The significance of lipid composition for membrane activity: new concepts and ways of assessing function

In the last decade or so, it has been realised that membranes do not just have a lipid-bilayer structure in which proteins are embedded or with which they associate. Structures are dynamic and contain areas of heterogeneity which are vital for their formation. In this review, we discuss some of the ways in which these dynamic and heterogeneous structures have implications during stress and in relation to certain human diseases. A particular stress is that of temperature which may instigate adaptation in poikilotherms or appropriate defensive responses during fever in mammals. Recent data emphasise the role of membranes in sensing temperature changes and in controlling a regulatory loop with chaperone proteins. This loop seems to need the existence of specific membrane microdomains and also includes association of chaperone (heat stress) proteins with the membrane. The role of microdomains is then discussed further in relation to various human pathologies such as cardiovascular disease, cancer and neurodegenerative diseases. The concept of modifying membrane lipids (lipid therapy) as a means for treating such pathologies is then introduced. Examples are given when such methods have been shown to have benefit. In order to study membrane microheterogeneity in detail and to elucidate possible molecular mechanisms that account for alteration in membrane function, new methods are needed. In the second part of the review, we discuss ultra-sensitive and ultra-resolution imaging techniques. These include atomic force microscopy, single particle tracking, single particle tracing and various modern fluorescence methods. Finally, we deal with computing simulation of membrane systems. Such methods include coarse-grain techniques and Monte Carlo which offer further advances into molecular dynamics. As computational methods advance they will have more application by revealing the very subtle interactions that take place between the lipid and protein components of membranes - and which are so essential to their function.     

Structure-based computational analysis of protein binding sites for function and druggability prediction

Protein binding sites are the places where molecular interactions occur. Thus, the analysis of protein binding sites is of crucial importance to understand the biological processes proteins are involved in. Herein, we focus on the computational analysis of protein binding sites and present structure-based methods that enable function prediction for orphan proteins and prediction of target druggability. We present the general ideas behind these methods, with a special emphasis on the scopes and limitations of these methods and their validation. Additionally, we present some successful applications of computational binding site analysis to emphasize the practical importance of these methods for biotechnology/bioeconomy and drug discovery.     

Identifying responsive functional modules from protein-protein interaction network

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.     

Process, structure and context in relation to integrative biology.

This paper seeks to provide some integrative tools of thought regarding biological function related to ideas of process, structure, and context. The incorporation of linguistic and mathematical thinking is discussed within the context of managing thinking about natural systems as described by Robert Rosen. Examples from ecology, protein networks, and liver function are introduced to illustrate key ideas. It is hoped that these tools of thought, and the further work needed to mobilise such ideas, will continue to address a number of issues raised and pursued by Michael Conrad, such as the seed-germination model and vertical information processing.     

Structural and regulatory evolution of cellular electrophysiological systems

SUMMARY Cellular electrophysiological systems, like developmental systems, appear to evolve primarily by means of regulatory evolution. It is suggested that electrophysiological systems share two key features with developmental systems that account for this dependence on regulatory evolution. For both systems, structural evolution has the potential to create significant problems of pleiotropy and both systems are predominantly computational in nature. It is concluded that the relative balance of physical and computational tasks that a biological system has to perform, combined with the probability that these tasks may have to change significantly during the course of evolution, will be major factors in determining the relative mix of regulatory and structural evolution that is observed for a given system. Physiological systems that directly interface with the environment will almost always perform some low-level physical task. In the majority of cases this will require evolution of protein function in order for the tasks themselves to evolve. For complex physiological systems a large fraction of their function will be devoted to high-level control functions that are predominantly computational in nature. In most cases regulatory evolution will be sufficient in order for these computational tasks to evolve.     

(In search of) probabilistic P systems

The aim of this paper is to (preliminarily) discuss various ways of introducing probabilities in membrane systems. We briefly present both ideas already circulated in the literature and new proposals, trying to have a systematic overview of possibilities of associating probabilities with the ingredients of a membrane system: with (localization of) single objects, with multiplicities of objects (hence with the multisets), with the rules (depending or not on the previous applied rule), with the communication targets. For a certain mode of using the probabilities associated with the evolution rules (in string-object P systems) we obtain the computational universality.     

Stability,sensitivity and uncertainty rates in the flow equations of ecological models

In previous work by the authors about dynamic system modeling, basic ecosystems concepts and their application to ecological modeling theory were formalized. Measuring how a variable effects certain processes leads to improvements in dynamic systems modelling and facilitates the author’s study of system diversity in which model sensitivity is a key theme. Initially, some variables and their numeric data are used for modeling. Predictions from the constructed models depend on these data. Generic study of sensitivity aims to show to what degree model behavior is altered by modification of some specific data. If small variations cause important modifications in the model’s global behavior then the model is very sensitive in relation to the variables used. If uncertain systems are considered, then it is important to submit the system to extreme situations and analyze its behavior. In this article, some indexes of uncertainty will be defined in order to determine the variables' influence in the case of extreme changes. This will permit analysis of the system’s sensitivity in relation to several simulations.     

Is there a biology of quantum information?

This paper briefly considers the notion of a biology of quantum information from a number of complementary points of view. We begin with a very brief look at some of the biomolecular systems that are thought to exploit quantum mechanical effects and then turn to the issue of measurement in these systems and the concomitant generation of information. This leads us to look at the internalist stance and the exchange interaction of quantum particles. We suggest that exchange interaction can also be viewed using ecological ideas related to apparatus-object. This can also help develop the important notion of complementarity in biosystems in relation to the nature and generation of information at the microphysical scale.     

A hybrid model framework for the optimization of preparative chromatographic processes

An optimization framework based on the use of hybrid models is presented for preparative chromatographic processes. The first step in the hybrid model strategy involves the experimental determination of the parameters of the physical model, which consists of the full general rate model coupled with the kinetic form of the steric mass action isotherm. These parameters are then used to carry out a set of simulations with the physical model to obtain data on the functional relationship between various objective functions and decision variables. The resulting data is then used to estimate the parameters for neural-network-based empirical models. These empirical models are developed in order to enable the exploration of a wide variety of different design scenarios without any additional computational requirements. The resulting empirical models are then used with a sequential quadratic programming optimization algorithm to maximize the objective function, production rate times yield (in the presence of solubility and purity constraints), for binary and tertiary model protein systems. The use of hybrid empirical models to represent complex preparative chromatographic systems significantly reduces the computational time required for simulation and optimization. In addition, it allows both multivariable optimization and rapid exploration of different scenarios for optimal design.     

Computational models of cells and tissues: machines, agents and fungal infection

Computational models have been of interest in biology for many years and have represented a particular approach to trying to understand biological processes and phenomena from a systems point of view. Much of the early work was rather abstract and high level and probably seemed to many to be of more philosophical than practical value. There have, however, been some advances in the development of more realistic models and the current state of computer science research provides us with new opportunities through both the emergence of models that can model seriously complex systems and also the support that modern software can give to the modelling process. This paper describes a few of the early simple models and then goes on to look at some new ideas in the area with a particular application drawn from the world of mycology. Some general principles relating to how new and emerging computational techniques can help to represent and understand extremely complex models conclude the paper.     

The erythroid cells of anaemic Xenopus laevis. II. Studies on nuclear non-histone proteins.

The mass ratio of nuclear non-histone protein: DNA in the immature circulating erythroid cells of phenylhydrazine-induced anaemic Xenopus is approximately threefold higher than in mature erythrocytes. This is largely due to the presence of increased amounts of low and intermediate molecular weight proteins in the nuclei of the immature cells. There are a few qualitative differences in the components of this class of proteins between the mature and immature cells, the most notable of which is the presence of a protein of molecular weight approximately 115000 in the former which is not detectable in the latter. These changes are discussed in relation to the changing synthetic capacities of cells and to certain generalizations about the function of the nuclear non-histone protein based on studies of other differentiating systems.     

Central nervous system and computation

Computational systems are useful in neuroscience in many ways. For instance, they may be used to construct maps of brain structure and activation, or to describe brain processes mathematically. Furthermore, they inspired a powerful theory of brain function, in which the brain is viewed as a system characterized by intrinsic computational activities or as a "computational information processor. "Although many neuroscientists believe that neural systems really perform computations, some are more cautious about computationalism or reject it. Thus, does the brain really compute? Answering this question requires getting clear on a definition of computation that is able to draw a line between physical systems that compute and systems that do not, so that we can discern on which side of the line the brain (or parts of it) could fall. In order to shed some light on the role of computational processes in brain function, available neurobiological data will be summarized from the standpoint of a recently proposed taxonomy of notions of computation, with the aim of identifying which brain processes can be considered computational. The emerging picture shows the brain as a very peculiar system, in which genuine computational features act in concert with noncomputational dynamical processes, leading to continuous self-organization and remodeling under the action of external stimuli from the environment and from the rest of the organism.     

Numerical Parameter Space Compression and Its Application to Biophysical Models

Physical models of biological systems can become difficult to interpret when they have a large number of parameters. But the models themselves actually depend on (i.e., are sensitive to) only a subset of those parameters. This phenomenon is due to parameter space compression (PSC), in which a subset of parameters emerges as “stiff” as a function of time or space. PSC has only been used to explain analytically solvable physics models. We have generalized this result by developing a numerical approach to PSC that can be applied to any computational model. We validated our method against analytically solvable models of a random walk with drift and protein production and degradation. We then applied our method to a simple computational model of microtubule dynamic instability. We propose that numerical PSC has the potential to identify the low-dimensional structure of many computational models in biophysics. The low-dimensional structure of a model is easier to interpret and identifies the mechanisms and experiments that best characterize the system.     

Spatio-logical processes in intracellular signalling

Classical models of intracellular signalling describe how small changes in a cell's external environment can bring about major changes in cellular activity. Recent findings from experimental biology indicate that many intracellular signalling systems show a high level of spatial organisation. This permits the modification, by protein kinase or protein phosphatase action, of specific subsets of intracellular proteins - an attribute that is not addressed in classical signalling models. Here we use ideas and concepts from computer science to describe the information processing nature of intracellular signalling pathways and the impact of spatial heterogeneity of their components (e.g. protein kinases and protein phosphatases) on signalling activity. We argue that it is useful to view the signalling ecology as a vast parallel distributed processing network of agents operating in heterogeneous microenvironments, and we conclude with an overview of the mathematical and semantic methodologies that might help clarify this analogy between biological and computational systems.     

Membrane protein prediction methods

We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling. In general, predictions of functional and structural features of membrane proteins are improving, although progress is hampered by the limited amount of high-resolution experimental information available. While predictions of transmembrane segments and protein topology rank among the most accurate methods in computational biology, more attention and effort will be required in the future to ameliorate database search, homology and ab initio modeling.     

Optimal systems: I. The vascular system

An approach to quantitative work on optimal systems is considered. Desired optimal principles are utilized in constructing a hypothetical system similar to the organ system considered. A comparison of this constructed system with the anatomical system then gives an indication of the importance of the optimal principles in the form or function of the organ system considered. These ideas are applied to the mammalian vascular system, and limiting values are obtained for some of its important component parts. The constructed system gives good agreement with anatomical data for vessel radii, lengths, and hydrodynamic resistance to flow.     

Function prediction of uncharacterized proteins

Function prediction of uncharacterized protein sequences generated by genome projects has emerged as an important focus for computational biology. We have categorized several approaches beyond traditional sequence similarity that utilize the overwhelmingly large amounts of available data for computational function prediction, including structure-, association (genomic context)-, interaction (cellular context)-, process (metabolic context)-, and proteomics-experiment-based methods. Because they incorporate structural and experimental data that is not used in sequence-based methods, they can provide additional accuracy and reliability to protein function prediction. Here, first we review the definition of protein function. Then the recent developments of these methods are introduced with special focus on the type of predictions that can be made. The need for further development of comprehensive systems biology techniques that can utilize the ever-increasing data presented by the genomics and proteomics communities is emphasized. For the readers' convenience, tables of useful online resources in each category are included. The role of computational scientists in the near future of biological research and the interplay between computational and experimental biology are also addressed.     

Model reduction using a posteriori analysis

Mathematical models in biology and physiology are often represented by large systems of non-linear ordinary differential equations. In many cases, an observed behaviour may be written as a linear functional of the solution of this system of equations. A technique is presented in this study for automatically identifying key terms in the system of equations that are responsible for a given linear functional of the solution. This technique is underpinned by ideas drawn from a posteriori error analysis. This concept has been used in finite element analysis to identify regions of the computational domain and components of the solution where a fine computational mesh should be used to ensure accuracy of the numerical solution. We use this concept to identify regions of the computational domain and components of the solution where accurate representation of the mathematical model is required for accuracy of the functional of interest. The technique presented is demonstrated by application to a model problem, and then to automatically deduce known results from a cell-level cardiac electrophysiology model.