Robustness versus productivity during evolutionary community assembly: short-term synergies and long-term trade-offs.

The realization that evolutionary feedbacks need to be considered to fully grasp ecological dynamics has sparked interest in the effect of evolution on community properties like coexistence and productivity. However, little is known about the evolution of community robustness and productivity along diversification processes in species-rich systems. We leverage the recent structural approach to coexistence together with adaptive dynamics to study such properties and their relationships in a general trait-based model of competition on a niche axis. We show that the effects of coevolution on coexistence are two-fold and contrasting depending on the time scale considered. In the short term, evolution of niche differentiation strengthens coexistence, while long-term diversification leads to niche packing and decreased robustness. Moreover, we find that coevolved communities tend to be on average more robust and more productive than non-evolutionary assemblages. We illustrate how our theoretical predictions echo in observed empirical patterns and the implications of our results for empiricists and applied ecologists. We suggest that some of our results such as the improved robustness of Evolutionarily Stable Communities could be tested experimentally in suitable model systems.

Influence of oxygen uptake through the liver surface on the metabolism of ex vivo perfused liver during hypoxia.

The low quality of transplants having undergone hypoxic injury can lead to postoperative complications. The aim of the present research is to estimate, by means of mathematical modeling, how the process of oxygen uptake through the liver surface influences the metabolism of ex vivo perfused liver under hypoxia. The value of oxygen uptake through the surface was established to depend on the degree of oxygenation of the perfusion medium. A decrease in the oxygenation of the perfusion medium resulted in a decreased oxygen uptake through the liver surface. Stoichiometric modeling of the liver metabolism shows that upon the decreased oxygenation of the perfusion medium more energy is required for the process of oxygen uptake through the surface even at a lower level as compared to the normal oxygen supply. The application of the Pareto optimality allows estimating the optimum distribution of the energy resources in liver under ex vivo conditions. Both upon the normal and decreased oxygenation of the perfusion medium, the phenomenon of "free competition" for the resource was observed, with the energy being optimally distributed among all the metabolic fluxes. Moreover, this energy is also spent on the accompanying processes, e.g. for the transport of interstitial fluid.

Optimal protein production by a synthetic microbial consortium: coexistence, distribution of labor, and syntrophy.

The bacterium E. coli is widely used to produce recombinant proteins such as growth hormone and insulin. One inconvenience with E. coli cultures is the secretion of acetate through overflow metabolism. Acetate inhibits cell growth and represents a carbon diversion, which results in several negative effects on protein production. One way to overcome this problem is the use of a synthetic consortium of two different E. coli strains, one producing recombinant proteins and one reducing the acetate concentration. In this paper, we study a mathematical model of such a synthetic community in a chemostat where both strains are allowed to produce recombinant proteins. We give necessary and sufficient conditions for the existence of a coexistence equilibrium and show that it is unique. Based on this equilibrium, we define a multi-objective optimization problem for the maximization of two important bioprocess performance metrics, process yield and productivity. Solving numerically this problem, we find the best available trade-offs between the metrics. Under optimal operation of the mixed community, both strains must produce the protein of interest, and not only one (distribution instead of division of labor). Moreover, in this regime acetate secretion by one strain is necessary for the survival of the other (syntrophy). The results thus illustrate how complex multi-level dynamics shape the optimal production of recombinant proteins by synthetic microbial consortia.

Lineage motifs: developmental modules for control of cell type proportions.

In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells that produce specific sets of descendant cell types. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer progenitor states and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in zebrafish and rat retina and early mouse embryo development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.

Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems.

For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data, and an optimizer, for example, a multiobjective evolutionary algorithm, can then be utilized to find Pareto optimal solutions to the problem with surrogates as objective functions. In contrast to online data-driven MOPs, these surrogates cannot be updated with new data and, hence, the approximation accuracy cannot be improved by considering new data during the optimization process. Gaussian process regression (GPR) models are widely used as surrogates because of their ability to provide uncertainty information. However, building GPRs becomes computationally expensive when the size of the dataset is large. Using sparse GPRs reduces the computational cost of building the surrogates. However, sparse GPRs are not tailored to solve offline data-driven MOPs, where good accuracy of the surrogates is needed near Pareto optimal solutions. Treed GPR (TGPR-MO) surrogates for offline data-driven MOPs with continuous decision variables are proposed in this paper. The proposed surrogates first split the decision space into subregions using regression trees and build GPRs sequentially in regions close to Pareto optimal solutions in the decision space to accurately approximate tradeoffs between the objective functions. TGPR-MO surrogates are computationally inexpensive because GPRs are built only in a smaller region of the decision space utilizing a subset of the data. The TGPR-MO surrogates were tested on distance-based visualizable problems with various data sizes, sampling strategies, numbers of objective functions, and decision variables. Experimental results showed that the TGPR-MO surrogates are computationally cheaper and can handle datasets of large size. Furthermore, TGPR-MO surrogates produced solutions closer to Pareto optimal solutions compared to full GPRs and sparse GPRs.

L-lactate production in engineered Saccharomyces cerevisiae using a multistage multiobjective automated design framework.

The design of alternative biodegradable polymers has the potential of severely reducing the environmental impact, cost and production time currently associated with the petrochemical industry. In fact, growing demand for renewable feedstock has recently brought to the fore synthetic biology and metabolic engineering. These two interdependent research areas focus on the study of microbial conversion of organic acids, with the aim of replacing their petrochemical-derived equivalents with more sustainable and efficient processes. The particular case of Lactic acid (LA) production has been the subject of extensive research because of its role as an essential component for developing an eco-friendly biodegradable plastic-widely used in industrial biotechnological applications. Because of its resistance to acidic environments, among the many LA-producing microbes, Saccharomyces cerevisiae has been the main focus of research into related biocatalysts. In this study, we present an extensive in silico investigation of S. cerevisiae cell metabolism (modeled with Flux Balance Analysis) with the overall aim of maximizing its LA production yield. We focus on the yeast 8.3 steady-state metabolic model and analyze it under the impact of different engineering strategies including: gene knock-in, gene knock-out, gene regulation and medium optimization; as well as a comparison between results in aerobic and anaerobic conditions. We designed ad-hoc constrained multiobjective evolutionary algorithms to automate the engineering process and developed a specific postprocessing methodology to analyze the genetic manipulation results obtained. The in silico results reported in this paper empirically show that our method is able to automatically select a small number of promising genetic and metabolic manipulations, deriving competitive strains that promise to impact microorganisms design in the production of sustainable chemicals.

Emergence of division of labor in tissues through cell interactions and spatial cues.

Most cell types in multicellular organisms can perform multiple functions. However, not all functions can be optimally performed simultaneously by the same cells. Functions incompatible at the level of individual cells can be performed at the cell population level, where cells divide labor and specialize in different functions. Division of labor can arise due to instruction by tissue environment or through self-organization. Here, we develop a computational framework to investigate the contribution of these mechanisms to division of labor within a cell-type population. By optimizing collective cellular task performance under trade-offs, we find that distinguishable expression patterns can emerge from cell-cell interactions versus instructive signals. We propose a method to construct ligand-receptor networks between specialist cells and use it to infer division-of-labor mechanisms from single-cell RNA sequencing (RNA-seq) and spatial transcriptomics data of stromal, epithelial, and immune cells. Our framework can be used to characterize the complexity of cell interactions within tissues.

A novel mathematical model to generate semi-automated optimal IMRT treatment plan based on predicted 3D dose distribution and prescribed dose.

BACKGROUND: In recent years, with the development of artificial intelligence and deep learning techniques, it has become possible to predict the three-dimensional distribution dose (3D3 ) of a new patient based on the treatment plans of similar recent patients. Therefore, some new questions have arisen for the above issue: how to make use of the predicted 3D3 obtained from deep learning, to facilitate treatment planning? How to convert the predicted 3D3 to a clinical deliverable Pareto optimal plan? Little research has been done and limited software has been developed in this regard. PURPOSE: In the current research, an attempt was made to contribute the knowledge-based planning by presenting a new mathematical model, and to take a novel step towards optimizing the treatment plan derived from both predicted 3D3 as well as dose prescription to generate a semi-automated clinically applicable optimal IMRT treatment plan. METHODS: The presented model has benefited from both prescribed dose as well as predicted dose and its objective function includes both quadratic and linear phrases, so it was called the QuadLin model. The model has been run on the data of 30 patients with head and neck cancer randomly selected from the Open-KBP dataset. There are 19 sets of dose prediction data for each patient in this database. Therefore, a total of 570 problems have been solved in the CVX framework with commercial solver Mosek and the results have been evaluated by two plan quality approaches (1) DVH points differences, and (2) satisfied clinical criteria. RESULTS: The results of the current study indicate a strong significant improvement in almost all plan evaluation indicators compared to the reference plan of the dataset, 3D3 predictions, as well as the results of previous research, based on the Wilcoxon signed ranks test with a significance level of 0.01. Accordingly, for all regions of interest (ROIs) (or structures) of all 570 problems total clinical indicators have improved by more than 21%, 15%, and at least13%, on average, compared to the predicted dose, the reference plan, and previous research, respectively, with 341 s as the average of solving time. CONCLUSIONS: Evaluation of the research results indicates the significant effect of the QuadLin model on improving the dose delivery to the target volumes while reducing the dose and preserving organs at risk. Based on the literature, the proposed model has generated the best-known treatment plan from the predicted 3D3 so far.

Role of climate fund raising under fiscal balance on climate change mitigation: an analysis from Pareto optimality.

Since there is little progress being made in multinational climate discussions, climate finance is at a crossroads as lenders must come up with new plans for the "Future of Environment Funds." The mission of effectively and efficiently distributing money to support the shift to low-carbon, climate-resilient economies has been given to climate finance organizations. Due to its purpose to contribute to a paradigm shift, the Green Climate Fund (GCF) is anticipated to help the most vulnerable populations adapt to and mitigate climate change. This research alters the premise of the Baumol and Oates public externality model to make it more appropriate for global climate governance analysis. This research then deduces the special pricing conditions to persuade the market to comply with Pareto optimality criteria by contrasting the Pareto optimality model of global climate governance and the market equilibrium model. The rules and potential approaches that must be followed for raising capital and allocating GCFs are then determined by taking into account global Pareto optimality and fiscal balance. The study finds that when each country assumes that the GCF aims to achieve Pareto optimality in climate governance globally and its own fiscal balance, the equilibrium results of the international climate game will not achieve both the financial balance of the GCF and global Pareto optimality simultaneously. The GCF may successfully finance non-bankable components of bigger "almost bankable projects," according to our empirical analysis of the GCF portfolio structure and strategy in this research. This lends credence to an alternative interpretation of the GCF.

Statistical Designs to Improve Downstream Processing.

The efficient extraction and purification of recombinant proteins from leaf and seed tissues is often a challenging task, involving multiple steps that must be optimized by identifying and accommodating complex parameter interactions. Conventional one-factor-at-a-time approaches fail to reveal these complex interactions and often result in sub-optimal processes with unnecessary costs. Here, we describe generic considerations to identify global optima for the extraction and purification of recombinant proteins from complex plant matrices. The corresponding experiments can help to streamline downstream processing by reducing the time, costs, and number of unit operations. The procedure involves the knowledge-based selection of factors for screening, the systematic design and analysis of experiments, and the iterative refinement of suitable conditions. The resulting descriptive models can be used to guide process scale-up and offer scientific justifications for process development decisions in negotiations with regulatory authorities.

The L-shaped selection algorithm for multitrait genomic selection.

Selecting for multiple traits as opposed to a single trait has become increasingly important in genomic selection. As one of the most popular approaches to multitrait genomic selection, index selection uses a weighted average of all traits as a single breeding objective. Although intuitive and effective, index selection is not only numerically sensitive but also structurally incapable of finding certain optimal breeding parents. This paper proposes a new selection method for multitrait genomic selection, the L-shaped selection, which addresses the limitations of index selection by normalizing the trait values and using an L-shaped objective function to find optimal breeding parents. This algorithm has been proven to be able to find any Pareto optimal solution with appropriate weights. Two performance metrics have also been defined to quantify multitrait genomic selection algorithms with respect to their ability to accelerate genetic gain and preserve genetic diversity. Computational experiments were conducted to demonstrate the improved performance of L-shaped selection over-index selection.

A specialist-generalist framework for epithelial-mesenchymal plasticity in cancer.

Epithelial-mesenchymal plasticity (EMP) reflects the capacity of cells to interconvert between epithelial and mesenchymal phenotypes. In cancer, these dynamics ultimately contribute to disease progression. Despite decades of study, a consistent molecular definition of this plasticity remains elusive because of its inherent variability. The advent of quantitative single-cell biology is unveiling unexpected complexity, and new conceptual frameworks are required to understand the emergence and relevance of EMP in cancer. Here, we use principles from multitask optimization to propose that EMP reflects an adaptive response of epithelial cells in response to homeostatic disruption, giving rise to generalist phenotypes. We use this theory to predict properties of these cells and their contribution to tumor progression.

An Efficient Pareto Optimal Resource Allocation Scheme in Cognitive Radio-Based Internet of Things Networks.

The high data rates detail that internet-connected devices have been increasing exponentially. Cognitive radio (CR) is an auspicious technology used to address the resource shortage issue in wireless IoT networks. Resource optimization is considered a non-convex and nondeterministic polynomial (NP) complete problem within CR-based Internet of Things (IoT) networks (CR-IoT). Moreover, the combined optimization of conflicting objectives is a challenging issue in CR-IoT networks. In this paper, energy efficiency (EE) and spectral efficiency (SE) are considered as conflicting optimization objectives. This research work proposed a hybrid tabu search-based stimulated algorithm (HTSA) in order to achieve Pareto optimality between EE and SE. In addition, the fuzzy-based decision is employed to achieve better Pareto optimality. The performance of the proposed HTSA approach is analyzed using different resource allocation parameters and validated through simulation results.

A decision analysis model for reducing carbon emission from coal-fired power plants and its compensatory units.

The increasing carbon dioxide level in the earth's atmosphere and continuously changing climate creates a significant challenge to sustainability in the world. It is not easy to control pollution due to carbon dioxide emissions from coal-fired power plants into the atmosphere. However, carbon capture technology provides an advantage for capturing carbon from power plants. Various researchers suggested the non-linear optimization model with post-combustion carbon capture technology in coal-fired power plants to reduce carbon emission. However, in their research articles, most researchers did not include loss of power due to retrofitting carbon capture technology in power plants and carbon emission from the compensatory power plant. This paper proposes a linear optimization model that minimizes the emission release from the power plant and its compensatory plant by appropriate selection of carbon capture technology. Our proposed model incorporates loss of power due to adopting carbon capture technology and emission release from the power plant and compensatory power plant in the problem formulation. We have also generated the Pareto curve that determines the trade-off solutions between emission release and the overall electricity cost. The applicability of our model is illustrated through power sector data from two Indian states. The net reduction of emissions in the two states are 27.17 % and 26.29 %, achieved by a mixed integer linear programming approach in coal-fired power plants. The model developed is generic and provides a sustainable environment for the generation of electricity.

The Widened Pipe Model of plant hydraulic evolution.

Shaping global water and carbon cycles, plants lift water from roots to leaves through xylem conduits. The importance of xylem water conduction makes it crucial to understand how natural selection deploys conduit diameters within and across plants. Wider conduits transport more water but are likely more vulnerable to conduction-blocking gas embolisms and cost more for a plant to build, a tension necessarily shaping xylem conduit diameters along plant stems. We build on this expectation to present the Widened Pipe Model (WPM) of plant hydraulic evolution, testing it against a global dataset. The WPM predicts that xylem conduits should be narrowest at the stem tips, widening quickly before plateauing toward the stem base. This universal profile emerges from Pareto modeling of a trade-off between just two competing vectors of natural selection: one favoring rapid widening of conduits tip to base, minimizing hydraulic resistance, and another favoring slow widening of conduits, minimizing carbon cost and embolism risk. Our data spanning terrestrial plant orders, life forms, habitats, and sizes conform closely to WPM predictions. The WPM highlights carbon economy as a powerful vector of natural selection shaping plant function. It further implies that factors that cause resistance in plant conductive systems, such as conduit pit membrane resistance, should scale in exact harmony with tip-to-base conduit widening. Furthermore, the WPM implies that alterations in the environments of individual plants should lead to changes in plant height, for example, shedding terminal branches and resprouting at lower height under drier climates, thus achieving narrower and potentially more embolism-resistant conduits.

Trade-off drives Pareto optimality of within- and among-year emergence timing in response to increasing aridity.

Adaptation to current and future climates can be constrained by trade-offs between fitness-related traits. Early seedling emergence often enhances plant fitness in seasonal environments, but if earlier emergence in response to seasonal cues is genetically correlated with lower potential to spread emergence among years (i.e., bet-hedging), then this functional trade-off could constrain adaptive evolution. Consequently, selection favoring both earlier within-year emergence and greater spread of emergence among years-as is expected in more arid environments-may constrain adaptive responses to trait value combinations at which a performance gain in either function (i.e., evolving earlier within- or greater among-year emergence) generates a performance loss in the other. All such trait value combinations that cannot be improved for both functions simultaneously are described as Pareto optimal and together constitute the Pareto front. To investigate how this potential emergence timing trade-off might constrain adaptation to increasing aridity, we sourced seeds of two grasses, Stipa pulchra and Bromus diandrus, from multiple maternal lines within populations across an aridity gradient in California and examined their performance in a greenhouse experiment. We monitored emergence and assayed ungerminated seeds for viability to determine seed persistence, a metric of potential among-year emergence spread. In both species, maternal lines with larger fractions of persistent seeds emerged later, indicating a trade-off between within-year emergence speed and potential among-year emergence spread. In both species, populations on the Pareto front for both earlier emergence and larger seed persistence fraction occupied significantly more arid sites than populations off the Pareto front, consistent with the hypothesis that more arid sites impose the strongest selection for earlier within-year emergence and greater among-year emergence spread. Our results provide an example of how evaluating genetically based correlations within populations and applying Pareto optimality among populations can be used to detect evolutionary constraints and adaptation across environmental gradients.

A new approach based on inversion of a partial least squares model searching for a preset analytical target profile. Application to the determination of five bisphenols by liquid chromatography with diode array detector.

The paper shows a procedure for selecting the control method parameters (factors) to obtain a preset 'analytical target profile' when a liquid chromatographic technique is going to be carried out for the simultaneous determination of five bisphenols (bisphenol-A, bisphenol-S, bisphenol-F, bisphenol-Z and bisphenol-AF), some of them regulated by the European Union. The procedure has three steps. The first consists of building a D-optimal combined design (mixture-process design) for the control method parameters, which are the composition of the ternary mobile phase and its flow rate. The second step is to fit a PLS2 model to predict six analytical responses (namely, the resolution between each pair of consecutive peaks, and the initial and final chromatographic time) as a function of the control method parameters. The third final step is the inversion of the PLS2 model to obtain the conditions needed for attaining a preset analytical target profile. The computational inversion of the PLS2 prediction model looking for the Pareto front of these six responses provides a set of experimental conditions to conduct the chromatographic determination, specifically 22% of water, mixed with 58% methanol and 20% of acetonitrile, keeping the flow rate at 0.66 mL min-1. These conditions give a chromatogram with retention times of 2.180, 2.452, 2.764, 3.249 and 3.775 min for BPS, BPF, BPA, BPAF and BPZ, respectively, and excellent resolution among all the chromatographic peaks. Finally, the analytical method is validated under the selected experimental conditions, in terms of trueness and precision. In addition, the detection capability for the five bisphenols were: 596, 334, 424, 458 and 1156 µg L-1, with probabilities of false positive and of false negative equal to 0.05.

Determination of the Geometric Parameters of Electrode Systems for Electrical Impedance Myography: A Preliminary Study.

The electrical impedance myography method is widely used in solving bionic control problems and consists of assessing the change in the electrical impedance magnitude during muscle contraction in real time. However, the choice of electrode systems sizes is not always properly considered when using the electrical impedance myography method in the existing approaches, which is important in terms of electrical impedance signal expressiveness and reproducibility. The article is devoted to the determination of acceptable sizes for the electrode systems for electrical impedance myography using the Pareto optimality assessment method and the electrical impedance signals formation model of the forearm area, taking into account the change in the electrophysical and geometric parameters of the skin and fat layer and muscle groups when performing actions with a hand. Numerical finite element simulation using anthropometric models of the forearm obtained by volunteers' MRI 3D reconstructions was performed to determine a sufficient degree of the forearm anatomical features detailing in terms of the measured electrical impedance. For the mathematical description of electrical impedance relationships, a forearm two-layer model, represented by the skin-fat layer and muscles, was reasonably chosen, which adequately describes the change in electrical impedance when performing hand actions. Using this model, for the first time, an approach that can be used to determine the acceptable sizes of electrode systems for different parts of the body individually was proposed.

Criticality in Pareto Optimal Grammars?

What are relevant levels of description when investigating human language? How are these levels connected to each other? Does one description yield smoothly into the next one such that different models lie naturally along a hierarchy containing each other? Or, instead, are there sharp transitions between one description and the next, such that to gain a little bit accuracy it is necessary to change our framework radically? Do different levels describe the same linguistic aspects with increasing (or decreasing) accuracy? Historically, answers to these questions were guided by intuition and resulted in subfields of study, from phonetics to syntax and semantics. Need for research at each level is acknowledged, but seldom are these different aspects brought together (with notable exceptions). Here, we propose a methodology to inspect empirical corpora systematically, and to extract from them, blindly, relevant phenomenological scales and interactions between them. Our methodology is rigorously grounded in information theory, multi-objective optimization, and statistical physics. Salient levels of linguistic description are readily interpretable in terms of energies, entropies, phase transitions, or criticality. Our results suggest a critical point in the description of human language, indicating that several complementary models are simultaneously necessary (and unavoidable) to describe it.

Using optimal control to understand complex metabolic pathways.

BACKGROUND: Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles have been justified from an evolutionary perspective. In the context of the cell, previous studies have shown how dynamics of gene expression in small metabolic models can be explained assuming that cells have developed optimal adaptation strategies. Most of these works have considered rather simplified representations, such as small linear pathways, or reduced networks with a single branching point, and a single objective for the optimality criteria. RESULTS: Here we consider the extension of this approach to more realistic scenarios, i.e. biochemical pathways of arbitrary size and structure. We first show that exploiting optimality principles for these networks poses great challenges due to the complexity of the associated optimal control problems. Second, in order to surmount such challenges, we present a computational framework which has been designed with scalability and efficiency in mind, including mechanisms to avoid the most common pitfalls. Third, we illustrate its performance with several case studies considering the central carbon metabolism of S. cerevisiae and B. subtilis. In particular, we consider metabolic dynamics during nutrient shift experiments. CONCLUSIONS: We show how multi-objective optimal control can be used to predict temporal profiles of enzyme activation and metabolite concentrations in complex metabolic pathways. Further, we also show how to consider general cost/benefit trade-offs. In this study we have considered metabolic pathways, but this computational framework can also be applied to analyze the dynamics of other complex pathways, such as signal transduction or gene regulatory networks.