Systems approaches and algorithms for discovery of combinatorial therapies

Effective therapy of complex diseases requires control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells. In this review paper we describe the main current and proposed approaches to the design of combinatorial therapies, including the empirical methods used now by clinicians and alternative approaches suggested recently by several authors. New approaches for designing combinations arising from systems biology are described. We discuss in special detail the design of algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. The use of new technology for high-throughput measurements is key to these new approaches to combination therapy and essential for the characterization of control landscapes and implementation of the algorithms. Combinatorial optimization in medical therapy is also compared with the combinatorial optimization of engineering and materials science and similarities and differences are delineated.Comment: 25 page

Molecular mechanisms of increased cerebral vulnerability after repeated mild blast-induced traumatic brain injury

AbstractThe consequences of a mild traumatic brain injury can be especially severe if it is repeated within the period of increased cerebral vulnerability (ICV) that follows the initial insult. To better understand the molecular mechanisms that contribute to ICV, we exposed rats to different levels of mild blast overpressure (5 exposures; total pressure range: 15.54–19.41psi or 107.14–133.83kPa) at a rate of 1 per 30min, monitored select physiological parameters, and assessed behavior. Two days post-injury or sham, we determined changes in protein biomarkers related to various pathologies in behaviorally relevant brain regions and in plasma. We found that oxygen saturation and heart rate were transiently depressed following mild blast exposure and that injured rats exhibited significantly increased anxiety- and depression-related behaviors. Proteomic analyses of the selected brain regions showed evidence of substantial oxidative stress and vascular changes, altered cell adhesion, and inflammation predominantly in the prefrontal cortex. Importantly, these pathological changes as well as indications of neuronal and glial cell loss/damage were also detected in the plasma of injured rats. Our findings illustrate some of the complex molecular changes that contribute to the period of ICV in repeated mild blast-induced traumatic brain injury. Further studies are needed to determine the functional and temporal relationship between the various pathomechanisms. The validation of these and other markers can help to diagnose individuals with ICV using a minimally invasive procedure and to develop evidence-based treatments for chronic neuropsychiatric conditions

Taurolidine Antiadhesive Properties on Interaction with E. coli; Its Transformation in Biological Environment and Interaction with Bacteria Cell Wall

The taurine amino-acid derivative, taurolidine, bis-(1,1-dioxoperhydro-1,2,4-thiabiazinyl–4)methane, shows broad antibacterial action against gram-positive and gram-negative bacteria, mycobacteria and some clinically relevant fungi. It inhibits, in vitro, the adherence of Escherichia coli and Staphylococcus aureus to human epithelial and fibroblast cells. Taurolidine is unstable in aqueous solution and breaks down into derivatives which are thought to be responsible for the biological activity. To understand the taurolidine antibacterial mechanism of action, we provide the experimental single crystal X-ray diffraction results together with theoretical methods to characterize the hydrolysis/decomposition reactions of taurolidine. The crystal structure features two independent molecules linked through intermolecular H-bonds with one of them somewhat positively charged. Taurolidine in a biological environment exists in equilibrium with taurultam derivatives and this is described theoretically as a 2-step process without an energy barrier: formation of cationic taurolidine followed by a nucleophilic attack of O(hydroxyl) on the exocyclic C(methylene). A concerted mechanism describes the further hydrolysis of the taurolidine derivative methylol-taurultam. The interaction of methylol-taurultam with the diaminopimelic NH2 group in the E. coli bacteria cell wall (peptidoglycan) has a negative ΔG value (−38.2 kcal/mol) but a high energy barrier (45.8 kcal/mol) suggesting no reactivity. On the contrary, taurolidine docking into E. coli fimbriae protein, responsible for bacteria adhesion to the bladder epithelium, shows it has higher affinity than mannose (the natural substrate), whereas methylol-taurultam and taurultam are less tightly bound. Since taurolidine is readily available because it is administered in high doses after peritonitis surgery, it may successfully compete with mannose explaining its effectiveness against bacterial infections at laparoscopic lesions

Canonical Correlation Analysis and Partial Least Squares for identifying brain-behaviour associations: a tutorial and a comparative study

Canonical Correlation Analysis (CCA) and Partial Least Squares (PLS) are powerful multivariate methods for capturing associations across two modalities of data (e.g., brain and behaviour). However, when the sample size is similar or smaller than the number of variables in the data, CCA and PLS models may overfit, i.e., find spurious associations that generalise poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimising the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and the Alzheimer's Disease Neuroimaging Initiative (both of n>500). We use both low and high dimensionality versions of each data (i.e., ratios between sample size and variables in the range of ∼1-10 and ∼0.1-0.01) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial

TVL₁ Planarity Regularization for 3D Shape Approximation

The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within. This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy

Reusing integer homology information of binary digital images

In this paper, algorithms for computing integer (co)homology of a simplicial complex of any dimension are designed, extending the work done in [1,2,3]. For doing this, the homology of the object is encoded in an algebraic-topological format (that we call AM-model). Moreover, in the case of 3D binary digital images, having as input AM-models for the images I and J, we design fast algorithms for computing the integer homology of I ∪J, I ∩J and I ∖J

Gene Expression Signature of Normal Cell-of-Origin Predicts Ovarian Tumor Outcomes

The potential role of the cell-of-origin in determining the tumor phenotype has been raised, but not adequately examined. We hypothesized that distinct cells-of-origin may play a role in determining ovarian tumor phenotype and outcome. Here we describe a new cell culture medium for in vitro culture of paired normal human ovarian (OV) and fallopian tube (FT) epithelial cells from donors without cancer. While these cells have been cultured individually for short periods of time, to our knowledge this is the first long-term culture of both cell types from the same donors. Through analysis of the gene expression profiles of the cultured OV/FT cells we identified a normal cell-of-origin gene signature that classified primary ovarian cancers into OV-like and FT-like subgroups; this classification correlated with significant differences in clinical outcomes. The identification of a prognostically significant gene expression signature derived solely from normal untransformed cells is consistent with the hypothesis that the normal cell-of-origin may be a source of ovarian tumor heterogeneity and the associated differences in tumor outcome

Characterization of twenty-five ovarian tumour cell lines that phenocopy primary tumours

Currently available human tumour cell line panels consist of a small number of lines in each lineage that generally fail to retain the phenotype of the original patient tumour. Here we develop a cell culture medium that enables us to routinely establish cell lines from diverse subtypes of human ovarian cancers with >95% efficiency. Importantly, the 25 new ovarian tumour cell lines described here retain the genomic landscape, histopathology and molecular features of the original tumours. Furthermore, the molecular profile and drug response of these cell lines correlate with distinct groups of primary tumours with different outcomes. Thus, tumour cell lines derived using this methodology represent a significantly improved platform to study human tumour pathophysiology and response to therapy