scholarly journals Evolutionary constraints in regulatory networks defined by partial order between phenotypes

2019 ◽  
Author(s):  
Manjunatha Kogenaru ◽  
Philippe Nghe ◽  
Frank J. Poelwijk ◽  
Sander J. Tans
Keyword(s):  
Partial Order ◽  
Network Architecture ◽  
Regulatory Networks ◽  
Fine Tuning ◽  
Evolutionary Constraints ◽  
Pareto Optimal ◽  
Binding Affinities ◽  
Pareto Optimal Front ◽  
Gene Regulation Networks ◽  
Network Output

AbstractGene regulation networks allow organisms to adapt to diverse environmental niches. However, the constraints underlying the evolution of regulatory phenotypes remain ill-defined both theoretically and experimentally. Here, we show that the concept of partial order identifies such constraints, and test the predictions by experimentally evolving an engineered signal-integrating network in multiple environments. We find that populations: 1) expand in fitness space along the Pareto-optimal front predicted by conflicts in regulatory demands, by fine-tuning binding affinities within the network, 2) expand beyond this constraint by changes in the network structure, thus allowing access to new fitness domains. Strikingly, the constraint predictions are based on whether the network output increases or decreases in response to the different signals, and do not require information on the network architecture or underlying genetics. Overall, our findings show that limited knowledge on current regulatory phenotypes can provide predictions on future evolutionary constraints.

scholarly journals TSAL: Two steps Adversarial learning based domain adaptation

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Haidi Hasan Badr ◽  
Nayer Mahmoud Wanas ◽  
Magda Fayek
Keyword(s):  
Network Architecture ◽  
Domain Adaptation ◽  
Data Availability ◽  
Fine Tuning ◽  
Target Domain ◽  
Adversarial Learning ◽  
Prior Art ◽  
Real World Problem ◽  
Wide Range ◽  
Benchmark Datasets

Since labeled data availability differs greatly across domains, Domain Adaptation focuses on learning in new and unfamiliar domains by reducing distribution divergence. Recent research suggests that the adversarial learning approach could be a promising way to achieve the domain adaptation objective. Adversarial learning is a strategy for learning domain-transferable features in robust deep networks. This paper introduces the TSAL paradigm, a two-step adversarial learning framework. It addresses the real-world problem of text classification, where source domain(s) has labeled data but target domain (s) has only unlabeled data. TSAL utilizes joint adversarial learning with class information and domain alignment deep network architecture to learn both domain-invariant and domain-specific features extractors. It consists of two training steps that are similar to the paradigm, in which pre-trained model weights are used as initialization for training with new data. TSAL’s two training phases, however, are based on the same data, not different data, as is the case with fine-tuning. Furthermore, TSAL only uses the learned domain-invariant feature extractor from the first training as an initialization for its peer in subsequent training. By doubling the training, TSAL can emphasize the leverage of the small unlabeled target domain and learn effectively what to share between various domains. A detailed analysis of many benchmark datasets reveals that our model consistently outperforms the prior art across a wide range of dataset distributions.

scholarly journals Integration of Operability Framework and Pareto Optimal Front to Calculate Optimal Condition of Ethane Cracking Process

2020 ◽  
pp. 105-113
Author(s):  
M. Farsi
Keyword(s):  
Pareto Front ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Base Case ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Cracking Process

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.  

scholarly journals Sequential Activity in Asymmetrically Coupled Winner-Take-All Circuits

2014 ◽  
Vol 26 (9) ◽  
pp. 1973-2004 ◽  
Author(s):  
Hesham Mostafa ◽  
Giacomo Indiveri
Keyword(s):  
Network Architecture ◽  
External Input ◽  
Spike Timing ◽  
Fine Tuning ◽  
Learning Mechanisms ◽  
State Dependent ◽  
Synaptic Dynamics ◽  
Experimental Findings ◽  
Winner Take All ◽  
Take All

Understanding the sequence generation and learning mechanisms used by recurrent neural networks in the nervous system is an important problem that has been studied extensively. However, most of the models proposed in the literature are either not compatible with neuroanatomy and neurophysiology experimental findings, or are not robust to noise and rely on fine tuning of the parameters. In this work, we propose a novel model of sequence learning and generation that is based on the interactions among multiple asymmetrically coupled winner-take-all (WTA) circuits. The network architecture is consistent with mammalian cortical connectivity data and uses realistic neuronal and synaptic dynamics that give rise to noise-robust patterns of sequential activity. The novel aspect of the network we propose lies in its ability to produce robust patterns of sequential activity that can be halted, resumed, and readily modulated by external input, and in its ability to make use of realistic plastic synapses to learn and reproduce the arbitrary input-imposed sequential patterns. Sequential activity takes the form of a single activity bump that stably propagates through multiple WTA circuits along one of a number of possible paths. Because the network can be configured to either generate spontaneous sequences or wait for external inputs to trigger a transition in the sequence, it provides the basis for creating state-dependent perception-action loops. We first analyze a rate-based approximation of the proposed spiking network to highlight the relevant features of the network dynamics and then show numerical simulation results with spiking neurons, realistic conductance-based synapses, and spike-timing dependent plasticity (STDP) rules to validate the rate-based model.

Application of pareto-optimal front as an option to desirability function for the optimization of a microbiological process

2020 ◽  
Vol 155 ◽  
pp. 104682
Author(s):  
Pablo C. Giordano ◽  
Virginia Pereyra ◽  
Alejandro J. Beccaria ◽  
Silvana Vero ◽  
Héctor C. Goicoechea
Keyword(s):  
Desirability Function ◽  
Pareto Optimal ◽  
Pareto Optimal Front ◽  
Microbiological Process

scholarly journals Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

2000 ◽  
Vol 8 (2) ◽  
pp. 173-195 ◽  
Author(s):  
Eckart Zitzler ◽  
Kalyanmoy Deb ◽  
Lothar Thiele
Keyword(s):  
Evolutionary Algorithms ◽  
Multiobjective Optimization ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Multiobjective Evolutionary Algorithms ◽  
Test Functions ◽  
Test Function ◽  
Pareto Optimal Front ◽  
Systematic Comparison ◽  
Empirical Results

In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.

scholarly journals A Simple Procedure for Searching Pareto Optimal Front in Machining Process: Electric Discharge Machining

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ushasta Aich ◽  
Simul Banerjee
Keyword(s):  
Electric Discharge ◽  
Removal Rate ◽  
Simple Procedure ◽  
Operating Conditions ◽  
Machining Process ◽  
Optimum Process ◽  
Pareto Optimal ◽  
Electric Discharge Machining ◽  
Optimum Process Parameter ◽  
Pareto Optimal Front

Optimum control parameter setting in complex and stochastic type processes is one of the most challenging problems to the process engineers. As such, effective model development and determination of optimal operating conditions of electric discharge machining process (EDM) are reasonably difficult. In this apper, an easy to handle optimization procedure, weight-varying multiobjective simulated annealing, is proposed and is applied to optimize two conflicting type response parameters in EDM—material removal rate (MRR) and average surface roughness (Ra) simultaneously. A solution set is generated. The Pareto optimal front thus developed is further modeled. An inverse solution procedure is devised so that near-optimum process parameter settings can be determined for specific need based requirements of process engineers. The results are validated.

scholarly journals Gynoecium size and ovule number are interconnected traits that impact seed yield

2020 ◽  
Vol 71 (9) ◽  
pp. 2479-2489 ◽  
Author(s):  
Mara Cucinotta ◽  
Maurizio Di Marzo ◽  
Andrea Guazzotti ◽  
Stefan de Folter ◽  
Martin M Kater ◽  
...  
Keyword(s):  
Seed Yield ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Current Knowledge ◽  
Fine Tuning ◽  
World Population ◽  
Meristematic Tissue ◽  
Reproductive Part ◽  
Ovule Number ◽  
Gene Regulatory

Abstract Angiosperms form the largest group of land plants and display an astonishing diversity of floral structures. The development of flowers greatly contributed to the evolutionary success of the angiosperms as they guarantee efficient reproduction with the help of either biotic or abiotic vectors. The female reproductive part of the flower is the gynoecium (also called pistil). Ovules arise from meristematic tissue within the gynoecium. Upon fertilization, these ovules develop into seeds while the gynoecium turns into a fruit. Gene regulatory networks involving transcription factors and hormonal communication regulate ovule primordium initiation, spacing on the placenta, and development. Ovule number and gynoecium size are usually correlated and several genetic factors that impact these traits have been identified. Understanding and fine-tuning the gene regulatory networks influencing ovule number and pistil length open up strategies for crop yield improvement, which is pivotal in light of a rapidly growing world population. In this review, we present an overview of the current knowledge of the genes and hormones involved in determining ovule number and gynoecium size. We propose a model for the gene regulatory network that guides the developmental processes that determine seed yield.

scholarly journals Predicting Evolutionary Constraints by Identifying Conflicting Demands in Regulatory Networks

Cell Systems ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 526-534.e3
Author(s):  
Manjunatha Kogenaru ◽  
Philippe Nghe ◽  
Frank J. Poelwijk ◽  
Sander J. Tans
Keyword(s):  
Regulatory Networks ◽  
Evolutionary Constraints ◽  
Conflicting Demands

scholarly journals Physiological regulatory networks: ecological roles and evolutionary constraints

2012 ◽  
Vol 27 (8) ◽  
pp. 428-435 ◽  
Author(s):  
Alan A. Cohen ◽  
Lynn B. Martin ◽  
John C. Wingfield ◽  
Scott R. McWilliams ◽  
Jennifer A. Dunne
Keyword(s):  
Regulatory Networks ◽  
Evolutionary Constraints ◽  
Ecological Roles
Sign in / Sign up

Export Citation Format

Share Document