Multi-objective optimization of glycopeptide antibiotic production in batch and fed batch processes

Fermentation optimization involves potentially conflicting multiple objectives such as product concentration and production media cost. Simultaneous optimization of these objectives would result in a multiobjective optimization problem, which is characterized by a set of multiple solutions, knows as pareto optimal solutions. These solutions gives flexibility in evaluating the trade-offs and selecting the most suitable operating policy. Here, ε-constraint approach was used to generate the pareto solutions for two objectives: product concentration and product per unit cost of media, for batch and fed batch operations using process model for Amycolatopsis balhimycina, a glycopeptide antibiotic producer. This resulted in a set of several pareto optimal solutions with the two objectives ranging from (0.75 g l⁻¹, 3.97 g $^-1) to (0.44 g l⁻¹, 5.19 g $^-1) for batch and from (1.5 g l⁻¹, 5.46 g $^-1) to (1.1 g l⁻¹, 6.34 g $^-1) for fed batch operations. One pareto solution each for batch and for fed batch mode was experimentally validated.     

Allometric scaling law in a simple oxygen exchanging network: possible implications on the biological allometric scaling laws

A simple model of an oxygen exchanging network is presented and studied. This network's task is to transfer a given oxygen rate from a source to an oxygen consuming system. It consists of a pipeline, that interconnects the oxygen consuming system and the reservoir and of a fluid, the active oxygen transporting element, moving through the pipeline. The network optimal design (total pipeline surface) and dynamics (volumetric flow of the oxygen transporting fluid), which minimize the energy rate expended in moving the fluid, are calculated in terms of the oxygen exchange rate, the pipeline length, and the pipeline cross-section. After the oxygen exchanging network is optimized, the energy converting system is shown to satisfy a 3/4-like allometric scaling law, based upon the assumption that its performance regime is scale invariant as well as on some feasible geometric scaling assumptions. Finally, the possible implications of this result on the allometric scaling properties observed elsewhere in living beings are discussed.     

基于不同网络构建方法的生境网络优化研究——以苏锡常地区白鹭为例

生境网络在支持物种长期存活中具有重要意义,由于受人类社会经济频繁活动的影响,迫切需要优化。选取城市化快速发展的苏锡常地区为研究区域,以湿地代表性鸟类白鹭为目标物种,利用2000、2010年土地利用/覆被数据,采用两种不同的网络构建方法,识别出恢复或新建生境节点或斑块,以及基于网络连接度并兼顾了集合覆盖问题遴选出重要新增生境节点,得到生境网络优化方案。结果表明:(1)恢复型生境节点对应的生境斑块面积在10—50 hm~2之间,以乔木林地为主;新建型生境节点对应的生境斑块是乔木林地和湖泊水库与河流兼而有之,均满足白鹭生境地类要求;优化的生境网络连接度较高、整体结构状况最好,兼顾了资源有限性下经济效益和生态效益最大化,与历史观测点实际情况较为吻合,可作为优化方案。(2)相同的生境斑块格局受生境格局时空变化、生态过程模拟方法及网络优化评判标准的影响,在采用不同网络构建方法时,会得到不同的网络结构及网络优化结果。采用时空格局变化与不同生态过程模拟相结合的网络优化方法,有助于分析格局-生态过程及网络结构变化情况,其思路为网络优化方法进一步深入研究提供了借鉴。     

Process performance models in the optimization of multiproduct protein production plants

In this work we propose a model that simultaneously optimizes the process variables and the structure of a multiproduct batch plant for the production of recombinant proteins. The complete model includes process performance models for the unit stages and a posynomial representation for the multiproduct batch plant. Although the constant time and size factor models are the most commonly used to model multiproduct batch processes, process performance models describe these time and size factors as functions of the process variables selected for optimization. These process performance models are expressed as algebraic equations obtained from the analytical integration of simplified mass balances and kinetic expressions that describe each unit operation. They are kept as simple as possible while retaining the influence of the process variables selected to optimize the plant. The resulting mixed-integer nonlinear program simultaneously calculates the plant structure (parallel units in or out of phase, and allocation of intermediate storage tanks), the batch plant decision variables (equipment sizes, batch sizes, and operating times of semicontinuous items), and the process decision variables (e.g., final concentration at selected stages, volumetric ratio of phases in the liquid-liquid extraction). A noteworthy feature of the proposed approach is that the mathematical model for the plant is the same as that used in the constant factor model. The process performance models are handled as extra constraints. A plant consisting of eight stages operating in the single product campaign mode (one fermentation, two microfiltrations, two ultrafiltrations, one homogenization, one liquid-liquid extraction, and one chromatography) for producing four different recombinant proteins by the genetically engineered yeast Saccharomyces cerevisiae was modeled and optimized. Using this example, it is shown that the presence of additional degrees of freedom introduced by the process performance models, with respect to a fixed size and time factor model, represents an important development in improving plant design.     

建设用地减量化的苏锡常地区土地利用格局优化——基于白鹭生境网络优化视角

建设用地减量化管理国家战略的本质就是土地利用格局优化,具有重要理论价值和实践意义。以建设用地减量化为背景、快速城市化苏锡常地区为研究区,采用目标物种白鹭生境网络优化为减量化依据的方法,旨在回答"建设用地怎么减?减哪里?减多少?依据是什么?"的土地利用格局优化问题。结果表明:1)白鹭核心生境斑块距离人为干扰源较远,需减量化的建设用地数量较少,但多数核心生境斑块面积小于平均值,破碎化现象较严重;白鹭迁移廊道受人为干扰严重,需减量化的建设用地数量为6542.1 hm~2,占减量化总面积的99%以上;生态核心区经过耕地占补平衡后,城乡建设用地二次减量213.7 hm~2、新增水田208.7 hm~2;研究区建设用地减量化总面积6755.8 hm~2。2)通过增加核心生境斑块面积、提高迁移廊道通达安全性,白鹭生境网络优化了。同时完成了建设用地总量减量化,实现了区域土地利用格局优化的目的。     

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN)

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg^?1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R² (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.     

跨区域视角下苏锡常地区白鹭生境网络优化

打破行政界线,跨区域协同治理生态问题,是生态文明建设的必然要求。选取快速城镇化地区苏锡常为研究区域,地区优势物种白鹭为研究对象,在等量保护成本限定下,基于跨区域和分区视角,将保护成本用于生境斑块优化,通过设定不同的优化情景,确定新增生境斑块的优化顺序,计算出各情景下生态保护成本与生态收益提升值,对比不同优化方案下网络结构指数变化情况,进而探讨跨区域生境网络优化是否能够更好地实现物种保护的"最大覆盖问题"。结果表明:(1)新增生境斑块的总量和空间布局都呈现为不均匀分布,以苏州最多,无锡次之,常州最少;具有"网络连通性较高但保护成本较低"的斑块分布特征与之相同;(2)根据单位效益成本值大小对生境斑块优化排序,将保护成本优先用于"网络连通性指数较高而保护成本较低"的生境斑块。在等量保护成本限定下,跨区域生境网络优化比分区优化的网络连通性指数α、β、γ分别提升了13.17%、12.97%、13.76%,实现保护成本的生态效益提升最大化,更好地解决了物种保护的"最大覆盖问题"。     

基于网络效能分析的生境网络构建与优化——以苏锡常地区白鹭为例

从网络效能分析出发研究生境网络优化具有重要意义。以快速城市化苏锡常地区为小尺度研究区、苏锡常及周边15个城市为大尺度泛研究区、白鹭为焦点物种,通过采用引入并构建网络效能概念框架、识别和构建不同尺度生境网络、调整网络结构和转换网络结构特性相结合的方法,实现了跨尺度下白鹭生境网络优化。结果表明:1)依据大尺度迁移网络完善小尺度生境网络得到的方案一和改变方案一网络特性得到的方案二均是现状生境网络的优化方案;2)方案二与方案一相比,在平均路径长度值增加、聚类系数值减少、节点度概率分布幂次关系拟合度提高,即小世界特性减弱、无标度特性增强的基础上,测度网络连接度的α、β、γ指数值得到增加,同时新增生境节点和迁移廊道数量更少,具有更好的优化成效;3)从网络效能分析出发,有益于提高生境网络跨尺度下优化方案的可行性,其分析思路既丰富了网络优化的方法论研究,也为提高上位与下位规划空间契合度的实践提供了理论支持。但从网络效能概念框架看,如网络层级分析、其他尺度情景研究、网络质量测度指标等方面还亟待丰富与完善。     

Lovastatin and (+)‐geodin formation by Aspergillus terreus ATCC 20542 in a batch culture with the simultaneous use of lactose and glycerol as carbon sources

The influence of initial glycerol and lactose concentrations on lovastatin and (+)‐geodin formation in batch cultures of Aspergillus terreus ATCC 20542 was presented. At first the experiments comprised lovastatin biosynthesis on glycerol as the sole carbon source. Lovastatin titers below 40 mg/L were found under these conditions and they were lower than previously obtained results when lactose was used as the sole carbon source. However, the application of the mixture of glycerol and lactose allowed in achieving higher lovastatin concentration in the broth. It even exceeded 122 mg/L when 10 g lactose and 15 g glycerol per liter were used. The calculated lovastatin volumetric and specific formation rates on glycerol or lactose and on the mixture of these two showed that lovastatin was faster produced on lactose than on glycerol. In the trophophase, the maximum volumetric lovastatin formation rate on lactose was up to four times higher than on glycerol and so was the lovastatin specific formation rate. Similar relations for the accompanying (+)‐geodin biosynthesis were also studied. When the mixture of lactose and glycerol was used, the transformation of (+)‐geodin to other polyketide metabolites also took place.     

Assessing the social dimension in strategic network optimization for a sustainable development: The case of bioethanol production in the EU

The complexity of social indicators and their subjective and often qualitative nature render their inclusion into quantitative optimization models for network design and strategic decision-making challenging. The social dimension is thus often implemented only rudimentarily, thwarting a holistic sustainability assessment and neglecting many of the social issues addressed in the sustainable development goals (SDGs). This work presents a structured process for including a comprehensive set of social aspects by selecting applicable quantitative and regionalized social indicators. This approach is applied to the case of second-generation bioethanol production in the EU. Based on inter alia the Guidelines for Social Life Cycle Assessment of Products and Organizations, the Social Hotspots Database, state-of-the-art literature, as well as previous work, we compile 9 social objective functions and 25 functions for social hotspot identification. They are evaluated alongside 1 economic and 21 environmental LCA-based objective functions in a mixed-integer linear programming (MILP) model. Key results show that social optimization either leads to large, labor-intensive or regionally focused, indicator-driven networks. Injuries and fatalities in the feedstock sectors of Central and Eastern European countries is the primary social hotspot. On the level of the overarching SDGs, SDG13 is most congruent with other goals, while SDG7 is hindered by pursuing other goals. This study's approach is novel in strategic network design and the European bioeconomy, and, by operationalizing the social dimension, enables a more holistic life cycle sustainability assessment and the consideration of the SDGs. This article met the requirements for a gold-gold JIE data openness badge described at http://jie.click/badges .      

Towards a sounder interpretation of entropy-based indicators in ecological network analysis

Various indicators rooted in the concepts of information and entropy have been proposed to be used for ecological network analysis. They are theoretically well grounded and widely used in the literature, but have always been difficult to interpret due to an apparent lack of strict relations with node and link weight. We generated several sets of 10,000 networks in order to explore such relations and work towards a sounder interpretation. The indices we explored are based on network composition (i.e., type and importance of network compartments), or network flows (i.e., type and importance of flows among compartments), including Structural Information (SI), Total System Throughput (TST), Average Mutual Information (AMI), Flow Diversity (H), and Ascendency (ASC). A correlation analysis revealed a lack of strict relationships among the responses of the investigated indicators within the simulated space of variability of the networks. However, fairly coherent patterns of response were revealed when networks were sorted by following a “bottom-up” criterion, i.e. by increasing the dominance of the large-sized top predator in the network. This ranking is reminiscent of ecosystem succession, along which the prominence of higher trophic level organisms progressively increases. In particular, the results show that a simple increase in organisms having large size and low consumption rates is potentially able to simultaneously lead to an increase of different types of information (as SI, H and AMI), thus also emphasizing the importance of bionomic traits related to body size in affecting information-related properties in a trophically connected community. The observed trends suffer from a certain dispersion of data, which was diminished by imposing specific and ecologically meaningful constraints, such as mass balancing and restriction to certain range of the ratio A/C, an index related to the viability of ecological networks. These results suggest that the identification of a set of effective constraints may help to identify improved conditions for applicability of the investigated flow-based indicators, and also provide indication on how to normalise them with respect to meaningful network properties or reference states. Thus, in order to increase confidence in the derived network metrics describing a particular ecosystem state, and thus increase their applicability, it is advisable to construct replicate networks by taking the variability of input data into account, and by applying uncertainty and sensitivity analyses.     

Medium optimization for the production of the secondary metabolite squalestatin S1 by a Phoma sp. combining orthogonal design and response surface methodology

In the present work, a combined statistical approach of orthogonal design (L₂₇(3¹³)), response surface techniques and polynomial regression were applied to optimize the composition and concentration of a liquid fermentation medium for the production of squalestatin S1 by a fungus (a Phoma species). Optimal conditions for maximal titres and productivity were determined based on 13 parameters at three different levels. Initially, a screening design methodology was used to evaluate the process variables, which were relevant to S1 titre and the response surfaces applied to find optimal regions for production. The sources of carbon and concentration, and their interactions with oily precursors were statistically significant factors. The combined orthogonal design and response surface methodology predicted optimal conditions for of 273 mg l⁻¹ of squalestatin S1. Confirmatory experiments of the optimal medium composition produced titres of 434 mg l⁻¹ in a 5-day fermentation at 25 °C. This represented a 60% improvement in the maximum titre predicted, and a two-fold higher productivity when compared with reported S1 yields of various fungal species. This combined statistical approach enables rapid identification and integration of key medium parameters for optimising secondary metabolite production and could be very useful in pharmaceutical screening programmes.     

长江下游平原区生态网络识别与优化——以常州市金坛区为例

生态网络识别与优化是保障区域生态安全格局,开展国土空间生态保护修复的重要研究手段。立足图论和景观生态学理论方法,以长江下游平原区的金坛区为例,利用遥感影像、土地利用数据、POI数据等,通过最小面积阈值、最佳距离阈值分析,识别了区域现状生态网络,开展了生态盲区指导下的生态网络优化,并对优化前后的生态网络性能、区域生境斑块重要性进行评价,取得以下研究结果：①开展生态盲区指导下生态网络优化,可显著提升区域景观连接度,且在生态水网区域具有较强适用性;②研究区存在生态景观断裂,生态网络布局亟待优化,经优化Harary指数增加了10.92倍,整体连接度指数提升了43.45%,可能性连接度指数提升了99.58%;③国土空间生态保护修复应重点关注关键区域整治,可采取重点斑块生态保护、关键节点生态修复、特殊区域生态建设等差异化生态网络修复策略。     

The evolutionary dynamics of evolvability in a gene network model

Evolvability, the ability of populations to adapt, has recently emerged as a major unifying concept in biology. Although the study of evolvability offers new insights into many important biological questions, the conceptual bases of evolvability, and the mechanisms of its evolution, remain controversial. We used simulated evolution of a model of gene network dynamics to test the contentious hypothesis that natural selection can favour high evolvability, in particular in sexual populations. Our results conclusively demonstrate that fluctuating natural selection can increase the capacity of model gene networks to adapt to new environments. Detailed studies of the evolutionary dynamics of these networks establish a broad range of validity for this result and quantify the evolutionary forces responsible for changes in evolvability. Analysis of the genotype–phenotype map of these networks also reveals mechanisms connecting evolvability, genetic architecture and robustness. Our results suggest that the evolution of evolvability can have a pervasive influence on many aspects of organisms.     

Plant species with the trait of continuous flowering do not hold core roles in a Neotropical lowland plant‐pollinating insect network

Plant–animal interaction science repeatedly finds that plant species differ by orders of magnitude in the number of interactions they support. The identification of plant species that play key structural roles in plant–animal networks is a global conservation priority; however, in hyperdiverse systems such as tropical forests, empirical datasets are scarce. Plant species with longer reproductive seasons are posited to support more interactions compared to plant species with shorter reproductive seasons but this hypothesis has not been evaluated for plant species with the longest reproductive season possible at the individual plant level, the continuous reproductive phenology. Resource predictability is also associated with promoting specialization, and therefore, continuous reproduction may instead favor specialist interactions. Here, we use quantitative pollinating insect–plant networks constructed from countryside habitat of the Tropical Wet forest Life Zone and modularity analysis to test whether plant species that share the trait of continuous flowering hold core roles in mutualistic networks. With a few exceptions, most plant species sampled within our network were assigned to the role of peripheral. All but one network had significantly high modularity scores and each continuous flowering plant species was in a different module. Our work reveals that the continuous flowering plant species differed in some networks in their topological role, and that more evidence was found for the phenology to support specialized subsets of interactions. Our findings suggest that the conservation of Neotropical pollinating insect communities may require planting species from each module rather than identifying and conserving network hubs.     

基于成本收益分析的生境网络优化——以苏锡常地区白鹭为例

引入经济学成本收益分析方法对于生境网络优化保护具有重要现实意义。以苏锡常地区白鹭生境网络优化为例,通过分别构建研究区生境网络保护成本与提升收益测算体系、模拟多种情景和优化方案、成本收益分析的方法,实现了满足不同约束条件的优化方案。结果表明:(1)最大收益累积情景下的方案在总收益最高的基础上,实现了保护总成本较低的目的;最佳效益累积情景下的方案在研究区土地资源有限和保护总成本最低的基础上,实现了优化成效最佳的目的;(2)最佳效益累积情景下的方案在实现成本最低的基础上,兼顾到了优化成效的最大化,适宜于土地资源稀缺且城市化快速发展地区;(3)有限效益累积情景作为最佳效益累积情景的基本模式,其优化方案在一定范围内可以兼顾到成本较低和成效较高。将经济学与生态学相结合的网络优化方法,综合考虑了研究区的生态、经济和社会等现实因素,极大的提高了网络优化方案操作的可行性,其思路和方法拓展了网络优化研究视角。但诸多实现因素也决定了成本与收益体系的构建还处于不断完善之中。     

Optimization of culture parameters for extracellular protease production from a newly isolated Pseudomonas sp. using response surface and artificial neural network models

Radial basis function (RBF) artificial neural network (ANN) and response surface methodology (RSM) were used to build a predictive model of the combined effects of independent variables (pH, temperature, inoculum volume) for extracellular protease production from a newly isolated Pseudomonas sp. The optimum operating conditions obtained from the quadratic form of the RSM and ANN models were pH 7.6, temperature 38 °C, and inoculum volume of 1.5 with 58.5 U/ml of predicted protease activity within 24 h of incubation. The normalized percentage mean squared error obtained from ANN and RSM models were 0.05 and 0.1%, respectively. The results demonstrated an higher prediction accuracy of ANN compared to RSM. This superiority of ANN over other multi factorial approaches could make this estimation technique a very helpful tool for fermentation monitoring and control.     

Identification of retinoblastoma related genes with shortest path in a protein-protein interaction network

This paper presents a new method for identifying retinoblastoma related genes by integrating gene expression profile and shortest path in a functional linkage graph. With the existing protein-protein interaction data from STRING, a weighted functional linkage graph is constructed. 119 consistently differentially expressed genes between retinoblastoma and normal retina were obtained from the overlap of two gene expression studies of retinoblastoma. Then the shortest paths between each pair of these 119 genes were determined with Dijkstra's algorithm. Finally, all the genes present on the shortest paths were extracted and ranked according to their betweenness and the 119 shortest genes with a betweenness greater than 100 and with a p-value less than 0.05 were selected for further analysis. We also identified 53 retinoblastoma related miRNAs from published miRNA array data and most of the 238 (119 consistently differentially expressed genes and 119 shortest path genes) retinoblastoma genes were shown to be target genes of these 53 miRNAs. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network included more cancer genes than did the genes identified from the gene expression profiles alone. In addition, these genes also had greater functional similarity to the reported cancer genes than did the genes identified from the gene expression profiles alone. This study shows promising results and proves the efficiency of the proposed methods.     

Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network

PurposeWe introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time.MethodsWe collected 105 patients’ Computed Tomography (CT) scans that diagnosed locally advanced cervical cancer and treated with radiotherapy in one hospital. Seven organs, including the bladder, bone marrow, left femoral head, right femoral head, rectum, small intestine and spinal cord were defined as OARs. The annotated contours of the OARs previously delineated manually by the patient’s radiotherapy oncologist and confirmed by the professional committee consisted of eight experienced oncologists before the radiotherapy were used as the ground truth masks. A multi-class segmentation model based on U-Net was designed to fulfil the OARs segmentation task. The Dice Similarity Coefficient (DSC) and 95th Hausdorff Distance (HD) are used as quantitative evaluation metrics to evaluate the proposed method.ResultsThe mean DSC values of the proposed method are 0.924, 0.854, 0.906, 0.900, 0.791, 0.833 and 0.827 for the bladder, bone marrow, femoral head left, femoral head right, rectum, small intestine, and spinal cord, respectively. The mean HD values are 5.098, 1.993, 1.390, 1.435, 5.949, 5.281 and 3.269 for the above OARs respectively.ConclusionsOur proposed method can help reduce the inter-observer and intra-observer variability of manual OARs delineation and lessen oncologists’ efforts. The experimental results demonstrate that our model outperforms the benchmark U-Net model and the oncologists’ evaluations show that the segmentation results are highly acceptable to be used in radiation therapy planning.     

Characterization of receptors with a new negative image: Use in molecular docking and lead optimization

The characterization of receptor binding sites is an important aspect of molecular docking, molecular recognition, and the structure-based design process. This characterization can take several forms: the receptor surface itself can be delineated or described, the space adjacent to the surface can be chemically mapped, or a negative image of the protein binding region can be generated. In this report, we describe a new method of constructing a negative image through generation of a set of spheres. These spheres lie along the receptor surface, and their centers represent possible ligand atom positions. By the method in which they are constructed, these spheres carry a limited amount of energetic and chemical information in addition to their primary geometric information. We test the accuracy of the image by comparing sphere positions to the positions of bound ligand atoms and propose a figure of merit for such tests. Then, we use the spheres to orient ligands in enzyme active sites and show how they can be used to generate low scoring configurations more efficiently than other approaches that search orientation space. In addition, two novel applications of these spheres are described: they are used to help identify structural differences among families of enzymes and to suggest points for ligand modification in analog design. Proteins 30:321–336, 1998. © 1998 Wiley-Liss, Inc.