The Metabolic Core and Catalytic Switches Are Fundamental Elements in the Self-Regulation of the Systemic Metabolic Structure of Cells

[Background] Experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a metabolic core formed by a set of enzymatic reactions which are always active under all environmental conditions, while the rest of catalytic processes are only intermittently active. The reactions of the metabolic core are essential for biomass formation and to assure optimal metabolic performance. The on-off catalytic reactions and the metabolic core are essential elements of a Systemic Metabolic Structure which seems to be a key feature common to all cellular organisms. [Methodology/Principal Findings] In order to investigate the functional importance of the metabolic core we have studied different catalytic patterns of a dissipative metabolic network under different external conditions. The emerging biochemical data have been analysed using information-based dynamic tools, such as Pearson's correlation and Transfer Entropy (which measures effective functionality). Our results show that a functional structure of effective connectivity emerges which is dynamical and characterized by significant variations of bio-molecular information flows. [Conclusions/Significance] We have quantified essential aspects of the metabolic core functionality. The always active enzymatic reactions form a hub –with a high degree of effective connectivity- exhibiting a wide range of functional information values being able to act either as a source or as a sink of bio-molecular causal interactions. Likewise, we have found that the metabolic core is an essential part of an emergent functional structure characterized by catalytic modules and metabolic switches which allow critical transitions in enzymatic activity. Both, the metabolic core and the catalytic switches in which also intermittently-active enzymes are involved seem to be fundamental elements in the self-regulation of the Systemic Metabolic Structure.Consejo Superior de Investigaciones Cientificas (CSIC),grant 201020I026. Ministerio de Ciencia e Innovacion (MICINN). Programa Ramon y Cajal. Campus de Excelencia Internacional CEI BioTIC GENIL, grant PYR-2010-14. Junta de Andalucia, grant P09-FQM-4682

MORTALIDAD ATRIBUIBLE AL CONSUMO DE TABACO EN LOS AÑOS 1987 Y 1997 EN CASTILLA-LA MANCHA, ESPAÑA

Fundamento: El consumo de tabaco constituye un importante problema de salud pública, siendo una de las principales causas de morbilidad y mortalidad evitable y prematura. El objetivo de este trabajo es describir la mortalidad atribuible al consumo de tabaco en Castilla-La Mancha en los años 1987 y 1997. Método: Las defunciones por edad, sexo y causa se obtuvieron del Registro de Mortalidad de Castilla-La Mancha. A partir de las Encuestas Nacionales de Salud de 1987 y 1997 se tomaron los porcentajes de nunca fumadores, fumadores y exfumadores de la población por edad y sexo. Los riesgos relativos de muerte se obtuvieron del Cancer Prevention Study II, llevado a cabo en los Estados Unidos de América. Se calculó la proporción de muertes atribuibles al tabaco para cada año, sexo y grupo de edad a partir de la fracción etiológica poblacional. Asimismo, se calcularon los años potenciales de vida perdidos y la media de años potenciales de vida perdidos. Resultados: Durante los periodos estudiados se pueden atribuir al consumo de tabaco el 18% de todas las muertes en Castilla La Mancha. La mortalidad es más elevada en hombres que en mujeres, y las categorías diagnósticas que más contribuyeron fueron el cáncer tráqueobroncopulmonar (24,3%) en los hombres y las enfermedades del aparato circulatorio (24,28%) en las mujeres, coincidiendo con las más frecuentemente responsables de años potenciales de vida perdidos. Conclusiones: Cada día en Castilla-La Mancha fallecen 8 personas por causas atribuibles al consumo de tabaco. Las medidas introducidas para controlar el tabaquismo son insuficientes

The metabolic memories are local minima of the DMN dynamics (case ).

<p>This figure is similar than Fig. 3 but now there are metabolic memory encoded in the weights. A,C: the first metabolic memory in both cases LSE and worse than LSE conditions. B,D: similar than in A,C but for the second metabolic memory. A,B: stimulation condition I (only stimulus S1). C,D: condition II (both stimuli S1 and S2). In this case of , the LSE solution has been found by using an genetic algorithm for minimization of the cost given by Eq. (36), details in the text. The temperature parameter is fixed to T = 0.7.</p

Weights connectivity matrix learned from the DMN by the Boltzmann machine.

<p>For the two stimulation conditions, we plot the two matrices of weights connectivity that are the result of the learning by the Boltzmann machine. Notice that although the mean values in both matrices are small (2.71 in panel A and 0.37 in panel B), the variance in panel A is much higher compared to the one in panel B (A: 34.36, B: 2.83). The tables with these values and their corresponding statistics are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058284#pone-0058284-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058284#pone-0058284-t003" target="_blank">3</a>.</p

Matrix of weights connectivity: condition I (only stimulus S1).

<p>Each cell in the table corresponds with a given weight; ith row, jth column is correponding to . Notice that the matrix is symmetric and with the principal diagonal equal to zero. Mean val 2.71; std dev 34.36; min val −150.11; max val 151.23.</p

Dynamic catalytic behavior in the dissipative metabolic network (DMN).

<p>A,D: metabolic network formed by 18 self-organized multienzymatic complexes (metabolic subsystems); it is shown the interconnection by substrate fluxes and the substrate input fluxes. For simplification in the illustration, the topological architecture of the regulatory signals affecting the DMN is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058284#pone.0058284.s001" target="_blank">Fig. S1</a>. The network was studied in two different external conditions: condition I, in which only the stationary stimulus S1 was affecting the DMN (left column in panel) and condition II, with two substrate input fluxes S1 and S2 (right column). A: A systemic metabolic structure spontaneously emerges in the network in which the enzymatic subsystem MSb12 is always active (i.e. the metabolic core, red circle) whereas the rest of enzymatic subsystems exhibit on-off changing states (white circles). D: In the condition II the network preserves the metabolic core (red circle) but the MSb15 becomes in a permanent off-state (black circle). B: for condition I, an example of the enzymatic activity of the MSb12 (metabolic core) which presents different catalytic transitions between periodic oscillations and steady states, and (E) same as in B but for condition II. C,F: Time series of the amplitude of several catalytic activity oscillations as a function of the iterations number. Green lines represent the average value of the amplitude in the whole time series. In blue we are plotting the two-states representation of the amplitude time series, 1 for values higher than the green line and 0 for lower values. This figure was slightly adapted from Fig. 2 in (De la Fuente et al. 2011).</p

Vector of thersholds: condition II (both stimuli S1 and S2).

<p>Each cell for each threshold value. Mean val −1.26; std dev 4.29; min val −7.33; max val 6.16.</p><p>Note: The NaN number in position 15 is because the MSb15 is in an off-state, which is equivalent to have a positive infinite threshold. This value has been removed and not considered in the calculation of both mean and standard deviation.</p

Matrix of weights connectivity: condition II (both stimuli S1 and S2).

<p>Similar to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058284#pone-0058284-t001" target="_blank">Table 1</a>, ith row, jth column is corresponding to . Notice that the matrix is symmetric and with the principal diagonal equal to zero. Mean val 0.37; std dev 2.83; min val −6.66; max val 18.31.</p