Energy Consumption and Entropy Production in a Stochastic Formulation of BCM Learning.

In a series of previous studies, we provided a stochastic description of a theory of synaptic plasticity. This theory, called BCM from the names of the three authors, has been formulated in two ways: the original formulation, where the plasticity threshold is defined as the square of the time-averaged neuronal activity, and a newer formulation, where the plasticity threshold is defined as the time average of the square of the neuronal activity. The newest formulation of the BCM rule of synaptic activity has interesting statistical properties, derived from a risk (or energy) function, the minimization of which leads to seeking of interesting projections in high-dimensional space. Moreover, these two rules, if implemented by a chemical master equation approach, show another interesting difference: the original rule satisfies the detailed balance, whereas the other not. Based on this different behavior, we found a continuous parameterization between these two rules. This parameterization shows a minimum that corresponds to maximum negative eigenvalues of the Jacobian matrix. In addition, the newest rule, due to the fact that it is in a nonequilibrium steady state (NESS), shows a higher level of plasticity than the original rule. This higher level of plasticity has to be interpreted in the framework of open thermodynamical systems and we show that entropy production and energy consumption in the newest rule are both less than in the original BCM rule.

X-ray induced photodynamic therapy with copper-cysteamine nanoparticles in mice tumors.

Photodynamic therapy (PDT), a treatment that uses a photosensitizer, molecular oxygen, and light to kill target cells, is a promising cancer treatment method. However, a limitation of PDT is its dependence on light that is not highly penetrating, precluding the treatment of tumors located deep in the body. Copper-cysteamine nanoparticles are a new type of photosensitizer that can generate cytotoxic singlet oxygen molecules upon activation by X-rays. In this paper, we report on the use of copper-cysteamine nanoparticles, designed to be targeted to tumors, for X-ray-induced PDT. In an in vivo study, results show a statistically significant reduction in tumor size under X-ray activation of pH-low insertion peptide-conjugated, copper-cysteamine nanoparticles in mouse tumors. This work confirms the effectiveness of copper-cysteamine nanoparticles as a photosensitizer when activated by radiation and suggests that these Cu-Cy nanoparticles may be good candidates for PDT in deeply seated tumors when combined with X-rays and conjugated to a tumor-targeting molecule.

Gold Nanoparticles Enhance Radiation Therapy at Low Concentrations, and Remain in Tumors for Days.

Gold nanoparticles are a potential method for enhancing radiation therapy, causing extra damage to tumors when irradiated through the Auger effect. One of the major obstacles to using gold nanoparticles in human trials is the relatively large amount of gold required. This paper details an experiment where a relatively small amount of gold (200 µg) was used to significantly reduce tumor volume in mice, as well as the results of an inter-tissue biodistribution experiment. Using a longitudinal analysis, tumor size as a function of time was found to be significantly reduced when mice were given 200 µg of gold nanoparticles and 20 Gray of radiation, compared to radiation alone. 200 µg in a 20-gram mouse would be mass equivalent to 750 mg of gold in a 75 kg person. Biodistribution measurements demonstrated that gold nanoparticles stayed in the tumor for at least one week after injection when targeted to tumors using pH-Low Insertion Peptide and intratumoral injections. These results show gold nanoparticles to be effective at one of the smallest amounts of gold ever attempted in a mouse, and showed that tumor targeting has the potential to keep gold nanoparticles available in tumors long enough to be beneficial to fractionated radiation treatments (a key component of radiation therapy in the clinic).

Gene Expression as a Dosimeter in Irradiated Drosophila melanogaster.

Biological indicators would be of use in radiation dosimetry in situations where an exposed person is not wearing a dosimeter, or when physical dosimeters are insufficient to estimate the risk caused by the radiation exposure. In this work, we investigate the use of gene expression as a dosimeter. Gene expression analysis was done on 15,222 genes of Drosophila melanogaster (fruit flies) at days 2, 10, and 20 postirradiation, with X-ray exposures of 10, 1000, 5000, 10,000, and 20,000 roentgens. Several genes were identified, which could serve as a biodosimeter in an irradiated D. melanogaster model. Many of these genes have human homologues. Six genes showed a linear response (R2 > 0.9) with dose at all time points. One of these genes, inverted repeat-binding protein, is a known DNA repair gene and has a human homologue (XRCC6). The lowest dose, 10 roentgen, is very low for fruit flies. If the lowest dose is excluded, 13 genes showed a linear response with dose at all time points. This includes 5 of 6 genes that were linear with all radiation doses included. Of these 13 genes, 4 have human homologues and 8 have known functions. The expression of this panel of genes, particularly those with human homologues, could potentially be used as the biological indicator of radiation exposure in dosimetry applications.

Gold Nanoparticles for Radiation Enhancement in Vivo.

Enhancing the effect of radiation on tumors would be a significant improvement in radiation therapy. With radiation enhancement, less radiation could be used to achieve the same goals, lessening damage to healthy tissue and lessening side effects. Gold nanoparticles are a promising method for achieving this enhancement, particularly when the gold nanoparticles are targeted to cancer. This literature review discusses the properties of gold nanoparticles as well as existing in vivo radiation enhancement results using both targeted and non-targeted gold nanoparticles.

Enhancement of radiation effect on cancer cells by gold-pHLIP.

Previous research has shown that gold nanoparticles can increase the effectiveness of radiation on cancer cells. Improved radiation effectiveness would allow lower radiation doses given to patients, reducing adverse effects; alternatively, it would provide more cancer killing at current radiation doses. Damage from radiation and gold nanoparticles depends in part on the Auger effect, which is very localized; thus, it is important to place the gold nanoparticles on or in the cancer cells. In this work, we use the pH-sensitive, tumor-targeting agent, pH Low-Insertion Peptide (pHLIP), to tether 1.4-nm gold nanoparticles to cancer cells. We find that the conjugation of pHLIP to gold nanoparticles increases gold uptake in cells compared with gold nanoparticles without pHLIP, with the nanoparticles distributed mostly on the cellular membranes. We further find that gold nanoparticles conjugated to pHLIP produce a statistically significant decrease in cell survival with radiation compared with cells without gold nanoparticles and cells with gold alone. In the context of our previous findings demonstrating efficient pHLIP-mediated delivery of gold nanoparticles to tumors, the obtained results serve as a foundation for further preclinical evaluation of dose enhancement.

Drosophila melanogaster show a threshold effect in response to radiation.

We investigate the biological effects of radiation using adult Drosophila melanogaster as a model organism, focusing on gene expression and lifespan analysis to determine the effect of different radiation doses. Our results support a threshold effect in response to radiation: no effect on lifespan and no permanent effect on gene expression is seen at incident radiation levels below 100 J/kg. We also find that it is more appropriate to compare radiation effects in flies using the absorbed energy rather than incident radiation levels.

CORaL: comparison of ranked lists for analysis of gene expression data.

Because a very large number of gene expression data sets are currently publicly available, comparisons across experiments between different laboratories have become a common task. However, most existing methods of comparing gene expression data sets require setting arbitrary cutoffs (e.g., for statistical significance or fold change), which could select genes according to different criteria because of differences in experimental protocols and statistical analysis in different data sets. A new method is proposed for comparing expression profiles across experiments by using the rank of genes in the different datasets. We introduce a maximization statistic, which can be calculated recursively and allows for efficient searches on a large space (paths on a grid). We apply our method to both simulated and real datasets and show that it outperforms other existing rank-based algorithms. CORaL is a novel method for comparison of gene expression data that performs well on simulated and real data. It has the potential for wide and effective use in computational biology.

The BCM theory of synapse modification at 30: interaction of theory with experiment.

Thirty years have passed since the publication of Elie Bienenstock, Leon Cooper and Paul Munro's 'Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex', known as the BCM theory of synaptic plasticity. This theory has guided experimentalists to discover some fundamental properties of synaptic plasticity and has provided a mathematical structure that bridges molecular mechanisms and systems-level consequences of learning and memory storage.

Semi-coherent time of arrival estimation using regression.

Time of arrival (ToA) estimation is essential for many types of remote sensing applications including radar, sonar, and underground exploration. The standard method for ToA estimation employs a matched filter for computing the maximum likelihood estimator (MLE) for ToA. The accuracy of the MLE decreases rapidly whenever the amount of noise in a received signal rises above a certain threshold. This well-known threshold effect is unavoidable in several important applications due to various limitations on the power and the spectrum of a narrowband source pulse. A measurement performed in the presence of the threshold effect employs a receiver which operates in the semi-coherent state. Therefore, the conventional methods assuming a coherent state receiver should be adapted to the semi-coherent case. In this paper, a biosonar-inspired method for the semi-coherent ToA estimation is described. The method abandons the exploration of an echo signal by a single matched filter in favor of the analysis by multiple phase-shifted unmatched filters. Each phase-shifted unmatched filter gives rise to a biased ToA estimator. The described method uses regression for combining these estimators into a single unbiased ToA estimator that outperform the MLE in the presence of the threshold effect.

Bistability in the chemical master equation for dual phosphorylation cycles.

Dual phospho/dephosphorylation cycles, as well as covalent enzymatic-catalyzed modifications of substrates are widely diffused within cellular systems and are crucial for the control of complex responses such as learning, memory, and cellular fate determination. Despite the large body of deterministic studies and the increasing work aimed at elucidating the effect of noise in such systems, some aspects remain unclear. Here we study the stationary distribution provided by the two-dimensional chemical master equation for a well-known model of a two step phospho/dephosphorylation cycle using the quasi-steady state approximation of enzymatic kinetics. Our aim is to analyze the role of fluctuations and the molecules distribution properties in the transition to a bistable regime. When detailed balance conditions are satisfied it is possible to compute equilibrium distributions in a closed and explicit form. When detailed balance is not satisfied, the stationary non-equilibrium state is strongly influenced by the chemical fluxes. In the last case, we show how the external field derived from the generation and recombination transition rates, can be decomposed by the Helmholtz theorem, into a conservative and a rotational (irreversible) part. Moreover, this decomposition allows to compute the stationary distribution via a perturbative approach. For a finite number of molecules there exists diffusion dynamics in a macroscopic region of the state space where a relevant transition rate between the two critical points is observed. Further, the stationary distribution function can be approximated by the solution of a Fokker-Planck equation. We illustrate the theoretical results using several numerical simulations.

New comparative genomics approach reveals a conserved health span signature across species.

Environmental and genetic interventions extend health span in a range of organisms by triggering changes in different specific but complementary pathways. We investigated the gene expression changes that occur across species when health span is extended via different interventions. To perform this comparison using heterogeneous datasets from different measurement platforms and organisms, we developed a novel non-parametric methodology that can detect statistical significance of overlaps in ranked lists of genes, and estimate the number of genes with a common expression profile. By comparing genetic and environmental interventions that consistently lead to increased health span in invertebrates and vertebrates we built a conserved health span signature and described how such a signature depends on tissue type. Furthermore, we examined the relationship between calorie restriction and resveratrol administration and for the first time, identified common gene and pathway changes in calorie restriction and resveratrol in both invertebrates and mammals. Our approach can thus be used to explore and better define the relationships between highly complex biological phenomena, in this case those that affect the health and longevity.

Eigenvalue distributions for a class of covariance matrices with application to Bienenstock-Cooper-Munro neurons under noisy conditions.

We analyze the effects of noise correlations in the input to, or among, Bienenstock-Cooper-Munro neurons using the Wigner semicircular law to construct random, positive-definite symmetric correlation matrices and compute their eigenvalue distributions. In the finite dimensional case, we compare our analytic results with numerical simulations and show the effects of correlations on the lifetimes of synaptic strengths in various visual environments. These correlations can be due either to correlations in the noise from the input lateral geniculate nucleus neurons, or correlations in the variability of lateral connections in a network of neurons. In particular, we find that for fixed dimensionality, a large noise variance can give rise to long lifetimes of synaptic strengths. This may be of physiological significance.

Toward a microscopic model of bidirectional synaptic plasticity.

We show that a 2-step phospho/dephosphorylation cycle for the alpha-amino-3-hydroxy-5-methyl-4-isoxazole proprionic acid receptor (AMPAR), as used in in vivo learning experiments to assess long-term potentiation (LTP) induction and establishment, exhibits bistability for a wide range of parameters, consistent with values derived from biological literature. The AMPAR model we propose, hence, is a candidate for memory storage and switching behavior at a molecular-microscopic level. Furthermore, the stochastic formulation of the deterministic model leads to a mesoscopic interpretation by considering the effect of enzymatic fluctuations on the Michelis-Menten average dynamics. Under suitable hypotheses, this leads to a stochastic dynamical system with multiplicative noise whose probability density evolves according to a Fokker-Planck equation in the Stratonovich sense. In this approach, the probability density associated with each AMPAR phosphorylation state allows one to compute the probability of any concentration value, whereas the Michaelis-Menten equations consider the average concentration dynamics. We show that bistable dynamics are robust for multiplicative stochastic perturbations and that the presence of both noise and bistability simulates LTP and long-term depression (LTD) behavior. Interestingly, the LTP part of this model has been experimentally verified as a result of in vivo, one-trial inhibitory avoidance learning protocol in rats, that produced the same changes in hippocampal AMPARs phosphorylation state as observed with in vitro induction of LTP with high-frequency stimulation (HFS). A consequence of this model is the possibility of characterizing a molecular switch with a defined biochemical set of reactions showing bistability and bidirectionality. Thus, this 3-enzymes-based biophysical model can predict LTP as well as LTD and their transition rates. The theoretical results can be, in principle, validated by in vitro and in vivo experiments, such as fluorescence measurements and electrophysiological recordings at multiple scales, from molecules to neurons. A further consequence is that the bistable regime occurs only within certain parametric windows, which may simulate a "history-dependent threshold". This effect might be related to the Bienenstock-Cooper-Munro theory of synaptic plasticity.

Effect of correlated lateral geniculate nucleus firing rates on predictions for monocular eye closure versus monocular retinal inactivation.

Monocular deprivation experiments can be used to distinguish between different ideas concerning properties of cortical synaptic plasticity. Monocular deprivation by lid suture causes a rapid disconnection of the deprived eye connected to cortical neurons whereas total inactivation of the deprived eye produces much less of an ocular dominance shift. In order to understand these results one needs to know how lid suture and retinal inactivation affect neurons in the lateral geniculate nucleus (LGN) that provide the cortical input. Recent experimental results by Linden showed that monocular lid suture and monocular inactivation do not change the mean firing rates of LGN neurons but that lid suture reduces correlations between adjacent neurons whereas monocular inactivation leads to correlated firing. These, somewhat surprising, results contradict assumptions that have been made to explain the outcomes of different monocular deprivation protocols. Based on these experimental results we modify our assumptions about inputs to cortex during different deprivation protocols and show their implications when combined with different cortical plasticity rules. Using theoretical analysis, random matrix theory and simulations we show that high levels of correlations reduce the ocular dominance shift in learning rules that depend on homosynaptic depression (i.e., Bienenstock-Cooper-Munro type rules), consistent with experimental results, but have the opposite effect in rules that depend on heterosynaptic depression (i.e., Hebbian/principal component analysis type rules).

Recovery from monocular deprivation using binocular deprivation.

Ocular dominance (OD) plasticity is a robust paradigm for examining the functional consequences of synaptic plasticity. Previous experimental and theoretical results have shown that OD plasticity can be accounted for by known synaptic plasticity mechanisms, using the assumption that deprivation by lid suture eliminates spatial structure in the deprived channel. Here we show that in the mouse, recovery from monocular lid suture can be obtained by subsequent binocular lid suture but not by dark rearing. This poses a significant challenge to previous theoretical results. We therefore performed simulations with a natural input environment appropriate for mouse visual cortex. In contrast to previous work, we assume that lid suture causes degradation but not elimination of spatial structure, whereas dark rearing produces elimination of spatial structure. We present experimental evidence that supports this assumption, measuring responses through sutured lids in the mouse. The change in assumptions about the input environment is sufficient to account for new experimental observations, while still accounting for previous experimental results.

Reconstructing networks of pathways via significance analysis of their intersections.

BACKGROUND: Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statistical test. This problem is typically approached by applying post hoc corrections to control the false discovery rate, without taking into account prior biological knowledge. Pathway or gene ontology analysis can provide an alternative way to relax the significance threshold applied to single genes and may lead to a better biological interpretation. RESULTS: Here we propose a new analysis method based on the study of networks of pathways. These networks are reconstructed considering both the significance of single pathways (network nodes) and the intersection between them (links). We apply this method for the reconstruction of networks of pathways to two gene expression datasets: the first one obtained from a c-Myc rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein; the second one obtained from the comparison of Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia derived from bone marrow samples. CONCLUSION: Our method extends statistical models that have been recently adopted for the significance analysis of functional groups of genes to infer links between these groups. We show that groups of genes at the interface between different pathways can be considered as relevant even if the pathways they belong to are not significant by themselves.

Effect of stochastic synaptic and dendritic dynamics on synaptic plasticity in visual cortex and hippocampus.

Various forms of synaptic plasticity, including spike timing-dependent plasticity, can be accounted for by calcium-dependent models of synaptic plasticity. However, recent results in which synaptic plasticity is induced by multi-spike protocols cannot simply be accounted for by linear superposition of plasticity due to spike pairs or by existing calcium-dependent models. In this paper, we show that multi-spike protocols can be accounted for if, in addition to the dynamics of back-propagating action potentials, stochastic synaptic dynamics are taken into account. We show that a stochastic implementation can account for the data better than a deterministic implementation and is also more robust. Our results demonstrate that differences between experimental results obtained in hippocampus and visual cortex can be accounted for by the different synaptic and dendritic dynamics in these two systems.