OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

Computational tools have been widely adopted for strain optimisation in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximisation of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout) leading to high biochemical production. The usefulness 1and capabilities of OptDesign are demonstrated for the production of three biochemicals in E. coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. Source code is available at https://github.com/chang88ye/OptDesign.

Applications of big data and communications to sustainable development of smart cities

The formation of a smart city is a dynamic process that involves the implementation of systemic steps that transform the city into a comfortable environment for living. Smart cities are evolving on the basis of a flexible telecommunications architecture for IoT devices. Existing sustainability technologies require a large amount of computing power to process IoT data. For effective detection and localization of dysfunctions of complex socio-technical systems of smart cities, it is proposed to use an approach based on a parametric representation of objects of interest. In order to eliminate the influence of the variability of the Internet of Things on the classification accuracy, it is proposed to use a combination of optimality principles, taking into account the parameters of energy consumption, processor and memory usage.

Strong Time-Consistent Solution for Cooperative Differential Games with Network Structure

One class of cooperative differential games on networks is considered. It is assumed that interaction on the network is possible not only between neighboring players, but also between players connected by paths. Various cooperative optimality principles and their properties for such games are investigated. The construction of the characteristic function is proposed, taking into account the network structure of the game and the ability of players to cut off connections. The conditions under which a strong time-consistent subcore is not empty are studied. The formula for explicit calculation of the Shapley value is derived. The results are illustrated by the example of one differential marketing game.

Thermodynamic optimality principles in Earth sciences

<p>Ecohydrological systems are a result of long-term co-evolution of soils, biota and atmospheric conditions, and often respond to perturbations in non-intuitive ways. Their short-term responses can be explained and sometimes predicted if we understand the underlying dynamic processes and if we can observe the initial state precisely enough. However, how do they co-evolve in the long-term after a change in the boundary conditions? In 1922, Alfred Lotka hypothesised that the natural selection governing the evolution of biota and composition of ecosystems may be obeying some thermodynamic principles related to maximising energy flow through these systems. Similar thoughts have been formulated for various components of the Earth system and individual processes, such as heat transport in the atmosphere and oceans, erosion and sediment transport in river systems and estuaries, the formation of vegetation patterns, and many others. Different thermodynamic optimality principles have been applied to predict or explain a given system property or behaviour, of which the maximum entropy production and the maximum power principles are most widespread. However, the different studies did not use a common systematic approach for the formulation of the relevant system boundaries, state variables and exchange fluxes, resulting in considerable ambiguity about the application of thermodynamic optimality principles in the scientific community. Such a systematic framework has been developed recently and can be tested online at:</p><p><span><span>https://renkulab.io/projects/stanislaus.schymanski/thermodynamic_optimality_blueprint</span></span></p><p>In the present study, we illustrate how such a common framework can be used to classify and compare different applications of thermodynamic optimality principles in the literature, and discuss the insights gained and key criteria for a more rigorous testing of such principles.</p>

Leaf economics explained by optimality principles

SUMMARYThe worldwide leaf economics spectrum relates leaf lifespan (LL) to leaf dry mass per unit area (LMA)1. By combining three well-supported principles2–4, we show that an isometric relationship between these two quantities maximizes the leaf’s net carbon gain. This theory predicts a spectrum of equally competent LMA-LL combinations in any given environment, and how their optimal ratio varies across environments. By analysing two large, independent leaf-trait datasets for woody species1,5, we provide quantitative empirical support for the predicted dependencies of LL on LMA and environment in evergreen plants, and for the distinct predicted dependencies of LMA on light, temperature, growing-season length and aridity in evergreen and deciduous plants. We thereby resolve the long-standing question of why deciduous LMA tends to increase (with increasing LL) towards the equator, while evergreen LMA and LL decrease6. We also show how the statistical distribution of LMA within communities can be modelled as an outcome of environmental selection on the global pool of species with diverse values of LMA and LL.

Cooperative optimality principals in differential games on networks

The paper describes a class of differential games on networks. The construction of cooperative optimality principles using a special type of characteristic function that takes into account the network structure of the game is investigated. The core, the Shapley value and the tau-value are used as cooperative optimality principles. The results are demonstrated on a model of a differential research investment game, where the Shapley value and the tau-value are explicitly constructed.

Developmental and evolutionary constraints on olfactory circuit selection

Across species, neural circuits show remarkable regularity, suggesting that their structure has been driven by underlying optimality principles. Here, we ask whether we can predict the neural circuitry of diverse species by optimizing the neural architecture to make learning as efficient as possible. We focus on the olfactory system, primarily because it has a relatively simple evolutionarily conserved structure, and because its input and intermediate layer sizes exhibits a tight allometric scaling. In mammals, it has been shown that the number of neurons in layer 2 of piriform cortex scales as the number of glomeruli (the input units) to the 3/2 power; in invertebrates, we show that the number of mushroom body Kenyon cells scales as the number of glomeruli to the 7/2 power. To understand these scaling laws, we model the olfactory system as a three layered nonlinear neural network, and analytically optimize the intermediate layer size for efficient learning from a limited number of samples. We find that the 3/2 scaling observed in mammals emerges naturally, both in full batch optimization and under stochastic gradient learning. We extended the framework to the case where a fraction of the olfactory circuit is genetically specified, not learned. We show numerically that this makes the scaling law steeper when the number of glomeruli is small, and we are able to recover the 7/2 scaling law observed in invertebrates. This study paves the way for a deeper understanding of the organization of brain circuits from an evolutionary perspective.

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.