Predicting pattern formation in embryonic stem cells using a minimalist, agent-based probabilistic model.

The mechanisms of pattern formation during embryonic development remain poorly understood. Embryonic stem cells in culture self-organise to form spatial patterns of gene expression upon geometrical confinement indicating that patterning is an emergent phenomenon that results from the many interactions between the cells. Here, we applied an agent-based modelling approach in order to identify plausible biological rules acting at the meso-scale within stem cell collectives that may explain spontaneous patterning. We tested different models involving differential motile behaviours with or without biases due to neighbour interactions. We introduced a new metric, termed stem cell aggregate pattern distance (SCAPD) to probabilistically assess the fitness of our models with empirical data. The best of our models improves fitness by 70% and 77% over the random models for a discoidal or an ellipsoidal stem cell confinement respectively. Collectively, our findings show that a parsimonious mechanism that involves differential motility is sufficient to explain the spontaneous patterning of the cells upon confinement. Our work also defines a region of the parameter space that is compatible with patterning. We hope that our approach will be applicable to many biological systems and will contribute towards facilitating progress by reducing the need for extensive and costly experiments.

Knowledge Driven Phenotyping.

Extracting patient phenotypes from routinely collected health data (such as Electronic Health Records) requires translating clinically-sound phenotype definitions into queries/computations executable on the underlying data sources by clinical researchers. This requires significant knowledge and skills to deal with heterogeneous and often imperfect data. Translations are time-consuming, error-prone and, most importantly, hard to share and reproduce across different settings. This paper proposes a knowledge driven framework that (1) decouples the specification of phenotype semantics from underlying data sources; (2) can automatically populate and conduct phenotype computations on heterogeneous data spaces. We report preliminary results of deploying this framework on five Scottish health datasets.

Nanoparticles and phage display selected peptides for imaging and therapy of cancer.

Molecular imaging probes are a special class of pharmaceuticals that target specific biochemical signatures associated with disease and allow for noninvasive imaging on the molecular level. Because changes in biochemistry occur before diseases reach an advanced stage, molecular imaging probes make it possible to locate and stage disease, track the effectiveness of drugs, treat disease, monitor response, and select patients to allow for more personalized diagnosis and treatment of disease. Targeting agents radiolabeled with positron emitters are of interest due to their ability to quantitatively measure biodistribution and receptor expression to allow for optimal dose determinations. (68)Ga is a positron emitter, which allows for quantitative imaging through positron emission chromatography (PET). The availability of (68)Ga from a generator and its ability to form stable complexes with a variety of chelates hold promise for expanding PET utilization to facilities unable to afford their own cyclotron. Nanoparticles conjugated with various proteins and peptides derived from phage display that can be selectively targeted are being developed and evaluated for guided imaging and therapy. Herein we highlight some initial efforts in combining the enhanced selectivity of nanoparticles and peptides with (68)Ga for use as molecular imaging probes.

OpenKnowledge for peer-to-peer experimentation in protein identification by MS/MS.

BACKGROUND: Traditional scientific workflow platforms usually run individual experiments with little evaluation and analysis of performance as required by automated experimentation in which scientists are being allowed to access numerous applicable workflows rather than being committed to a single one. Experimental protocols and data under a peer-to-peer environment could potentially be shared freely without any single point of authority to dictate how experiments should be run. In such environment it is necessary to have mechanisms by which each individual scientist (peer) can assess, locally, how he or she wants to be involved with others in experiments. This study aims to implement and demonstrate simple peer ranking under the OpenKnowledge peer-to-peer infrastructure by both simulated and real-world bioinformatics experiments involving multi-agent interactions. METHODS: A simulated experiment environment with a peer ranking capability was specified by the Lightweight Coordination Calculus (LCC) and automatically executed under the OpenKnowledge infrastructure. The peers such as MS/MS protein identification services (including web-enabled and independent programs) were made accessible as OpenKnowledge Components (OKCs) for automated execution as peers in the experiments. The performance of the peers in these automated experiments was monitored and evaluated by simple peer ranking algorithms. RESULTS: Peer ranking experiments with simulated peers exhibited characteristic behaviours, e.g., power law effect (a few dominant peers dominate), similar to that observed in the traditional Web. Real-world experiments were run using an interaction model in LCC involving two different types of MS/MS protein identification peers, viz., peptide fragment fingerprinting (PFF) and de novo sequencing with another peer ranking algorithm simply based on counting the successful and failed runs. This study demonstrated a novel integration and useful evaluation of specific proteomic peers and found MASCOT to be a dominant peer as judged by peer ranking. CONCLUSION: The simulated and real-world experiments in the present study demonstrated that the OpenKnowledge infrastructure with peer ranking capability can serve as an evaluative environment for automated experimentation.

Aza-retinoids as novel retinoid X receptor-specific agonists.

A new structurally simple series of potent lipophilic aza-retinoids RXR agonists has been developed. SAR studies for the N-alkyl-azadienoic acids described here demonstrate that the RXR activity profile is sensitive to the N-alkyl chain length. Further, we have expanded the work to include azadienoic acids, which exhibited many accessible conformations leading to a better understanding of the SAR around the series.

How can research organizations more effectively transfer research knowledge to decision makers?

Five questions--What should be transferred to decision makers? To whom should it be transferred? By whom? How? With what effect?--provide an organizing framework for a knowledge transfer strategy. Opportunities for improving how research organizations transfer research knowledge can be found in the differences between the answers suggested by our understanding of the research literature and those provided by research-organization directors asked to describe what they do. In Canada, these opportunities include developing actionable messages for decision makers (only 30 percent of research organizations frequently or always do this), developing knowledge-uptake skills in target audiences and knowledge-transfer skills in research organizations (only 20 to 22 percent frequently or always do this), and evaluating the impact of knowledge-transfer activities (only 8 to 12 percent frequently or always conduct an evaluation). Research funders can help research organizations take advantage of these opportunities.

Neurostimulation for refractory epilepsy.

Neurostimulation is an emerging treatment for refractory epilepsy. To date the precise mechanism of action remains to be elucidated. Better insight in the mechanism of action may identify seizure types or syndromes that respond to such a treatment and may guide the search for optimal stimulation parameters and finally improve clinical efficacy. In the past ten years some progress has been made through neurophysiological, neuroanatomical, neurochemical and cerebral blood flow studies in patients and animals undergoing vagus nerve stimulation (VNS). Interesting results have been found in VNS-treated patients that underwent evoked potential measurements, cerebrospinal fluid investigation, neuropsychological testing and PET, SPECT and fMRI testing. Desynchronisation of abnormal synchronous epileptic activity is one of the hypotheses on the mode of action that might primarily be responsible for an anti-seizure effect. There is however increasing evidence from research and clinical observation that VNS might establish a true and long-term anti-epileptic effect. It has been shown that VNS influences neurotransmission in the brain and provokes long-term changes in cerebral blood flow in areas crucial for epileptogenesis such as the thalamus and medial temporal lobe structures. Deep brain stimulation (DBS) for epilepsy has regained interest. Central nervous system structures known to play a key role in the epileptogenic network such as the thalamus and subthalamic nucleus have been targeted. Another approach is to target the ictal onset zone such as the medial temporal lobe. At Ghent University Hospital 10 patients have been treated with long-term amygdalohippocampal DBS. Several hypotheses have been raised for the mechanism of action of DBS for refractory seizures. Seizure reduction may be due to a microlesion caused by electrode insertion or by provoking a reversible functional lesion due to the effect of electrical current on hyperexcitable tissue. Neurophysiological techniques such as evoked potentials monitoring and intraoperative single unit potential recordings may guide correct electrode placement, individual DBS titration and elucidation of the mechanims of action of DBS for epilepsy.