Nonlinearity,coherence and complexity: Biophysical aspects related to health and disease

Biological organisms are complex open dissipative systems whose dynamical stability is sustained due to the exchange of matter, energy and information. Dynamical stability occurs through a number of mechanisms that sustain efficient adaptive dynamics. Such properties of living matter can be the consequence of a self-consistent state of matter and electromagnetic field (EMF). Based on the soliton model of charge transport in redox processes, we describe a possible mechanism of the origin of endogenous EMF and coherence. Solitons are formed in polypeptides due to electron–lattice interaction. Solitons experience periodical potential barrier, as a result of which their velocity oscillates in time, and, hence, they emit electromagnetic radiation (EMR). Under the effect of such radiation from all other solitons, the synchronization of their dynamics takes place, which significantly increases the intensity of the general EMF. The complex structure of biological molecules, such as helical structure, is not only important for “structure-function” relations, but also the source of the stability of biophysical processes, e.g. effectiveness of energy and charge transport on macroscopic distances. Such a complex structure also provides the framework for the spatiotemporal structure of the endogenous EMF. The highly hierarchical organization of living organisms is a manifestation of their complexity, even at the level of simple unicellular organisms. This complexity increases the dynamical stability of open systems and enhances the possibility of information storage and processing. Our findings provide a qualitative overview of a possible biophysical mechanism that supports health and disease adaptive dynamics.     

Molecular dynamics simulations of RNA: an in silico single molecule approach

RNA molecules are now known to be involved in the processing of genetic information at all levels, taking on a wide variety of central roles in the cell. Understanding how RNA molecules carry out their biological functions will require an understanding of structure and dynamics at the atomistic level, which can be significantly improved by combining computational simulation with experiment. This review provides a critical survey of the state of molecular dynamics (MD) simulations of RNA, including a discussion of important current limitations of the technique and examples of its successful application. Several types of simulations are discussed in detail, including those of structured RNA molecules and their interactions with the surrounding solvent and ions, catalytic RNAs, and RNA-small molecule and RNA-protein complexes. Increased cooperation between theorists and experimentalists will allow expanded judicious use of MD simulations to complement conceptually related single molecule experiments. Such cooperation will open the door to a fundamental understanding of the structure-function relationships in diverse and complex RNA molecules. .     

On the evolution and dynamics of biological networks.

Large numbers of interacting non-genic molecules regulate metabolism and embryonic morphogenesis through often unspecific mechanisms. This lack of specificity suggests that the prevailing viewpoint, that such ordered processes result from the direct control of genes and their products irrespective of local molecular dynamics, is incomplete. Proposed here is a hypothetical type of control dynamics, called indirect, that is exhibited in natural biological networks of interacting and adapting elements. Evidence in the literature suggests that ordinary interactions among such elements - including organisms, cells and molecules - produce six network phenomena that can be attributed to indirect-control dynamics. Although these hypotheses can be disproved, including by showing that the phenomena can be accounted for by an alternative process, the set of ecological dynamics argued to underlie the phenomena is observable, biologically consistent and universal. In contrast, the direct-control dynamics required by the modern synthesis likely is biologically disadvantageous. This biological view of networks suggests new areas of research.     

The effect of molecular inhibition on evolutionary learning: studies in the hypernetwork architecture

The hypernetwork architecture is a biologically inspired learning model based on abstract molecules and molecular interactions that exhibits functional and organizational correlation with biological systems. Hypernetwork organisms were trained, by molecular evolution, to solve N-input parity tasks. We found that learning improves when molecules exhibit inhibitory sites, allowing molecular inhibition and opening the possibility of forming negative feedback regulatory pathways. Optimal learning is achieved when at least 20% of the molecules in each cell have inhibitory sites. Intra-cellular as well as inter-cellular molecular inhibitions play an important role in the information processing of hypernetwork organisms, by maintaining a balance of the molecular cascade reactions. Similar mechanisms inside neurons are considered important for memory.     

Noise-reduction through interaction in gene expression and biochemical reaction processes

We demonstrate that interaction in gene expression and biochemical reaction processes has a significant influence on reducing fluctuations. Especially, we have found that the interaction between synthesized proteins and background molecules can reduce the fluctuation level in gene expression, which is a counter example to the intuition that background factors disturb information processing in genetic networks by increasing the noise level. This fact also indicates that the macromolecular crowding observed in actual cells can contribute to reduce the noise level. In addition, the noise-reduction phenomenon is not limited to the interaction between the proteins and background molecules, but can be applied to other reactions such as a dimerization process and the coupling of reactions with large fluctuations by intrinsic noise. Finally, on the basis of these results, we propose a new and plausible method for reducing the fluctuations generated in synthesized genetic networks, and also discuss the applicability of this method to the stabilization of system dynamics by using a toggle switch model.     

Independent alignment of RNA for dynamic studies using residual dipolar couplings

Molecular motion and dynamics play an essential role in the biological function of many RNAs. An important source of information on biomolecular motion can be found in residual dipolar couplings which contain dynamics information over the entire ms-ps timescale. However, these methods are not fully applicable to RNA because nucleic acid molecules tend to align in a highly collinear manner in different alignment media. As a consequence, information on dynamics that can be obtained with this method is limited. In order to overcome this limitation, we have generated a chimeric RNA containing both the wild type TAR RNA, the target of our investigation of dynamics, as well as the binding site for U1A protein. When U1A protein was bound to the portion of the chimeric RNA containing its binding site, we obtained independent alignment of TAR by exploiting the physical chemical characteristics of this protein. This technique can allow the extraction of new information on RNA dynamics, which is particularly important for time scales not covered by relaxation methods where important RNA motions occur.     

Integrated information increases with fitness in the evolution of animats

One of the hallmarks of biological organisms is their ability to integrate disparate information sources to optimize their behavior in complex environments. How this capability can be quantified and related to the functional complexity of an organism remains a challenging problem, in particular since organismal functional complexity is not well-defined. We present here several candidate measures that quantify information and integration, and study their dependence on fitness as an artificial agent ("animat") evolves over thousands of generations to solve a navigation task in a simple, simulated environment. We compare the ability of these measures to predict high fitness with more conventional information-theoretic processing measures. As the animat adapts by increasing its "fit" to the world, information integration and processing increase commensurately along the evolutionary line of descent. We suggest that the correlation of fitness with information integration and with processing measures implies that high fitness requires both information processing as well as integration, but that information integration may be a better measure when the task requires memory. A correlation of measures of information integration (but also information processing) and fitness strongly suggests that these measures reflect the functional complexity of the animat, and that such measures can be used to quantify functional complexity even in the absence of fitness data.     

How to build an information gathering and processing system: lessons from naturally and artificially intelligent systems

Imagine a situation in which you had to design a physical agent that could collect information from its environment, then store and process that information to help it respond appropriately to novel situations. What kinds of information should it attend to? How should the information be represented so as to allow efficient use and re-use? What kinds of constraints and trade-offs would there be? There are no unique answers. In this paper, we discuss some of the ways in which the need to be able to address problems of varying kinds and complexity can be met by different information processing systems. We also discuss different ways in which relevant information can be obtained, and how different kinds of information can be processed and used, by both biological organisms and artificial agents. We analyse several constraints and design features, and show how they relate both to biological organisms, and to lessons that can be learned from building artificial systems. Our standpoint overlaps with Karmiloff-Smith (1992) in that we assume that a collection of mechanisms geared to learning and developing in biological environments are available in forms that constrain, but do not determine, what can or will be learnt by individuals.     

Design of interacting multi-stable nucleic acids for molecular information processing

Despite an exponential increase in computing power over the past decades, present information technology falls far short of expectations in areas such as cognitive systems and micro robotics. Organisms demonstrate that it is possible to implement information processing in a radically different way from what we have available in present technology, and that there are clear advantages from the perspective of power consumption, integration density, and real-time processing of ambiguous data. Accordingly, the question whether the current silicon substrate and associated computing paradigm is the most suitable approach to all types of computation has come to the fore. Macromolecular materials, so successfully employed by nature, possess uniquely promising properties as an alternate substrate for information processing. The two key features of macromolecules are their conformational dynamics and their self-assembly capabilities. The purposeful design of macromolecules capable of exploiting these features has proven to be a challenge, however, for some groups of molecules it is increasingly practicable. We here introduce an algorithm capable of designing groups self-assembling of nucleic acid molecules with multiple conformational states. Evaluation using natural and artificially designed nucleic acid molecules favours this algorithm significantly, as compared to the probabilistic approach. Furthermore, the thermodynamic properties of the generated candidates are within the same approximation as the customised trans-acting switching molecules reported in the laboratory.     

A method for imaging single molecules at the plasma membrane of live cells within tissue slices

Recent advances in light microscopy allow individual biological macromolecules to be visualized in the plasma membrane and cytosol of live cells with nanometer precision and ∼10-ms time resolution. This allows new discoveries to be made because the location and kinetics of molecular interactions can be directly observed in situ without the inherent averaging of bulk measurements. To date, the majority of single-molecule imaging studies have been performed in either unicellular organisms or cultured, and often chemically fixed, mammalian cell lines. However, primary cell cultures and cell lines derived from multi-cellular organisms might exhibit different properties from cells in their native tissue environment, in particular regarding the structure and organization of the plasma membrane. Here, we describe a simple approach to image, localize, and track single fluorescently tagged membrane proteins in freshly prepared live tissue slices and demonstrate how this method can give information about the movement and localization of a G protein–coupled receptor in cardiac tissue slices. In principle, this experimental approach can be used to image the dynamics of single molecules at the plasma membrane of many different soft tissue samples and may be combined with other experimental techniques.     

Phagocytic processing of antigens for presentation by MHC molecules

Phagocytosis plays a major role in the defence of higher organisms against microbial infection not only by allowing ingested microbes to be destroyed by microbicidal mechanisms, but also by providing the basis for processing of their antigens to forms that generate immune responses. This article examines the role of the phagolysosome in antigen processing, and discusses the contributions of both MHC class II and MHC class I molecules to the presentation of antigens derived from phagocytosed material.     

Fast flexible modeling of RNA structure using internal coordinates

Modeling the structure and dynamics of large macromolecules remains a critical challenge. Molecular dynamics (MD) simulations are expensive because they model every atom independently, and are difficult to combine with experimentally derived knowledge. Assembly of molecules using fragments from libraries relies on the database of known structures and thus may not work for novel motifs. Coarse-grained modeling methods have yielded good results on large molecules but can suffer from difficulties in creating more detailed full atomic realizations. There is therefore a need for molecular modeling algorithms that remain chemically accurate and economical for large molecules, do not rely on fragment libraries, and can incorporate experimental information. RNABuilder works in the internal coordinate space of dihedral angles and thus has time requirements proportional to the number of moving parts rather than the number of atoms. It provides accurate physics-based response to applied forces, but also allows user-specified forces for incorporating experimental information. A particular strength of RNABuilder is that all Leontis-Westhof basepairs can be specified as primitives by the user to be satisfied during model construction. We apply RNABuilder to predict the structure of an RNA molecule with 160 bases from its secondary structure, as well as experimental information. Our model matches the known structure to 10.2 Angstroms RMSD and has low computational expense.     

Linear-nonlinear cascades capture synaptic dynamics

Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.     

Designs,applications, and limitations of genetically encoded fluorescent sensors to explore plant biology

The understanding of signaling and metabolic processes in multicellular organisms requires knowledge of the spatial dynamics of small molecules and the activities of enzymes, transporters, and other proteins in vivo, as well as biophysical parameters inside cells and across tissues. The cellular distribution of receptors, ligands, and activation state must be integrated with information about the cellular distribution of metabolites in relation to metabolic fluxes and signaling dynamics in order to achieve the promise of in vivo biochemistry. Genetically encoded sensors are engineered fluorescent proteins that have been developed for a wide range of small molecules, such as ions and metabolites, or to report biophysical processes, such as transmembrane voltage or tension. First steps have been taken to monitor the activity of transporters in vivo. Advancements in imaging technologies and specimen handling and stimulation have enabled researchers in plant sciences to implement sensor technologies in intact plants. Here, we provide a brief history of the development of genetically encoded sensors and an overview of the types of sensors available for quantifying and visualizing ion and metabolite distribution and dynamics. We further discuss the pros and cons of specific sensor designs, imaging systems, and sample manipulations, provide advice on the choice of technology, and give an outlook into future developments.

Different types of genetically encoded sensors in plants can be used to quantify and visualize ion and metabolite distributions and dynamics.     

Nonlinear transient computation as a potential "kernel trick" in cortical processing

Evidence has been found for the presence of chaotic dynamics at all levels of the mammalian brain. This has led to some searching questions about the potential role that nonlinear dynamics may have in neural information processing. We propose that chaos equips the brain with the equivalent of a kernel trick for solving hard nonlinear problems. The approach presented, which is described as nonlinear transient computation, uses the dynamics of a well known chaotic attractor. The paper provides experimental results to show that this approach can be used to solve some challenging pattern recognition tasks. The paper also offers evidence to suggest that the efficacy of nonlinear transient computation for nonlinear pattern classification is dependent only on the generic properties of chaotic attractors and is not sensitive to the particular dynamics of specific sub-regions of chaotic phase space. If, as this work suggests, nonlinear transient computation is independent of the particulars of any given chaotic attractor, then it could be offered as a possible explanation of how the chaotic dynamics that have been observed in brain structures contribute to neural information processing tasks.     

NCBI Reference Sequence project: update and current status

The goal of the NCBI Reference Sequence (RefSeq) project is to provide the single best non-redundant and comprehensive collection of naturally occurring biological molecules, representing the central dogma. Nucleotide and protein sequences are explicitly linked on a residue-by-residue basis in this collection. Ideally all molecule types will be available for each well-studied organism, but the initial database collection pragmatically includes only those molecules and organisms that are most readily identified. Thus different amounts of information are available for different organisms at any given time. Furthermore, for some organisms additional intermediate records are provided when the genome sequence is not yet finished. The collection is supplied by NCBI through three distinct pipelines in addition to collaborations with community groups. The collection is curated on an ongoing basis. Additional information about the NCBI RefSeq project is available at http://www.ncbi.nih.gov/RefSeq/.     

Basic module for an adaptive control system based on neurone information processing

The design of such devices as robotic aids for handicapped people, powered prostheses and manipulative aids such as page turners would benefit from the use of an adaptive control system. Much recent work on adaptive networks has been based on simplified models of the information processing capabilities of neurones. Neurones are now known to be capable of association learning and memory and this study incorporates these features in a neurone model. A single neuronal input system, the NMDA-type glutamate receptor, is modelled by deriving finite difference equations from its reaction dynamics so that the concentration of several molecules in the receptor can be plotted as a function of time. The model shows association learning taking place at the glutamate receptor. A whole neurone with ten glutamate receptor regions is also modelled and shows that a neurone should be capable of recognizing patterns of inputs. As the neurone model is complicated and slow to run, a much simplified form of the model is described which embodies the basic features of neurone information processing in a simple algorithm.