processes, secretory cell differentiation, cell death, and secretion rate per cell. This paper extends the model to describe the production of milk components (fat, protein, lactose, and water) throughout lactation by replacing milk secretion rate of the original model with the secretion rates of the four components. The milk component model approach was used to examine the relationship between milk yield and the major determinants of its production, using the secretion of milk components throughout lactation. Newly derived models were tested on 461 lactations from a single Holstein herd and used to estimate variability of secretion rates throughout lactation. Because the pattern of cell numbers throughout lactation is not precisely known, an alternative pattern of cell numbers was modeled and the concomitant change in secretion rates outlined. Fat secretion rate was the most variable, as measured by its weekly coefficient of variation throughout lactation. Secretion rates of lactose and water were nearly constant throughout lactation and highly correlated (0.94). Fat and protein secretion rates also were well correlated (0.53). The known biochemistry of milk component production related well to the secretion rate observations derived from the model. Lactose secretion rate and numbers of active secretory cells primarily determined daily milk yield.

Animals↗
PubMed2006

Robustness analysis of biochemical network models.

Biological systems that have been experimentally verified to be robust to significant changes in their environments require mathematical models that are themselves robust. In this context, a necessary condition for model robustness is that the model dynamics should not be sensitive to small variations in the model's parameters. Robustness analysis problems of this type have been extensively studied in the field of robust control theory and have been found to be very difficult to solve in general. The authors describe how some tools from robust control theory and nonlinear optimisation can be used to analyse the robustness of a recently proposed model of the molecular network underlying adenosine 3',5'-cyclic monophosphate (cAMP) oscillations observed in fields of chemotactic Dictyostelium cells. The network model, which consists of a system of seven coupled nonlinear differential equations, accurately reproduces the spontaneous oscillations in cAMP observed during the early development of D. discoideum. The analysis by the authors reveals, however, that very small variations in the model parameters can effectively destroy the required oscillatory dynamics. A biological interpretation of the analysis results is that correct functioning of a particular positive feedback loop in the proposed model is crucial to maintaining the required oscillatory dynamics.

Animals↗
PubMed2001

Density-dependent chemical interference--an extension of the biological response model.

The response of plants to many phytochemicals changes from stimulatory to inhibitory as the concentration of the phytochemical increases. In this paper, a previous biological response model is extended to yield estimates of plant responses to changes in phytochemical concentrations in the case of density-dependent phytotoxicity. This requires a knowledge of plant densities, phytochemical concentrations in soil, and the relationship between the two. According to this model extension, inhibition is a probable outcome in density-dependent chemical interference, but phytotoxic effects may become stimulatory as plant density increases. In addition, low phytochemical concentrations in soil may cause an increase in the slope of the biomass-density relationship compared to the slope of control treatments. Experimental data from the literature support this model extension, and in several cases density-dependent chemical interference can be estimated mathematically.

Adaptation, Physiological↗
PubMed2005

Geometric properties of a class of piecewise affine biological network models.

The purpose of this report is to investigate some dynamical properties common to several biological systems. A model is chosen, which consists of a system of piecewise affine differential equations. Such a model has been previously studied in the context of gene regulation and neural networks, as well as biochemical kinetics. Unlike most of these studies, nonuniform decay rates and several thresholds per variable are assumed, thus considering a more realistic model. This model is investigated with the aid of a geometric formalism. We first provide an analysis of a continuous-space, discrete-time dynamical system equivalent to the initial one, by the way of a transition map. This is similar to former studies. Especially, the analysis of periodic trajectories is carried out in the case of multiple thresholds, thus extending previous results, which all concerned the restricted case of binary systems. The piecewise affine structure of such models is then used to provide a partition of the phase space, in terms of explicit cells. Allowed transitions between these cells define a language on a finite alphabet. Some words are proved to be forbidden in this language, thus improving the knowledge on such systems in terms of symbolic dynamics. More precisely, we show that taking these forbidden words into account leads to a dynamical system with strictly lower topological entropy. This holds for a class of systems, characterized by the presence of a splitting box, with additional conditions. We conclude after an illustrative three-dimensional example.

Algorithms↗