Automated design of gene circuits with optimal mushroom-bifurcation behavior

Recent advances in synthetic biology are enabling exciting technologies, including the next generation of biosensors, the rational design of cell memory, modulated synthetic cell differentiation, and generic multifunctional biocircuits. These novel applications require the design of gene circuits leading to sophisticated behaviors and functionalities. At the same time, designs need to be kept minimal to avoid compromising cell viability. Bifurcation theory addresses such challenges by associating circuit dynamical properties with molecular details of its design. Nevertheless, incorporating bifurcation analysis into automated design processes has not been accomplished yet. This work presents an optimization-based method for the automated design of synthetic gene circuits with specified bifurcation diagrams that employ minimal network topologies. Using this approach, we designed circuits exhibiting the mushroom bifurcation, distilled the most robust topologies, and explored its multifunctional behavior. We then outline potential applications in biosensors, memory devices, and synthetic cell differentiation

Implementing Parallel Differential Evolution on Spark

[Abstract] Metaheuristics are gaining increased attention as an efficient way of solving hard global optimization problems. Differential Evolution (DE) is one of the most popular algorithms in that class. However, its application to realistic problems results in excessive computation times. Therefore, several parallel DE schemes have been proposed, most of them focused on traditional parallel programming interfaces and infrastruc- tures. However, with the emergence of Cloud Computing, new program- ming models, like Spark, have appeared to suit with large-scale data processing on clouds. In this paper we investigate the applicability of Spark to develop parallel DE schemes to be executed in a distributed environment. Both the master-slave and the island-based DE schemes usually found in the literature have been implemented using Spark. The speedup and efficiency of all the implementations were evaluated on the Amazon Web Services (AWS) public cloud, concluding that the island- based solution is the best suited to the distributed nature of Spark. It achieves a good speedup versus the serial implementation, and shows a decent scalability when the number of nodes grows.[Resumen] Las metaheurísticas están recibiendo una atención creciente como técnica eficiente en la resolución de problemas difíciles de optimización global. Differential Evolution (DE) es una de las metaheurísticas más populares, sin embargo su aplicación en problemas reales deriva en tiempos de cómputo excesivos. Por ello se han realizado diferentes propuestas para la paralelización del DE, en su mayoría utilizando infraestructuras e interfaces de programación paralela tradicionales. Con la aparición de la computación en la nube también se han propuesto nuevos modelos de programación, como Spark, que permiten manejar el procesamiento de datos a gran escala en la nube. En este artículo investigamos la aplicabilidad de Spark en el desarrollo de implementaciones paralelas del DE para su ejecución en entornos distribuidos. Se han implementado tanto la aproximación master-slave como la basada en islas, que son las más comunes. También se han evaluado la aceleración y la eficiencia de todas las implementaciones usando el cloud público de Amazon (AWS, Amazon Web Services), concluyéndose que la implementación basada en islas es la más adecuada para el esquema de distribución usado por Spark. Esta implementación obtiene una buena aceleración en relación a la implementación serie y muestra una escalabilidad bastante buena cuando el número de nodos aumenta.[Resume] As metaheurísticas están recibindo unha atención a cada vez maior como técnica eficiente na resolución de problemas difíciles de optimización global. Differential Evolution (DE) é unha das metaheurísticas mais populares, ainda que a sua aplicación a problemas reais deriva en tempos de cómputo excesivos. É por iso que se propuxeron diferentes esquemas para a paralelización do DE, na sua maioría utilizando infraestruturas e interfaces de programación paralela tradicionais. Coa aparición da computación na nube tamén se propuxeron novos modelos de programación, como Spark, que permiten manexar o procesamento de datos a grande escala na nube. Neste artigo investigamos a aplicabilidade de Spark no desenvolvimento de implementacións paralelas do DE para a sua execución en contornas distribuidas. Implementáronse tanto a aproximación master-slave como a baseada en illas, que son as mais comúns. Tamén se avaliaron a aceleración e a eficiencia de todas as implementacións usando o cloud público de Amazon (AWS, Amazon Web Services), tirando como conclusión que a implementación baseada en illas é a mais acaida para o esquema de distribución usado por Spark. Esta implementación obtén unha boa aceleración en relación á implementación serie e amosa unha escalabilidade bastante boa cando o número de nos aumenta.Ministerio de Economía y Competitividad; DPI2014-55276-C5-2-RXunta de Galicia; GRC2013/055Xunta de Galicia; R2014/04

Pyoderma gangrenosum after caesarean section: a case report

BACKGROUND: Pyoderma gangrenosum is a rare ulcerative skin disease. The diagnosis is based on clinical features and excluding other causes of skin ulcers, as it does not have characteristic histopathology or laboratory findings. The etiology is poorly understood. Lesions can develop spontaneously, after surgery or after trauma. CASE PRESENTATION: We present the case of a 32-year-old woman with ulcerative wound defect after caesarean section. The wound was not healing despite standard wound care and antibiotic treatment. Pyoderma gangrenosum was diagnosed and after high dose corticosteroids wound healing started. CONCLUSION: Early diagnosis and subsequent treatment of pyoderma gangrenosum are crucial for limiting scar tissue. Diagnosis of pyoderma gangrenosum could easily be missed since gynaecologists are rarely confronted with this disorder

A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

This is a post-peer-review, pre-copyedit version of an article published in Cluster Computing. The final authenticated version is available online at: https://doi.org/10.1007/s10586-017-0860-1[Abstract] Metaheuristics are gaining increasing recognition in many research areas, computational systems biology among them. Recent advances in metaheuristics can be helpful in locating the vicinity of the global solution in reasonable computation times, with Differential Evolution (DE) being one of the most popular methods. However, for most realistic applications, DE still requires excessive computation times. With the advent of Cloud Computing effortless access to large number of distributed resources has become more feasible, and new distributed frameworks, like Spark, have been developed to deal with large scale computations on commodity clusters and cloud resources. In this paper we propose a parallel implementation of an enhanced DE using Spark. The proposal drastically reduces the execution time, by means of including a selected local search and exploiting the available distributed resources. The performance of the proposal has been thoroughly assessed using challenging parameter estimation problems from the domain of computational systems biology. Two different platforms have been used for the evaluation, a local cluster and the Microsoft Azure public cloud. Additionally, it has been also compared with other parallel approaches, another cloud-based solution (a MapReduce implementation) and a traditional HPC solution (a MPI implementation)Ministerio de Economía y Competitividad; DPI2014-55276-C5-2-RMinisterio de Economía y Competitividad; TIN2013-42148-PMinisterio de Economía y Competitividad; TIN2016-75845-PXunta de Galicia ; R2016/045Xunta de Galicia; GRC2013/05

Modulating gut microbiota in a mouse model of Graves' orbitopathy and its impact on induced disease

BACKGROUND: Graves' disease (GD) is an autoimmune condition in which autoantibodies to the thyrotropin receptor (TSHR) cause hyperthyroidism. About 50% of GD patients also have Graves' orbitopathy (GO), an intractable disease in which expansion of the orbital contents causes diplopia, proptosis and even blindness. Murine models of GD/GO, developed in different centres, demonstrated significant variation in gut microbiota composition which correlated with TSHR-induced disease heterogeneity. To investigate whether correlation indicates causation, we modified the gut microbiota to determine whether it has a role in thyroid autoimmunity. Female BALB/c mice were treated with either vancomycin, probiotic bacteria, human fecal material transfer (hFMT) from patients with severe GO or ddH2O from birth to immunization with TSHR-A subunit or beta-galactosidase (βgal; age ~ 6 weeks). Incidence and severity of GD (TSHR autoantibodies, thyroid histology, thyroxine level) and GO (orbital fat and muscle histology), lymphocyte phenotype, cytokine profile and gut microbiota were analysed at sacrifice (~ 22 weeks). RESULTS: In ddH2O-TSHR mice, 84% had pathological autoantibodies, 67% elevated thyroxine, 77% hyperplastic thyroids and 70% orbital pathology. Firmicutes were increased, and Bacteroidetes reduced relative to ddH2O-βgal; CCL5 was increased. The random forest algorithm at the genus level predicted vancomycin treatment with 100% accuracy but 74% and 70% for hFMT and probiotic, respectively. Vancomycin significantly reduced gut microbiota richness and diversity compared with all other groups; the incidence and severity of both GD and GO also decreased; reduced orbital pathology correlated positively with Akkermansia spp. whilst IL-4 levels increased. Mice receiving hFMT initially inherited their GO donors' microbiota, and the severity of induced GD increased, as did the orbital brown adipose tissue volume in TSHR mice. Furthermore, genus Bacteroides, which is reduced in GD patients, was significantly increased by vancomycin but reduced in hFMT-treated mice. Probiotic treatment significantly increased CD25+ Treg cells in orbital draining lymph nodes but exacerbated induced autoimmune hyperthyroidism and GO. CONCLUSIONS: These results strongly support a role for the gut microbiota in TSHR-induced disease. Whilst changes to the gut microbiota have a profound effect on quantifiable GD endocrine and immune factors, the impact on GO cellular changes is more nuanced. The findings have translational potential for novel, improved treatments. Video abstract

An iterative identification procedure for dynamic modeling of biochemical networks

<p>Abstract</p> <p>Background</p> <p>Mathematical models provide abstract representations of the information gained from experimental observations on the structure and function of a particular biological system. Conferring a predictive character on a given mathematical formulation often relies on determining a number of non-measurable parameters that largely condition the model's response. These parameters can be identified by fitting the model to experimental data. However, this fit can only be accomplished when identifiability can be guaranteed.</p> <p>Results</p> <p>We propose a novel iterative identification procedure for detecting and dealing with the lack of identifiability. The procedure involves the following steps: 1) performing a structural identifiability analysis to detect identifiable parameters; 2) globally ranking the parameters to assist in the selection of the most relevant parameters; 3) calibrating the model using global optimization methods; 4) conducting a practical identifiability analysis consisting of two (<it>a priori </it>and <it>a posteriori</it>) phases aimed at evaluating the quality of given experimental designs and of the parameter estimates, respectively and 5) optimal experimental design so as to compute the scheme of experiments that maximizes the quality and quantity of information for fitting the model.</p> <p>Conclusions</p> <p>The presented procedure was used to iteratively identify a mathematical model that describes the NF-<it>κ</it>B regulatory module involving several unknown parameters. We demonstrated the lack of identifiability of the model under typical experimental conditions and computed optimal dynamic experiments that largely improved identifiability properties.</p

Predictors of mortality of patients with acute respiratory failure secondary to chronic obstructive pulmonary disease admitted to an intensive care unit: A one year study

BACKGROUND: Patients with acute exacerbation of chronic obstructive pulmonary disease (COPD) commonly require hospitalization and admission to intensive care unit (ICU). It is useful to identify patients at the time of admission who are likely to have poor outcome. This study was carried out to define the predictors of mortality in patients with acute exacerbation of COPD and to device a scoring system using the baseline physiological variables for prognosticating these patients. METHODS: Eighty-two patients with acute respiratory failure secondary to COPD admitted to medical ICU over a one-year period were included. Clinical and demographic profile at the time of admission to ICU including APACHE II score and Glasgow coma scale were recorded at the time of admission to ICU. In addition, acid base disorders, renal functions, liver functions and serum albumin, were recorded at the time of presentation. Primary outcome measure was hospital mortality. RESULTS: Invasive ventilation was required in 69 patients (84.1%). Fifty-two patients survived to hospital discharge (63.4%). APACHE II score at the time of admission to ICU {odds ratio (95 % CI): 1.32 (1.138–1.532); p < 0.001} and serum albumin (done within 24 hours of admission) {odds ratio (95 % CI): 0.114 (0.03-0.432); p = 0.001}. An equation, constructed using the adjusted odds ratio for the two parameters, had an area under the ROC curve of 91.3%. For the choice of cut-off, sensitivity, specificity, positive and negative predictive value for predicting outcome was 90%, 86.5%, 79.4% and 93.7%. CONCLUSION: APACHE II score at admission and SA levels with in 24 hrs after admission are independent predictors of mortality for patients with COPD admitted to ICU. The equation derived from these two parameters is useful for predicting outcome of these patients

Native aggregation as a cause of origin of temporary cellular structures needed for all forms of cellular activity, signaling and transformations

According to the hypothesis explored in this paper, native aggregation is genetically controlled (programmed) reversible aggregation that occurs when interacting proteins form new temporary structures through highly specific interactions. It is assumed that Anfinsen's dogma may be extended to protein aggregation: composition and amino acid sequence determine not only the secondary and tertiary structure of single protein, but also the structure of protein aggregates (associates). Cell function is considered as a transition between two states (two states model), the resting state and state of activity (this applies to the cell as a whole and to its individual structures). In the resting state, the key proteins are found in the following inactive forms: natively unfolded and globular. When the cell is activated, secondary structures appear in natively unfolded proteins (including unfolded regions in other proteins), and globular proteins begin to melt and their secondary structures become available for interaction with the secondary structures of other proteins. These temporary secondary structures provide a means for highly specific interactions between proteins. As a result, native aggregation creates temporary structures necessary for cell activity