Maladaptation and the paradox of robustness in evolution

Background. Organisms use a variety of mechanisms to protect themselves against perturbations. For example, repair mechanisms fix damage, feedback loops keep homeostatic systems at their setpoints, and biochemical filters distinguish signal from noise. Such buffering mechanisms are often discussed in terms of robustness, which may be measured by reduced sensitivity of performance to perturbations. Methodology/Principal Findings. I use a mathematical model to analyze the evolutionary dynamics of robustness in order to understand aspects of organismal design by natural selection. I focus on two characters: one character performs an adaptive task; the other character buffers the performance of the first character against perturbations. Increased perturbations favor enhanced buffering and robustness, which in turn decreases sensitivity and reduces the intensity of natural selection on the adaptive character. Reduced selective pressure on the adaptive character often leads to a less costly, lower performance trait. Conclusions/Significance. The paradox of robustness arises from evolutionary dynamics: enhanced robustness causes an evolutionary reduction in the adaptive performance of the target character, leading to a degree of maladaptation compared to what could be achieved by natural selection in the absence of robustness mechanisms. Over evolutionary time, buffering traits may become layered on top of each other, while the underlying adaptive traits become replaced by cheaper, lower performance components. The paradox of robustness has widespread implications for understanding organismal design

Estimating the prevalence of breast cancer using a disease model: data problems and trends

BACKGROUND: Health policy and planning depend on quantitative data of disease epidemiology. However, empirical data are often incomplete or are of questionable validity. Disease models describing the relationship between incidence, prevalence and mortality are used to detect data problems or supplement missing data. Because time trends in the data affect their outcome, we compared the extent to which trends and known data problems affected model outcome for breast cancer. METHODS: We calculated breast cancer prevalence from Dutch incidence and mortality data (the Netherlands Cancer Registry and Statistics Netherlands) and compared this to regionally available prevalence data (Eindhoven Cancer Registry, IKZ). Subsequently, we recalculated the model adjusting for 1) limitations of the prevalence data, 2) a trend in incidence, 3) secondary primaries, and 4) excess mortality due to non-breast cancer deaths. RESULTS: There was a large discrepancy between calculated and IKZ prevalence, which could be explained for 60% by the limitations of the prevalence data plus the trend in incidence. Secondary primaries and excess mortality had relatively small effects only (explaining 17% and 6%, respectively), leaving a smaller part of the difference unexplained. CONCLUSION: IPM models can be useful both for checking data inconsistencies and for supplementing incomplete data, but their results should be interpreted with caution. Unknown data problems and trends may affect the outcome and in the absence of additional data, expert opinion is the only available judge

Autonomous decision-making against induced seismicity in deep fluid injections

The rise in the frequency of anthropogenic earthquakes due to deep fluid injections is posing serious economic, societal, and legal challenges to geo-energy and waste-disposal projects. We propose an actuarial approach to mitigate this risk, first by defining an autonomous decision-making process based on an adaptive traffic light system (ATLS) to stop risky injections, and second by quantifying a "cost of public safety" based on the probability of an injection-well being abandoned. The ATLS underlying statistical model is first confirmed to be representative of injection-induced seismicity, with examples taken from past reservoir stimulation experiments (mostly from Enhanced Geothermal Systems, EGS). Then the decision strategy is formalized: Being integrable, the model yields a closed-form ATLS solution that maps a risk-based safety standard or norm to an earthquake magnitude not to exceed during stimulation. Finally, the EGS levelized cost of electricity (LCOE) is reformulated in terms of null expectation, with the cost of abandoned injection-well implemented. We find that the price increase to mitigate the increased seismic risk in populated areas can counterbalance the heat credit. However this "public safety cost" disappears if buildings are based on earthquake-resistant designs or if a more relaxed risk safety standard or norm is chosen.Comment: 8 pages, 4 figures, conference (International Symposium on Energy Geotechnics, 26-28 September 2018, Lausanne, Switzerland

Maternal Hypothyroxinemia During Pregnancy and Growth of the Fetal and Infant Head

Severe maternal thyroid dysfunction during pregnancy affects fetal brain growth and corticogenesis. This study focused on the effect of maternal hypothyroxinemia during early pregnancy on growth of the fetal and infant head. In a population-based birth cohort, we assessed thyroid status in early pregnancy (median 13.4, 90% range 10.8-17.2), in 4894 women, and measured the prenatal and postnatal head size of their children at 5 time points. Hypothyroxinemia was defined as normal thyroid-stimulating hormone levels and free thyroxine-4 concentrations below the 10th percentile. Statistical analysis was performed using linear generalized estimating equation. Maternal hypothyroxinemia was associated with larger fetal and infant head size (overall estimate beta: 1.38, 95% confidence interval 0.56; 2.19, P = .001). In conclusion, in the general population, even small variations in maternal thyroid function during pregnancy may affect the developing head of the young child

Maternal Hypothyroxinemia During Pregnancy and Growth of the Fetal and Infant Head

Severe maternal thyroid dysfunction during pregnancy affects fetal brain growth and corticogenesis. This study focused on the effect of maternal hypothyroxinemia during early pregnancy on growth of the fetal and infant head. In a population-based birth cohort, we assessed thyroid status in early pregnancy (median 13.4, 90% range 10.8-17.2), in 4894 women, and measured the prenatal and postnatal head size of their children at 5 time points. Hypothyroxinemia was defined as normal thyroid-stimulating hormone levels and free thyroxine-4 concentrations below the 10th percentile. Statistical analysis was performed using linear generalized estimating equation. Maternal hypothyroxinemia was associated with larger fetal and infant head size (overall estimate beta: 1.38, 95% confidence interval 0.56; 2.19, P = .001). In conclusion, in the general population, even small variations in maternal thyroid function during pregnancy may affect the developing head of the young child

Evaluation of rate law approximations in bottom-up kinetic models of metabolism.

BackgroundThe mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.ResultsIn this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.ConclusionsOverall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches

A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics

BACKGROUND: Dynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (dis)functioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so many parameters that their identifiability from experimental data forms a serious problem. Recently, approximative rate equations, based on the linear logarithmic (linlog) format have been proposed as a suitable alternative with fewer parameters. RESULTS: In this paper we present a method for estimation of the kinetic model parameters, which are equal to the elasticities defined in Metabolic Control Analysis, from metabolite data obtained from dynamic as well as steady state perturbations, using the linlog kinetic format. Additionally, we address the question of parameter identifiability from dynamic perturbation data in the presence of noise. The method is illustrated using metabolite data generated with a dynamic model of the glycolytic pathway of Saccharomyces cerevisiae based on mechanistic rate equations. Elasticities are estimated from the generated data, which define the complete linlog kinetic model of the glycolysis. The effect of data noise on the accuracy of the estimated elasticities is presented. Finally, identifiable subset of parameters is determined using information on the standard deviations of the estimated elasticities through Monte Carlo (MC) simulations. CONCLUSION: The parameter estimation within the linlog kinetic framework as presented here allows the determination of the elasticities directly from experimental data from typical dynamic and/or steady state experiments. These elasticities allow the reconstruction of the full kinetic model of Saccharomyces cerevisiae, and the determination of the control coefficients. MC simulations revealed that certain elasticities are potentially unidentifiable from dynamic data only. Addition of steady state perturbation of enzyme activities solved this problem

Feed intake and production parameters of lactating crossbred cows fed maize-based diets of stover, silage or quality protein silage

Thirty-six Boran × Friesian dairy cows (392 ± 12 kg; mean ± SD) in early parity were used in a randomised complete block design. Cows were blocked by parity into three blocks of 12 animals and offered normal maize (NM) stover (T1), NM silage (T2) or quality protein maize (QPM) silage (T3) basal diets supplemented with a similar concentrate mix. Feed intake, body weight and condition changes and milk yield and composition were assessed. The daily intake of DM, OM, NDF and ADF for cows fed the NM stover-based diet was higher (P < 0.05) than for the cows fed the NM silage and QPM silage-based diets. However, the daily intake of DOM (9.3 kg) and ME (140.8 MJ) for cows on QPM silage-based diet was higher (P < 0.05) than for cows on NM stover-based diet (8.4 kg and 124.2 MJ) and NM silage-based diet (7.9 kg and 119.1 MJ). Body weight of cows was affected (P < 0.05) by the diet, but diet had no effect (P > 0.05) on body condition score, milk yield and milk composition. The digestible organic matter in the NM stover-based diet (724 g/kg DM) was lower (P < 0.05) than that in the NM (770 g/kg DM) and QPM silage-based diet (762 g/kg DM). It was concluded that the performances of the cows on the NM silage and QPM silage diets were similar and were not superior to that of the NM stover-based diet