Potentiality in Biology

We take the potentialities that are studied in the biological sciences (e.g., totipotency) to be an important subtype of biological dispositions. The goal of this paper is twofold: first, we want to provide a detailed understanding of what biological dispositions are. We claim that two features are essential for dispositions in biology: the importance of the manifestation process and the diversity of conditions that need to be satisfied for the disposition to be manifest. Second, we demonstrate that the concept of a disposition (or potentiality) is a very useful tool for the analysis of the explanatory practice in the biological sciences. On the one hand it allows an in-depth analysis of the nature and diversity of the conditions under which biological systems display specific behaviors. On the other hand the concept of a disposition may serve a unificatory role in the philosophy of the natural sciences since it captures not only the explanatory practice of biology, but of all natural sciences. Towards the end we will briefly come back to the notion of a potentiality in biology

SHIFTING THE PARADIGM IN RADIATION SAFETY

The current radiation safety paradigm using the linear no-threshold (LNT) model is based on the premise that even the smallest amount of radiation may cause mutations increasing the risk of cancer. Autopsy studies have shown that the presence of cancer cells is not a decisive factor in the occurrence of clinical cancer. On the other hand, suppression of immune system more than doubles the cancer risk in organ transplant patients, indicating its key role in keeping occult cancers in check. Low dose radiation (LDR) elevates immune response, and so it may reduce rather than increase the risk of cancer. LNT model pays exclusive attention to DNA damage, which is not a decisive factor, and completely ignores immune system response, which is an important factor, and so is not scientifically justifiable. By not recognizing the importance of the immune system in cancer, and not exploring exercise intervention, the current paradigm may have missed an opportunity to reduce cancer deaths among atomic bomb survivors. Increased antioxidants from LDR may reduce aging-related non-cancer diseases since oxidative damage is implicated in these. A paradigm shift is warranted to reduce further casualties, reduce fear of LDR, and enable investigation of potential beneficial applications of LDR

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation, and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.Comment: Submitted to 'Systems Medicine' as a book chapte

Efficiency of complex production in changing environment

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Markov clustering versus affinity propagation for the partitioning of protein interaction graphs

<p>Abstract</p> <p>Background</p> <p>Genome scale data on protein interactions are generally represented as large networks, or graphs, where hundreds or thousands of proteins are linked to one another. Since proteins tend to function in groups, or complexes, an important goal has been to reliably identify protein complexes from these graphs. This task is commonly executed using clustering procedures, which aim at detecting densely connected regions within the interaction graphs. There exists a wealth of clustering algorithms, some of which have been applied to this problem. One of the most successful clustering procedures in this context has been the Markov Cluster algorithm (MCL), which was recently shown to outperform a number of other procedures, some of which were specifically designed for partitioning protein interactions graphs. A novel promising clustering procedure termed Affinity Propagation (AP) was recently shown to be particularly effective, and much faster than other methods for a variety of problems, but has not yet been applied to partition protein interaction graphs.</p> <p>Results</p> <p>In this work we compare the performance of the Affinity Propagation (AP) and Markov Clustering (MCL) procedures. To this end we derive an unweighted network of protein-protein interactions from a set of 408 protein complexes from <it>S. cervisiae </it>hand curated in-house, and evaluate the performance of the two clustering algorithms in recalling the annotated complexes. In doing so the parameter space of each algorithm is sampled in order to select optimal values for these parameters, and the robustness of the algorithms is assessed by quantifying the level of complex recall as interactions are randomly added or removed to the network to simulate noise. To evaluate the performance on a weighted protein interaction graph, we also apply the two algorithms to the consolidated protein interaction network of <it>S. cerevisiae</it>, derived from genome scale purification experiments and to versions of this network in which varying proportions of the links have been randomly shuffled.</p> <p>Conclusion</p> <p>Our analysis shows that the MCL procedure is significantly more tolerant to noise and behaves more robustly than the AP algorithm. The advantage of MCL over AP is dramatic for unweighted protein interaction graphs, as AP displays severe convergence problems on the majority of the unweighted graph versions that we tested, whereas MCL continues to identify meaningful clusters, albeit fewer of them, as the level of noise in the graph increases. MCL thus remains the method of choice for identifying protein complexes from binary interaction networks.</p

Proof of concept, randomized, placebo-controlled study of the effect of simvastatin on the course of age-related macular degeneration

BACKGROUND: HMG Co-A reductase inhibitors are ubiquitous in our community yet their potential role in age-related macular degeneration (AMD) remains to be determined. METHODOLOGY/PRINCIPAL FINDINGS: OBJECTIVES: To evaluate the effect of simvastatin on AMD progression and the effect modification by polymorphism in apolipoprotein E (ApoE) and complement factor H (CFH) genes. DESIGN: A proof of concept double-masked randomized controlled study. PARTICIPANTS: 114 participants aged 53 to 91 years, with either bilateral intermediate AMD or unilateral non-advanced AMD (with advanced AMD in fellow eye), BCVA ≥ 20/60 in at least one eye, and a normal lipid profile. INTERVENTION: Simvastatin 40 mg/day or placebo, allocated 1:1. MAIN OUTCOME MEASURES: Progression of AMD either to advanced AMD or in severity of non-advanced AMD. Results. The cumulative AMD progression rates were 70% in the placebo and 54% in the simvastatin group. Intent to treat multivariable logistic regression analysis, adjusted for age, sex, smoking and baseline AMD severity, showed a significant 2-fold decrease in the risk of progression in the simvastatin group: OR 0.43 (0.18-0.99), p = 0.047. Post-hoc analysis stratified by baseline AMD severity showed no benefit from treatment in those who had advanced AMD in the fellow eye before enrolment: OR 0.97 (0.27-3.52), p = 0.96, after adjusting for age, sex and smoking. However, there was a significant reduction in the risk of progression in the bilateral intermediate AMD group compared to placebo [adjusted OR 0.23 (0.07-0.75), p = 0.015]. The most prominent effect was observed amongst those who had the CC (Y402H) at risk genotype of the CFH gene [OR 0.08 (0.02-0.45), p = 0.004]. No evidence of harm from simvastatin intervention was detected. CONCLUSION/SIGNIFICANCE: Simvastatin may slow progression of non-advanced AMD, especially for those with the at risk CFH genotype CC (Y402H). Further exploration of the potential use of statins for AMD, with emphasis on genetic subgroups, is warranted. TRIAL REGISTRATION: Australian New Zealand Clinical Trial Registry (ANZCTR) ACTRN1260500032065

fabH deletion increases DHA productionin Escherichia coli expressing Pfa genes

Background: Some marine bacteria, such as Moritella marina, produce the nutraceutical docosahexaenoic acid (DHA) thanks to a specific enzymatic complex called Pfa synthase. Escherichia coli heterologously expressing the pfa gene cluster from M. marina also produces DHA. The aim of this study was to find genetic or metabolic conditions to increase DHA production in E. coli. Results: First, we analysed the effect of the antibiotic cerulenin, showing that DHA production increased twofold. Then, we tested a series of single gene knockout mutations affecting fatty acid biosynthesis, in order to optimize the synthesis of DHA. The most effective mutant, fabH, showed a threefold increase compared to wild type strain. The combination of cerulenin inhibition and fabH deletion rendered a 6.5-fold improvement compared to control strain. Both strategies seem to have the same mechanism of action, in which fatty acid synthesis via the canonical pathway (fab pathway) is affected in its first catalytic step, which allows the substrates to be used by the heterologous pathway to synthesize DHA. Conclusions: DHA-producing E. coli strain that carries a fabH gene deletion boosts DHA production by tuning down the competing canonical biosynthesis pathway. Our approach can be used for optimization of DHA production in different organisms.Funding: The work in the FdlC and GM laboratories was financed by the Spanish Ministry of Economy, Industry and Competitiveness Grant BFU2014-55534-C2

Supplementation of Male Pheromone on Rock Substrates Attracts Female Rock Lizards to the Territories of Males: A Field Experiment

Background: Many animals produce elaborated sexual signals to attract mates, among them are common chemical sexual signals (pheromones) with an attracting function. Lizards produce chemical secretions for scent marking that may have a role in sexual selection. In the laboratory, female rock lizards (Iberolacerta cyreni) prefer the scent of males with more ergosterol in their femoral secretions. However, it is not known whether the scent-marks of male rock lizards may actually attract females to male territories in the field. Methodology/Principal Findings: In the field, we added ergosterol to rocks inside the territories of male lizards, and found that this manipulation resulted in increased relative densities of females in these territories. Furthermore, a higher number of females were observed associated to males in manipulated plots, which probably increased mating opportunities for males in these areas. Conclusions/Significance: These and previous laboratory results suggest that female rock lizards may select to settle in home ranges based on the characteristics of scent-marks from conspecific males. Therefore, male rock lizards might attract more females and obtain more matings by increasing the proportion of ergosterol when scent-marking their territories. However, previous studies suggest that the allocation of ergosterol to secretions may be costly and only high quality male

Investigation of Polyurea-Crosslinked Silica Aerogels as a Neuronal Scaffold: A Pilot Study

BACKGROUND: Polymer crosslinked aerogels are an attractive class of materials for future implant applications particularly as a biomaterial for the support of nerve growth. The low density and nano-porous structure of this material combined with large surface area, high mechanical strength, and tunable surface properties, make aerogels materials with a high potential in aiding repair of injuries of the peripheral nervous system. however, the interaction of neurons with aerogels remains to be investigated. METHODOLOGY: In this work the attachment and growth of neurons on clear polyurea crosslinked silica aerogels (PCSA) coated with: poly-L-lysine, basement membrane extract (BME), and laminin1 was investigated by means of optical and scanning electron microscopy. After comparing the attachment and growth capability of neurons on these different coatings, laminin1 and BME were chosen for nerve cell attachment and growth on PCSA surfaces. The behavior of neurons on treated petri dish surfaces was used as the control and behavior of neurons on treated PCSA discs was compared against it. CONCLUSIONS/SIGNIFICANCE: This study demonstrates that: 1) untreated PCSA surfaces do not support attachment and growth of nerve cells, 2) a thin application of laminin1 layer onto the PCSA discs adhered well to the PCSA surface while also supporting growth and differentiation of neurons as evidenced by the number of processes extended and b3-tubulin expression, 3) three dimensional porous structure of PCSA remains intact after fixing protocols necessary for preservation of biological samples and 4) laminin1 coating proved to be the most effective method for attaching neurons to the desired regions on PCSA discs. This work provides the basis for potential use of PCSA as a biomaterial scaffold for neural regeneration

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p