"Either-or" two-slit interference: stable coherent propagation of individual photons through separate slits

In quantum theory, nothing that is observable, be it physical, chemical, or biological, is separable from the observer. Furthermore, ". all possible knowledge concerning that object is given by its wave function" (Wigner, E. 1967. Symmetries and Reflections. Indiana University Press, Bloomington, IN), which can only describe probabilities of future events. In physical systems, quantum mechanical probabilistic events that are microscopic must, in turn, account for macroscopic events that are associated with a greater degree of certainty. In biological systems, probabilistic statistical mechanical events, such as secretion of microscopic synaptic vesicles, must account for macroscopic postsynaptic potentials; probabilistic single-channel events sum to produce a macroscopic ionic current across a cell membrane; and bleaching of rhodopsin molecules (responsible for quantal potential "bumps") produces a photoreceptor generator potential. Among physical systems, a paradigmatic example of how quantum theory applies to the observation of events concerns the interactions of particles (e.g., photons, electrons) with the two-slit apparatus to generate an interference pattern from a single common light source. For two-slit systems that use two independent laser sources with brief (<1 ms) intervals of mutual coherence (Paul, H. 1986. Rev. Modern Phys. 58:209-231), each photon has been considered to arise from both beams and has a probability amplitude to pass through each of the two slits. Here, a single laser source two-slit interference system was constructed so that each photon has a probability amplitude to pass through only one or the other, but not both slits. Furthermore, all photons passing through one slit could be distinguished from all photons passing through the other slit before their passage. This "either-or" system produced a stable interference pattern indistinguishable from the interference produced when both slits were accessible to each photon. Because this system excludes the interaction of one photon with both slits, phase correlation of photon movements derives from the "entanglement" of all photon wave functions due to their dependence on a common laser source. Because a laser source (as well as Young's original point source) will have stable time-averaged spatial coherence even at low intensities, the "either-or" two-slit interference can result from distinct individual photons passing one at a time through one or the other slit-rather than wave-like behavior of individual photons. In this manner, single, successive photons passing through separate slits will assemble over time in phase-correlated wave distributions that converge in regions of low and high probability.     

Quantum-like brain: "Interference of minds"

We present a contextualist statistical realistic model for quantum-like representations in physics, cognitive science, and psychology. We apply this model to describe cognitive experiments to check quantum-like structures of mental processes. The crucial role is played by interference of probabilities for mental observables. Recently one such experiment based on recognition of images was performed. This experiment confirmed our prediction on the quantum-like behavior of mind. In our approach "quantumness of mind" has no direct relation to the fact that the brain (as any physical body) is composed of quantum particles. We invented a new terminology "quantum-like (QL) mind." Cognitive QL-behavior is characterized by a nonzero coefficient of interference lambda. This coefficient can be found on the basis of statistical data. There are predicted not only cos theta-interference of probabilities, but also hyperbolic cosh theta-interference. This interference was never observed for physical systems, but we could not exclude this possibility for cognitive systems. We propose a model of brain functioning as a QL-computer (there is a discussion on the difference between quantum and QL computers).     

Prion potency in stem cells biology

Prion protein (PrP) can be considered a pivotal molecule because it interacts with several partners to perform a diverse range of critical biological functions that might differ in embryonic and adult cells. In recent years, there have been major advances in elucidating the putative role of PrP in the basic biology of stem cells in many different systems. Here, we review the evidence indicating that PrP is a key molecule involved in driving different aspects of the potency of embryonic and tissue-specific stem cells in self-perpetuation and differentiation in many cell types. It has been shown that PrP is involved in stem cell self-renewal, controlling pluripotency gene expression, proliferation, and neural and cardiomyocyte differentiation. PrP also has essential roles in distinct processes that regulate tissue-specific stem cell biology in nervous and hematopoietic systems and during muscle regeneration. Results from our own investigations have shown that PrP is able to modulate self-renewal and proliferation in neural stem cells, processes that are enhanced by PrP interactions with stress inducible protein 1 (STI1). Thus, the available data reveal the influence of PrP in acting upon the maintenance of pluripotent status or the differentiation of stem cells from the early embryogenesis through adulthood.     

Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods

Multivariate metabolic profiles from biofluids such as urine and plasma are highly indicative of the biological fitness of complex organisms and can be captured analytically in order to derive top-down systems biology models. The application of currently available modeling approaches to human and animal metabolic pathway modeling is problematic because of multicompartmental cellular and tissue exchange of metabolites operating on many time scales. Hence, novel approaches are needed to analyze metabolic data obtained using minimally invasive sampling methods in order to reconstruct the patho-physiological modulations of metabolic interactions that are representative of whole system dynamics. Here, we show that spectroscopically derived metabolic data in experimental liver injury studies (induced by hydrazine and alpha-napthylisothiocyanate treatment) can be used to derive insightful probabilistic graphical models of metabolite dependencies, which we refer to as metabolic interactome maps. Using these, system level mechanistic information on homeostasis can be inferred, and the degree of reversibility of induced lesions can be related to variations in the metabolic network patterns. This approach has wider application in assessment of system level dysfunction in animal or human studies from noninvasive measurements.     

UML as a cell and biochemistry modeling language

The systems biology community is building increasingly complex models and simulations of cells and other biological entities, and are beginning to look at alternatives to traditional representations such as those provided by ordinary differential equations (ODE). The lessons learned over the years by the software development community in designing and building increasingly complex telecommunication and other commercial real-time reactive systems, can be advantageously applied to the problems of modeling in the biology domain. Making use of the object-oriented (OO) paradigm, the unified modeling language (UML) and Real-Time Object-Oriented Modeling (ROOM) visual formalisms, and the Rational Rose RealTime (RRT) visual modeling tool, we describe a multi-step process we have used to construct top–down models of cells and cell aggregates. The simple example model described in this paper includes membranes with lipid bilayers, multiple compartments including a variable number of mitochondria, substrate molecules, enzymes with reaction rules, and metabolic pathways. We demonstrate the relevance of abstraction, reuse, objects, classes, component and inheritance hierarchies, multiplicity, visual modeling, and other current software development best practices. We show how it is possible to start with a direct diagrammatic representation of a biological structure such as a cell, using terminology familiar to biologists, and by following a process of gradually adding more and more detail, arrive at a system with structure and behavior of arbitrary complexity that can run and be observed on a computer. We discuss our CellAK (Cell Assembly Kit) approach in terms of features found in SBML, CellML, E-CELL, Gepasi, Jarnac, StochSim, Virtual Cell, and membrane computing systems.     

Non-local concepts and models in biology

In this paper, we consider local and non-local spatially explicit mathematical models for biological phenomena. We show that, when rate differences between fast and slow local dynamics are great enough, non-local models are adequate simplifications of local models. Non-local models thus avoid describing fast processes in mechanistic detail, instead describing the effects of fast processes on slower ones. As a consequence, non-local models are helpful to biologists because they describe biological systems on scales that are convenient to observation, data collection, and insight. We illustrate these arguments by comparing local and non-local models for the aggregation of hypothetical organisms, and we support theoretical ideas with concrete examples from cell biology and animal behavior.     

Possible contribution of quantum-like correlations to the placebo effect: consequences on blind trials

Background

Factors that participate in the biological changes associated with a placebo are not completely understood. Natural evolution, mean regression, concomitant procedures and other non specific effects are well-known factors that contribute to the “placebo effect”. In this article, we suggest that quantum-like correlations predicted by a probabilistic modeling could also play a role.

Results

An elementary experiment in biology or medicine comparing the biological changes associated with two placebos is modeled. The originality of this modeling is that experimenters, biological system and their interactions are described together from the standpoint of a participant who is uninvolved in the measurement process. Moreover, the small random probability fluctuations of a “real” experiment are also taken into account. If both placebos are inert (with only different labels), common sense suggests that the biological changes associated with the two placebos should be comparable. However, the consequence of this modeling is the possibility for two placebos to be associated with different outcomes due to the emergence of quantum-like correlations.

Conclusion

The association of two placebos with different outcomes is counterintuitive and this modeling could give a framework for some unexplained observations where mere placebos are compared (in some alternative medicines for example). This hypothesis can be tested in blind trials by comparing local vs. remote assessment of correlations.

     

Systems biology in drug discovery

The hope of the rapid translation of 'genes to drugs' has foundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Systems biology aims to describe and to understand the operation of complex biological systems and ultimately to develop predictive models of human disease. Although meaningful molecular level models of human cell and tissue function are a distant goal, systems biology efforts are already influencing drug discovery. Large-scale gene, protein and metabolite measurements ('omics') dramatically accelerate hypothesis generation and testing in disease models. Computer simulations integrating knowledge of organ and system-level responses help prioritize targets and design clinical trials. Automation of complex primary human cell-based assay systems designed to capture emergent properties can now integrate a broad range of disease-relevant human biology into the drug discovery process, informing target and compound validation, lead optimization, and clinical indication selection. These systems biology approaches promise to improve decision making in pharmaceutical development.     

Altered glycosylated PrP proteins can have different neuronal trafficking in brain but do not acquire scrapie-like properties

N-Linked glycans have been shown to have an important role in the cell biology of a variety of cell surface glycoproteins, including PrP protein. It has been suggested that glycosylation of PrP can influence the susceptibility to transmissible spongiform encephalopathy and determine the characteristics of the many different strains observed in this particular type of disease. To understand the role of carbohydrates in influencing the PrP maturation, stability, and cell biology, we have produced and analyzed gene-targeted murine models expressing differentially glycosylated PrP. Transgenic mice carrying the PrP substitution threonine for asparagine 180 (G1) or threonine for asparagine 196 (G2) or both mutations combined (G3), which eliminate the first, second, and both glycosylation sites, respectively, have been generated by double replacement gene targeting. An in vivo analysis of altered PrP has been carried out in transgenic mouse brains, and our data show that the lack of glycans does not influence PrP maturation and stability. The presence of one chain of sugar is sufficient for the trafficking to the cell membrane, whereas the unglycosylated PrP localization is mainly intracellular. However, this altered cellular localization of PrP does not lead to any overt phenotype in the G3 transgenic mice. Most importantly, we found that, in vivo, unglycosylated PrP does not acquire the characteristics of the aberrant pathogenic form (PrPSc), as was previously reported using in vitro models.     

Quantum-like behavior without quantum physics I

Recently there has been much interest in the possible quantum-like behavior of the human brain in such functions as cognition, the mental lexicon, memory, etc., producing a vast literature. These studies are both empirical and theoretical, the tenets of the theory in question being mainly, and apparently inevitably, those of quantum physics itself, for lack of other arenas in which quantum-like properties are presumed to obtain. However, attempts to explain this behavior on the basis of actual quantum physics going on at the atomic or molecular level within some element of brain or neuronal anatomy (other than the ordinary quantum physics that underlies everything), do not seem to survive much scrutiny. Moreover, it has been found empirically that the usual physics-like Hilbert space model seems not to apply in detail to human cognition in the large. In this paper we lay the groundwork for a theory that might explain the provenance of quantum-like behavior in complex systems whose internal structure is essentially hidden or inaccessible. The approach is via the logic obeyed by these systems which is similar to, but not identical with, the logic obeyed by actual quantum systems. The results reveal certain effects in such systems which, though quantum-like, are not identical to the kinds of quantum effects found in physics. These effects increase with the size of the system.     

Knockdown of the bovine prion gene PRNP by RNA interference (RNAi) technology

Background  

Since prion gene-knockout mice do not contract prion diseases and animals in which production of prion protein (PrP) is reduced by half are resistant to the disease, we hypothesized that bovine animals with reduced PrP would be tolerant to BSE. Hence, attempts were made to produce bovine PRNP (bPRNP) that could be knocked down by RNA interference (RNAi) technology. Before an in vivo study, optimal conditions for knocking down bPRNP were determined in cultured mammalian cell systems. Factors examined included siRNA (short interfering RNA) expression plasmid vectors, target sites of PRNP, and lengths of siRNAs.     

Probabilistic logic methods and some applications to biology and medicine

For the computational analysis of biological problems-analyzing data, inferring networks and complex models, and estimating model parameters-it is common to use a range of methods based on probabilistic logic constructions, sometimes collectively called machine learning methods. Probabilistic modeling methods such as Bayesian Networks (BN) fall into this class, as do Hierarchical Bayesian Networks (HBN), Probabilistic Boolean Networks (PBN), Hidden Markov Models (HMM), and Markov Logic Networks (MLN). In this review, we describe the most general of these (MLN), and show how the above-mentioned methods are related to MLN and one another by the imposition of constraints and restrictions. This approach allows us to illustrate a broad landscape of constructions and methods, and describe some of the attendant strengths, weaknesses, and constraints of many of these methods. We then provide some examples of their applications to problems in biology and medicine, with an emphasis on genetics. The key concepts needed to picture this landscape of methods are the ideas of probabilistic graphical models, the structures of the graphs, and the scope of the logical language repertoire used (from First-Order Logic [FOL] to Boolean logic.) These concepts are interlinked and together define the nature of each of the probabilistic logic methods. Finally, we discuss the initial applications of MLN to genetics, show the relationship to less general methods like BN, and then mention several examples where such methods could be effective in new applications to specific biological and medical problems.     

Metabolic engineering of recombinant protein secretion by Saccharomyces cerevisiae

The yeast Saccharomyces cerevisiae is a widely used cell factory for the production of fuels and chemicals, and it is also provides a platform for the production of many heterologous proteins of medical or industrial interest. Therefore, many studies have focused on metabolic engineering S. cerevisiae to improve the recombinant protein production, and with the development of systems biology, it is interesting to see how this approach can be applied both to gain further insight into protein production and secretion and to further engineer the cell for improved production of valuable proteins. In this review, the protein post-translational modification such as folding, trafficking, and secretion, steps that are traditionally studied in isolation will here be described in the context of the whole system of protein secretion. Furthermore, examples of engineering secretion pathways, high-throughput screening and systems biology applications of studying protein production and secretion are also given to show how the protein production can be improved by different approaches. The objective of the review is to describe individual biological processes in the context of the larger, complex protein synthesis network.     

Reverse engineering systems models of regulation: discovery, prediction and mechanisms

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties.     

Explicit Tracking of Uncertainty Increases the Power of Quantitative Rule-of-Thumb Reasoning in Cell Biology

Back-of-the-envelope or rule-of-thumb calculations involving rough estimates of quantities play a central scientific role in developing intuition about the structure and behavior of physical systems, for example in so-called Fermi problems in the physical sciences. Such calculations can be used to powerfully and quantitatively reason about biological systems, particularly at the interface between physics and biology. However, substantial uncertainties are often associated with values in cell biology, and performing calculations without taking this uncertainty into account may limit the extent to which results can be interpreted for a given problem. We present a means to facilitate such calculations where uncertainties are explicitly tracked through the line of reasoning, and introduce a probabilistic calculator called CALADIS, a free web tool, designed to perform this tracking. This approach allows users to perform more statistically robust calculations in cell biology despite having uncertain values, and to identify which quantities need to be measured more precisely to make confident statements, facilitating efficient experimental design. We illustrate the use of our tool for tracking uncertainty in several example biological calculations, showing that the results yield powerful and interpretable statistics on the quantities of interest. We also demonstrate that the outcomes of calculations may differ from point estimates when uncertainty is accurately tracked. An integral link between CALADIS and the BioNumbers repository of biological quantities further facilitates the straightforward location, selection, and use of a wealth of experimental data in cell biological calculations.     

Modeling and simulation of biological systems with stochasticity

Mathematical modeling is a powerful approach for understanding the complexity of biological systems. Recently, several successful attempts have been made for simulating complex biological processes like metabolic pathways, gene regulatory networks and cell signaling pathways. The pathway models have not only generated experimentally verifiable hypothesis but have also provided valuable insights into the behavior of complex biological systems. Many recent studies have confirmed the phenotypic variability of organisms to an inherent stochasticity that operates at a basal level of gene expression. Due to this reason, development of novel mathematical representations and simulations algorithms are critical for successful modeling efforts in biological systems. The key is to find a biologically relevant representation for each representation. Although mathematically rigorous and physically consistent, stochastic algorithms are computationally expensive, they have been successfully used to model probabilistic events in the cell. This paper offers an overview of various mathematical and computational approaches for modeling stochastic phenomena in cellular systems.     

From intracellular signaling to population oscillations: bridging size- and time-scales in collective behavior

Collective behavior in cellular populations is coordinated by biochemical signaling networks within individual cells. Connecting the dynamics of these intracellular networks to the population phenomena they control poses a considerable challenge because of network complexity and our limited knowledge of kinetic parameters. However, from physical systems, we know that behavioral changes in the individual constituents of a collectively behaving system occur in a limited number of well-defined classes, and these can be described using simple models. Here, we apply such an approach to the emergence of collective oscillations in cellular populations of the social amoeba Dictyostelium discoideum. Through direct tests of our model with quantitative in vivo measurements of single-cell and population signaling dynamics, we show how a simple model can effectively describe a complex molecular signaling network at multiple size and temporal scales. The model predicts novel noise-driven single-cell and population-level signaling phenomena that we then experimentally observe. Our results suggest that like physical systems, collective behavior in biology may be universal and described using simple mathematical models.     

Fundamental and functional aspects of mesoscopic architectures with examples in physics,cell biology,and chemistry

How small can a macroscopic object be made without losing its intended function? Obviously, the smallest possible size is determined by the size of an atom, but it is not so obvious how many atoms are required to assemble an object so small, and yet that performs the same function as its macroscopic counterpart. In this review, we are concerned with objects of intermediate nature, lying between the microscopic and the macroscopic world. In physics and chemistry literature, this regime in-between is often called mesoscopic, and is known to bear interesting and counterintuitive features. After a brief introduction to the concept of mesoscopic systems from the perspective of physics, we discuss the functional aspects of mesoscopic architectures in cell biology, and supramolecular chemistry through many examples from the literature. We argue that the biochemistry of the cell is largely regulated by mesoscopic functional architectures; however, the significance of mesoscopic phenomena seems to be quite underappreciated in biological sciences. With this motivation, one of our main purposes here is to emphasize the critical role that mesoscopic structures play in cell biology and biochemistry.     

Fundamental and functional aspects of mesoscopic architectures with examples in physics, cell biology, and chemistry

How small can a macroscopic object be made without losing its intended function? Obviously, the smallest possible size is determined by the size of an atom, but it is not so obvious how many atoms are required to assemble an object so small, and yet that performs the same function as its macroscopic counterpart. In this review, we are concerned with objects of intermediate nature, lying between the microscopic and the macroscopic world. In physics and chemistry literature, this regime in-between is often called mesoscopic, and is known to bear interesting and counterintuitive features. After a brief introduction to the concept of mesoscopic systems from the perspective of physics, we discuss the functional aspects of mesoscopic architectures in cell biology, and supramolecular chemistry through many examples from the literature. We argue that the biochemistry of the cell is largely regulated by mesoscopic functional architectures; however, the significance of mesoscopic phenomena seems to be quite underappreciated in biological sciences. With this motivation, one of our main purposes here is to emphasize the critical role that mesoscopic structures play in cell biology and biochemistry.     

Therapeutic biology: checkpoint pathway activation therapy, HIV Tat, and transkingdom RNA interference

Therapeutic biology is an exciting new frontier in the post-genomic era with the mission to better human health. The explosive increase in the understanding of molecular and regulatory biology has enabled the identification of a plethora of potential targets and pathways for the discovery of new medicines. Conversely, molecularly based therapeutic intervention of biological aberrations in human diseases offers a test of the depth of our understanding of biology. This article discusses three examples of therapeutic biology research. The first concerns the treatment of cancer by activating genome surveillance circuitry, namely checkpoint-pathway activation therapy. The second example is the identification of the HIV Tat protein as both a cause of immune cell activation and apoptosis, and as a vaccine candidate against HIV infection. The third example describes transkingdom RNA interference and its application in the investigational and therapeutic silencing of disease genes.