Cutaneous Melanoma and Sentinel Lymph Node Biopsy

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Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses

<p>Abstract</p> <p>Background</p> <p>Optimization methods allow designing changes in a system so that specific goals are attained. These techniques are fundamental for metabolic engineering. However, they are not directly applicable for investigating the evolution of metabolic adaptation to environmental changes. Although biological systems have evolved by natural selection and result in well-adapted systems, we can hardly expect that actual metabolic processes are at the theoretical optimum that could result from an optimization analysis. More likely, natural systems are to be found in a feasible region compatible with global physiological requirements.</p> <p>Results</p> <p>We first present a new method for globally optimizing nonlinear models of metabolic pathways that are based on the Generalized Mass Action (GMA) representation. The optimization task is posed as a nonconvex nonlinear programming (NLP) problem that is solved by an outer-approximation algorithm. This method relies on solving iteratively reduced NLP slave subproblems and mixed-integer linear programming (MILP) master problems that provide valid upper and lower bounds, respectively, on the global solution to the original NLP. The capabilities of this method are illustrated through its application to the anaerobic fermentation pathway in <it>Saccharomyces cerevisiae</it>. We next introduce a method to identify the feasibility parametric regions that allow a system to meet a set of physiological constraints that can be represented in mathematical terms through algebraic equations. This technique is based on applying the outer-approximation based algorithm iteratively over a reduced search space in order to identify regions that contain feasible solutions to the problem and discard others in which no feasible solution exists. As an example, we characterize the feasible enzyme activity changes that are compatible with an appropriate adaptive response of yeast <it>Saccharomyces cerevisiae </it>to heat shock</p> <p>Conclusion</p> <p>Our results show the utility of the suggested approach for investigating the evolution of adaptive responses to environmental changes. The proposed method can be used in other important applications such as the evaluation of parameter changes that are compatible with health and disease states.</p

The interval between primary melanoma excision and sentinel node biopsy is not associated with survival in sentinel node positive patients - An EORTC Melanoma Group study.

BACKGROUND: Worldwide, sentinel node biopsy (SNB) is the recommended staging procedure for stage I/II melanoma. Most melanoma guidelines recommend re-excision plus SNB as soon as possible after primary excision. To date, there is no evidence to support this timeframe. AIM: To determine melanoma specific survival (MSS) for time intervals between excisional biopsy and SNB in SNB positive patients. METHODS: Between 1993 and 2008, 1080 patients were diagnosed with a positive SNB in nine Melanoma Group centers. We selected 1015 patients (94%) with known excisional biopsy date. Time interval was calculated from primary excision until SNB. Kaplan-Meier estimated MSS was calculated for different cutoff values. Multivariable analysis was performed to correct for known prognostic factors. RESULTS: Median age was 51 years (Inter Quartile Range (IQR) 40-62 years), 535 (53%) were men, 603 (59%) primary tumors were located on extremities. Median Breslow thickness was 3.00 mm (IQR 1.90-4.80 mm), 442 (44%) were ulcerated. Median follow-up was 36 months (IQR 20-62 months). Median time interval was 47 days (IQR 32-63 days). Median Breslow thickness was equal for both <47 days and ≥47 days interval: 3.00 mm (1.90-5.00 mm) vs 3.00 mm (1.90-4.43 mm) (p = 0.402). Sentinel node tumor burden was significantly higher in patients operated ≥47 days (p = 0.005). Univariate survival was not significantly different for median time interval. Multivariable analysis confirmed that time interval was no independent prognostic factor for MSS. CONCLUSIONS: Time interval from primary melanoma excision until SNB was no prognostic factor for MSS in this SNB positive cohort. This information can be used to counsel patients

Modelling and Analysis of Central Metabolism Operating Regulatory Interactions in Salt Stress Conditions in a L-Carnitine Overproducing E. coli Strain

Based on experimental data from E. coli cultures, we have devised a mathematical model in the GMA-power law formalism that describes the central and L-carnitine metabolism in and between two steady states, non-osmotic and hyperosmotic (0.3 M NaCl). A key feature of this model is the introduction of type of kinetic order, the osmotic stress kinetic orders (gOSn), derived from the power law general formalism, which represent the effect of osmotic stress in each metabolic process of the model

Boolean network simulations for life scientists

Modern life sciences research increasingly relies on computational solutions, from large scale data analyses to theoretical modeling. Within the theoretical models Boolean networks occupy an increasing role as they are eminently suited at mapping biological observations and hypotheses into a mathematical formalism. The conceptual underpinnings of Boolean modeling are very accessible even without a background in quantitative sciences, yet it allows life scientists to describe and explore a wide range of surprisingly complex phenomena. In this paper we provide a clear overview of the concepts used in Boolean simulations, present a software library that can perform these simulations based on simple text inputs and give three case studies. The large scale simulations in these case studies demonstrate the Boolean paradigms and their applicability as well as the advanced features and complex use cases that our software package allows. Our software is distributed via a liberal Open Source license and is freely accessible fro

Intragenic deletion in the LARGE gene causes Walker-Warburg syndrome

Intragenic homozygous deletions in the Large gene are associated with a severe neuromuscular phenotype in the myodystrophy (myd) mouse. These mutations result in a virtual lack of glycosylation of α-dystroglycan. Compound heterozygous LARGE mutations have been reported in a single human patient, manifesting with mild congenital muscular dystrophy (CMD) and severe mental retardation. These mutations are likely to retain some residual LARGE glycosyltransferase activity as indicated by residual α-dystroglycan glycosylation in patient cells. We hypothesized that more severe LARGE mutations are associated with a more severe CMD phenotype in humans. Here we report a 63-kb intragenic LARGE deletion in a family with Walker-Warburg syndrome (WWS), which is characterized by CMD, and severe structural brain and eye malformations. This finding demonstrates that LARGE gene mutations can give rise to a wide clinical spectrum, similar as for other genes that have a role in the post-translational modification of the α-dystroglycan protein

Diseased muscles that lack dystrophin or laminin-α2 have altered compositions and proliferation of mononuclear cell populations

BACKGROUND: Multiple types of mononucleate cells reside among the multinucleate myofibers in skeletal muscles and these mononucleate cells function in muscle maintenance and repair. How neuromuscular disease might affect different types of muscle mononucleate cells had not been determined. In this study, therefore, we examined how two neuromuscular diseases, dystrophin-deficiency and laminin-α2-deficiency, altered the proliferation and composition of different subsets of muscle-derived mononucleate cells. METHODS: We used fluorescence-activated cell sorting combined with bromodeoxyuridine labeling to examine proliferation rates and compositions of mononuclear cells in diseased and healthy mouse skeletal muscle. We prepared mononucleate cells from muscles of mdx (dystrophin-deficient) or Lama2(-/- )(laminin-α2-deficient) mice and compared them to cells from healthy control muscles. We enumerated subsets of resident muscle cells based on Sca-1 and CD45 expression patterns and determined the proliferation of each cell subset in vivo by BrdU incorporation. RESULTS: We found that the proliferation and composition of the mononucleate cells in dystrophin-deficient and laminin-α2-deficient diseased muscles are different than in healthy muscle. The mdx and Lama2(-/- )muscles showed similar significant increases in CD45(+ )cells compared to healthy muscle. Changes in proliferation, however, differed between the two diseases with proliferation increased in mdx and decreased in Lama2(-/- )muscles compared to healthy muscles. In particular, the most abundant Sca-1(-)/CD45(- )subset, which contains muscle precursor cells, had increased proliferation in mdx muscle but decreased proliferation in Lama2(-/- )muscles. CONCLUSION: The similar increases in CD45(+ )cells, but opposite changes in proliferation of muscle precursor cells, may underlie aspects of the distinct pathologies in the two diseases

Optimization of Time-Course Experiments for Kinetic Model Discrimination

Systems biology relies heavily on the construction of quantitative models of biochemical networks. These models must have predictive power to help unveiling the underlying molecular mechanisms of cellular physiology, but it is also paramount that they are consistent with the data resulting from key experiments. Often, it is possible to find several models that describe the data equally well, but provide significantly different quantitative predictions regarding particular variables of the network. In those cases, one is faced with a problem of model discrimination, the procedure of rejecting inappropriate models from a set of candidates in order to elect one as the best model to use for prediction