Use of hybrid computers to analyze behavior of detailed models for biological systems. I. Model for aortic blood flow.
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Aorta↗
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Results for “Models, Biological”
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A framework for models of biological behaviour.
Modelling is most clearly understood as a adjunct in the process of deriving predictions from hypotheses. By representing a hypothesised mechanism in a model we hope by manipulating the model to understand the hypotheses' consequences. Eight dimensions on which models of biological behaviour can vary are described: the degree of realism with which they apply to biology; the level of biology they represent; the generality or range of systems the model is supposed to cover; the abstraction or amount of biological detail represented; the accuracy of representation of the mechanisms; the medium in which the model is built; the match of the model behaviour to biological behaviour; and the utility of the model in providing biological understanding and/or technical insight. It is hoped this framework will help to clarify debates over different approaches to modelling, particularly by pointing out how the above dimensions are relatively independent and should not be conflated.
Animals↗
Calibration of model constants in a biological reaction model for sewage treatment plants.
Various biological reaction models have been proposed which estimate concentrations of soluble and insoluble components in effluent of sewage treatment plants. These models should be effective to develop a better operation system and plant design, but their formulas consist of nonlinear equations, and there are many model constants, which are not easy to calibrate. A technique has been proposed to decide the model constants by precise experiments, but it is not practical for design engineers or process operators to perform these experiments regularly. Other approaches which calibrate the model constants by mathematical techniques should be used. In this paper, the optimal regulator method of modern control theory is applied as a mathematical technique to calibrate the model constants. This method is applied in a small sewage treatment testing facility. Calibration of the model constants is examined to decrease the deviations between calculated and measured concentrations. Results show that calculated values of component concentrations approach measured values and the method is useful for actual plants.
Calibration↗
Modeling and experiment in developmental biology.
Models in developmental biology continue to yield valuable insights, yet do not play a strong role as guides to experiment. This may be because of the largely unexplored complexity of most developing organisms, and the fact that the most powerful models work at a very abstract level. In Dictyostelium discoideum, however, a fully formed model incorporating detailed experimental results is now available.
Animals↗
Algorithms for inferring qualitative models of biological networks.
Modeling genetic networks and metabolic networks is an important topic in bioinformatics. We propose a qualitative network model which is a combination of the Boolean network and qualitative reasoning, where qualitative reasoning is a kind of reasoning method well-studied in Artificial Intelligence. We also present algorithms for inferring qualitative networks from time series data and an algorithm for inferring S-systems (synergistic and saturable systems) from time series data, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems.
Algorithms↗
Have we entered a 'post-model' era in plant biology?
Models such as arabidopsis (Arabidopsis thaliana) have underpinned genomic and physiological research in plant science. Advances in genome sequencing, pangenomics, and genome editing have prompted claims of a 'post-model' era, with model-crops and crops such as rice and bread wheat combining agricultural relevance with experimental tractability. We argue that the 'simplicity-to-complexity' approach remains valid, although model systems have evolved. Arabidopsis remains indispensable for interpreting multi-omics data, testing developmental hypotheses, and generating mechanistic insights difficult to obtain in crops. Linking these strengths to model-crops adds translational value by bridging discovery and breeding, while niche models such as Brachypodium distachyon and legumes address grass cell wall biology and nitrogen fixation. Future progress depends on diverse species with complementary strengths across fundamental and applied plant biology.
arabidopsis↗
Constructing biological pathway models with hybrid functional Petri nets.
In many research projects on modeling and analyzing biological pathways, the Petri net has been recognized as a promising method for representing biological pathways. From the pioneering works by Reddy et al., 1993, and Hofestädt, 1994, that model metabolic pathways by traditional Petri net, several enhanced Petri nets such as colored Petri net, stochastic Petri net, and hybrid Petri net have been used for modeling biological phenomena. Recently, Matsuno et al., 2003b, introduced the hybrid functional Petri net (HFPN) in order to give a more intuitive and natural modeling method for biological pathways than these existing Petri nets. Although the paper demonstrates the effectiveness of HFPN with two examples of gene regulation mechanism for circadian rhythms and apoptosis signaling pathway, there has been no detailed explanation about the method of HFPN construction for these examples. The purpose of this paper is to describe method to construct biological pathways with the HFPN step-by-step. The method is demonstrated by the well-known glycolytic pathway controlled by the lac operon gene regulatory mechanism.
Adenosine Diphosphate↗
Is a fluid-mosaic model of biological membranes fully relevant? Studies on lipid organization in model and biological membranes.
The basic concept of the fluid-mosaic model of Singer and Nicolson, an essential point of which is that the membrane proteins are floating in a sea of excess lipid molecules organized in the lipid bilayer, may be misleading in understanding the movement of membrane components in biological membranes that show distinct domain structure. It seems that the lipid bilayer is an active factor in forming the membrane structure, and the lipid composition is responsible for the presence of domains in the membrane. The main role in the process of domain formation is played by cholesterol and sphingolipids. The results presented here show that in a binary mixture of cholesterol and unsaturated phospholipids, cholesterol is segregated out from the bulk unsaturated liquid-crystalline phase. This forms cholesterol-enriched domains or clustered cholesterol domains due to the lateral nonconformability between the rigid planar ring structure of cholesterol and the rigid bend of the unsaturated alkyl chain at double bond position. These cholesterol-enriched domains may be stabilized by the presence of saturated alkyl chains of sphingomyelin or glycosphingolipids, and also by specific proteins which selectively locate in these domains and stabilize them as a result of protein-protein interaction. Such lipid domains are called "rafts" and have been shown to be responsible both for signal transduction to and from the cell and for protein sorting. We also looked at whether polar carotenoids, compounds showing some similarities to cholesterol and affecting membrane properties in a similar way, would also promote domain formation and locate preferentially in one of the lipid phases. Our preliminary data show that in the presence of cholesterol, lutein (a polar carotenoid) may segregate out from saturated lipid regions (liquid-ordered phase) and accumulate in the regions rich in unsaturated phospholipids forming carotenoid-rich domains there. Conventional and pulse EPR (electron paramagnetic resonance) spin labeling techniques were employed to assess the molecular organization and dynamics of the raft-constituent molecules and of the raft itself in the membrane.
Carotenoids↗
The fluid double polar-nonpolar-polar leaflet model for biological membranes.
A model for biological membranes is proposed according to which the plasma membrane consists of two functionally different polar-nonpolar-polar leaflets separated by a polar space. The binding of water-soluble proteins, integral lipoproteins and spanning proteins to a biological membrane as well as possible conformations of interphase peptides partitioned between polar and nonpolar layers are discussed. A model for the diffusion of water soluble proteins across nonpolar layers of a membrane is described. Three complete biological membranes containing two leaflets and an inter-leaflet space are defined. These are: 1: The inner nuclear membrane + the perinuclear space and the endoplasmatic cisternae + the outer nuclear membrane and the endoplasmatic reticulum, 2: the innner mitochondrial membrane + the mitochondrial intermembraneous space + the outer mitochondrial membrane and 3: the cytoplasmic leaflet of the plasma membrane + an intramembraneous space in the plasma membrane + the outer leaflet of the plasma membrane.
Biological Transport↗
Dynamic structure of biological and model membranes: analysis by optical anisotropy decay measurement.
Rotational Brownian motion of molecules in membranes can be "visualized" by time-resolved measurement of the decay of anisotropy of various flash-induced optical signals, such as fluorescence, phosphorescence, delayed fluorescence, transient absorption, or fluorescence depletion. The basic principles of the various forms of anisotropy measurement are illustrated in a unified manner. In organized structures such as membranes, rotational motion is restricted in angular range. Methods of analysis of observed optical anisotropy decays for the case of restricted rotation are described; the emphasis is laid on the separate estimation of the two important parameters, the range and rate, that characterize the restricted rotation. Practical aspects of the analytical procedures are also discussed. As an example of application, recent work from the authors' laboratory is reviewed: dynamic structures of lipid hydrocarbon chain region of membranes have been revealed by time-resolved fluorescence depolarization studies. A lipophilic fluorescent probe 1,6-diphenyl-1,3,5-hexatriene was incorporated in model and biological membranes of known compositions. The decay of fluorescence anisotropy indicated that the rod-shaped probe molecules wobbled in the membranes with a wobbling diffusion constant around 0.1 rad2/nsec, presumably reflecting the dynamics of surrounding lipid chains. The effects of temperature, ions, lipid chain unsaturation, cholesterol, and protein on the range and rate of wobbling were examined with model membranes. The dynamic structure of biological membranes was found to be basically similar to that of the bilayer of unsaturated phospholipid; proteins and cholesterol act mainly as barriers that reduce the angular range of wobbling motion.
Animals↗
A method for parameter optimization in computational biology.
Models in computational biology, such as those used in binding, docking, and folding, are often empirical and have adjustable parameters. Because few of these models are yet fully predictive, the problem may be nonoptimal choices of parameters. We describe an algorithm called ENPOP (energy function parameter optimization) that improves-and sometimes optimizes-the parameters for any given model and for any given search strategy that identifies the stable state of that model. ENPOP iteratively adjusts the parameters simultaneously to move the model global minimum energy conformation for each of m different molecules as close as possible to the true native conformations, based on some appropriate measure of structural error. A proof of principle is given for two very different test problems. The first involves three different two-dimensional model protein molecules having 12 to 37 monomers and four parameters in common. The parameters converge to the values used to design the model native structures. The second problem involves nine bumpy landscapes, each having between 4 and 12 degrees of freedom. For the three adjustable parameters, the globally optimal values are known in advance. ENPOP converges quickly to the correct parameter set.
Algorithms↗
Lipid domains in model and biological membranes.
Lipid domains that occur within biological of model membranes encompass a variety of structures with very different lifetimes. The separation of membrane lipids into compositional domains can be due to lateral phase separation, immiscibility within a single phase, or interaction of lipids with integral or peripheral proteins. Lipid domains can affect the extent and rate of reactions in the membrane and provide sites for the activity of specialized proteins. Domains are likely to be involved in the process of lipid sorting to various cellular membranes, as well as in other processes which involve membrane budding or invagination.
Animals↗
Mechanisms of carcinogenesis and biologically based models for estimation and prediction of risk.
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Animals↗
[Evaluation of mathematical models for biological research. The mathematical model of the functional tests on the blood glucose regulating system].
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Animals↗
[Biological therapy model for trigeminal neuralgia].
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Acupuncture Therapy↗
Simple stochastic fingerprints towards mathematical modeling in biology and medicine 2. Unifying Markov model for drugs side effects.
Most of present mathematical models for biological activity consider just the molecular structure. In the present article we pretend extending the use of Markov chain models to define novel molecular descriptors, which consider in addition other parameters like target site or biological effect. Specifically, this mathematical model takes into consideration not only the molecular structure but the specific biological system the drug affects too. Herein, a general Markov model is developed that describes 19 different drugs side effects grouped in eight affected biological systems for 178 drugs, being 270 cases finally. The data was processed by linear discriminant analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 100/95.8% for endocrine manifestations, (18 out of 18)/(13 out of 14); 90.5/92.3% for gastrointestinal manifestations, (38 out of 42)/(30 out of 32); 88.5/86.5% for systemic phenomena, (23 out of 26)/(17 out of 20); 81.8/77.3% for neurological manifestations, (27 out of 33)/(19 out of 25); 81.6/86.2% for dermal manifestations, (31 out of 38)/(25 out of 29); 78.4/85.1% for cardiovascular manifestation, (29 out of 37)/(24 out of 28); 77.1/75.7% for breathing manifestations, (27 out of 35)/(20 out of 26) and 75.6/75% for psychiatric manifestations, (31 out of 41)/(23 out of 31). Additionally a back-projection analysis (BPA) was carried out for two ulcerogenic drugs to prove in structural terms the physical interpretation of the models obtained. This article develops a mathematical model that encompasses a large number of drugs side effects grouped in specifics biological systems using stochastic absolute probabilities of interaction ((A)pi(k)(j)) by the first time.
Drug-Related Side Effects and Adverse Reactions↗
The two-stage clonal expansion model as an example of a biologically based model of radiation-induced cancer.
A model with two stages and clonal expansion (TSCE) is reviewed as a prototype for biologically based models of cancer development. Applications of the TSCE model to data sets for animals and humans for particle radiation (alpha particles) are presented. The results suggest that the radiation not only influences the initiating mutation, but may also act as a promoter. A possible mechanism for the promoting action is described. The consequences of these results for the shapes of the radiation dose-response curves at low doses and dose rates are discussed.
Alpha Particles↗
A neuropsychiatric model of biological and psychological processes in the remission of delusions and auditory hallucinations.
This selective review combines cognitive models and biological models of psychosis into a tentative integrated neuropsychiatric model. The aim of the model is to understand better, how pharmacotherapy and cognitive-behavior therapy come forward as partners in the treatment of psychosis and play complementary and mutually reinforcing roles. The article reviews the dominant models in literature. The focus in this review is on one hand on neural circuits that are involved in cognitive models and on the other hand on cognitive processes and experiences involved in biological models. In this way, a 4-component neuropsychiatric model is tentatively constructed: (1) a biological component that leads to aberrant perceptions and salience of stimuli, (2) a cognitive component that attempts to explain the psychic abnormal events, (3) a mediating component with psychological biases which influences the reasoning process in the direction of the formation of (secondary) delusions, and (4) a component of psychological processes that maintains delusions and prevents the falsification of delusional ideas. Remission consists actually of 2 processes. Biological remission consists of the dampening of mesolimbic dopamine releases with antipsychotic medication and decreases the continuous salient experiences. Psychological remission consists of the reappraisal of primary psychotic experiences. Both forms of remission are partially independent. We expect that a full remission including biological and psychological remission could prevent relapse.
Cognitive Behavioral Therapy↗