Genomic stuff: Governing the (im)matter of life

Emphasizing the context of what has often been referred to as “scarce natural resources”, in particular forests, meadows, and fishing stocks, Elinor Ostrom’s important work Governing the commons (1990) presents an institutional framework for discussing the development and use of collective action with respect to environmental problems. In this article we discuss extensions of Ostrom’s approach to genes and genomes and explore its limits and usefulness. With the new genetics, we suggest, the biological gaze has not only been turned inward to the management and mining of the human body, also the very notion of the “biological” has been destabilized. This shift and destabilization, we argue, which is the result of human refashioning and appropriation of “life itself”, raises important questions about the relevance and applicability of Ostrom’s institutional framework in the context of what we call “genomic stuff”, genomic material, data, and information

Iron status in 6-y-old children: associations with growth and earlier iron status

To access publisher full text version of this article. Please click on the hyperlink in Additional Links fieldOBJECTIVE: To investigate the iron status of 6-y-old children and its association with growth and earlier iron status. DESIGN: In a cross-sectional study, children's body size measurements were recorded and blood samples taken near their sixth birthday. SUBJECTS: A sample of 188 children, randomly selected in two previous studies, was contacted, and 139(74%) agreed to participate. RESULTS: No children had iron deficiency anaemia, one was iron-deficient (serum ferritin (SF) or =15 microg/l (258+/-31%; n=49) (P=0.001). MCV at 2 y predicted weight gain from 2 to 6 y (B+/-s.e.=1.721+/-0.581; P=0.005; adj. R2=0.153) (n=44); also, children with SF or =15 microg/l (n=35) gained 9.6+/-2.8 kg (P=0.007), furthermore a difference was seen in proportional weight gain from 2 to 6 y between children with depleted iron stores at 2 y and not, or 156+/-13 vs 169+/-18% (P=0.038). CONCLUSION: The results suggest that low iron status at 1 and 2 y might lead to slower growth up to 6 y of age. Low iron status at 1 and 2 y and/or slower growth from 1 and 2 y up to 6 y might contribute to worse iron status at 6 y, while faster growth in early childhood is related to lower iron status

Associations of iron status with dietary and other factors in 6-year-old children

To access publisher full text version of this article. Please click on the hyperlink in Additional Links fieldOBJECTIVE: To investigate the associations of iron status at 6 years of age with dietary and other factors. DESIGN: In a cross-sectional study, children's dietary intakes (3-day weighed food record) were recorded, body size was measured and blood samples were taken near their sixth birthday. SUBJECTS: A sample of 188 children, from two previous studies (cohorts 1 and 2), was contacted, and 139 (74%) agreed to participate. RESULTS: Multiple regression analyses with dietary and other factors showed that meat and fish consumption, multivitamin/mineral supplement intake (both positively) and cow's milk product consumption (negatively) were associated with log serum ferritin (SF) (adjusted R (2)=0.125; P=0.028; n=129), and juices and residence (rural>urban) with haemoglobin (Hb) (adjusted R (2)=0.085; P=0.034; n=127). Of 21 multivitamin/mineral consumers, none had depleted iron stores compared to 21 iron-depleted of 108 non-consumers (P=0.024). Children living in rural areas (10,000 inhabitants) (82.1+/-3.2 fl; n=103) (P=0.048). Multiple regression analyses with dietary and other factors and growth showed in cohort 1 that residence (rural>urban), weight gain 0-1years (negatively), and meat and fish intake (positively) were associated with Hb (adjusted R (2)=0.323; P=0.030; n=51), meat and fish (positively) with both log SF (adjusted R (2)=0.069; P=0.035; n=52) and MCV (adjusted R (2)=0.064; P=0.035; n=52), and in cohort 2 cow's milk product consumption (negatively) was associated with log SF (adjusted R (2)=0.119; P=0.017; n=41) and residence (rural>urban) with MCV (adjusted R (2)=0.102; P=0.025; n=41). CONCLUSIONS: Consumption of meat and fish and possibly also juices, as well as multivitamin/mineral intake might affect iron status in 6-year-old children positively, whereas cow's milk product consumption might affect iron status negatively. Slower growth in the first year of life and rural residence are positively related to iron status of 6-year-olds

Thermoelectric power in one-dimensional Hubbard model

The thermoelectric power S is studied within the one-dimensional Hubbard model using the linear response theory and the numerical exact-diagonalization method for small systems. While both the diagonal and off-diagonal dynamical correlation functions of particle and energy current are singular within the model even at temperature T>0, S behaves regularly as a function of frequency $\omega$ and T. Dependence on the electron density n below the half-filling reveals a change of sign of S at n_0=0.73+/-0.07 due to strong correlations, in the whole T range considered. Approaching half-filling S is hole-like and can become large for U>>t although decreasing with T.Comment: 6 pages, 4 figure

The Regularizing Capacity of Metabolic Networks

Despite their topological complexity almost all functional properties of metabolic networks can be derived from steady-state dynamics. Indeed, many theoretical investigations (like flux-balance analysis) rely on extracting function from steady states. This leads to the interesting question, how metabolic networks avoid complex dynamics and maintain a steady-state behavior. Here, we expose metabolic network topologies to binary dynamics generated by simple local rules. We find that the networks' response is highly specific: Complex dynamics are systematically reduced on metabolic networks compared to randomized networks with identical degree sequences. Already small topological modifications substantially enhance the capacity of a network to host complex dynamic behavior and thus reduce its regularizing potential. This exceptionally pronounced regularization of dynamics encoded in the topology may explain, why steady-state behavior is ubiquitous in metabolism.Comment: 6 pages, 4 figure

Flux networks in metabolic graphs

A metabolic model can be represented as bipartite graph comprising linked reaction and metabolite nodes. Here it is shown how a network of conserved fluxes can be assigned to the edges of such a graph by combining the reaction fluxes with a conserved metabolite property such as molecular weight. A similar flux network can be constructed by combining the primal and dual solutions to the linear programming problem that typically arises in constraint-based modelling. Such constructions may help with the visualisation of flux distributions in complex metabolic networks. The analysis also explains the strong correlation observed between metabolite shadow prices (the dual linear programming variables) and conserved metabolite properties. The methods were applied to recent metabolic models for Escherichia coli, Saccharomyces cerevisiae, and Methanosarcina barkeri. Detailed results are reported for E. coli; similar results were found for the other organisms.Comment: 9 pages, 4 figures, RevTeX 4.0, supplementary data available (excel

Duality, thermodynamics, and the linear programming problem in constraint-based models of metabolism

It is shown that the dual to the linear programming problem that arises in constraint-based models of metabolism can be given a thermodynamic interpretation in which the shadow prices are chemical potential analogues, and the objective is to minimise free energy consumption given a free energy drain corresponding to growth. The interpretation is distinct from conventional non-equilibrium thermodynamics, although it does satisfy a minimum entropy production principle. It can be used to motivate extensions of constraint-based modelling, for example to microbial ecosystems.Comment: 4 pages, 2 figures, 1 table, RevTeX 4, final accepted versio

Stochastic Simulations of the Repressilator Circuit

The genetic repressilator circuit consists of three transcription factors, or repressors, which negatively regulate each other in a cyclic manner. This circuit was synthetically constructed on plasmids in {\it Escherichia coli} and was found to exhibit oscillations in the concentrations of the three repressors. Since the repressors and their binding sites often appear in low copy numbers, the oscillations are noisy and irregular. Therefore, the repressilator circuit cannot be fully analyzed using deterministic methods such as rate-equations. Here we perform stochastic analysis of the repressilator circuit using the master equation and Monte Carlo simulations. It is found that fluctuations modify the range of conditions in which oscillations appear as well as their amplitude and period, compared to the deterministic equations. The deterministic and stochastic approaches coincide only in the limit in which all the relevant components, including free proteins, plasmids and bound proteins, appear in high copy numbers. We also find that subtle features such as cooperative binding and bound-repressor degradation strongly affect the existence and properties of the oscillations.Comment: Accepted to PR

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics