scholarly journals Physical Constraints on Epistasis

2020 ◽  
Vol 37 (10) ◽  
pp. 2865-2874 ◽  
Author(s):  
Kabir Husain ◽  
Arvind Murugan
Keyword(s):  
Experimental Data ◽  
Biological System ◽  
Regulatory Networks ◽  
Collective Mode ◽  
Biological Systems ◽  
Living Systems ◽  
Single Mutation ◽  
Physical Constraints ◽  
Long Time

Abstract Living systems evolve one mutation at a time, but a single mutation can alter the effect of subsequent mutations. The underlying mechanistic determinants of such epistasis are unclear. Here, we demonstrate that the physical dynamics of a biological system can generically constrain epistasis. We analyze models and experimental data on proteins and regulatory networks. In each, we find that if the long-time physical dynamics is dominated by a slow, collective mode, then the dimensionality of mutational effects is reduced. Consequently, epistatic coefficients for different combinations of mutations are no longer independent, even if individually strong. Such epistasis can be summarized as resulting from a global nonlinearity applied to an underlying linear trait, that is, as global epistasis. This constraint, in turn, reduces the ruggedness of the sequence-to-function map. By providing a generic mechanistic origin for experimentally observed global epistasis, our work suggests that slow collective physical modes can make biological systems evolvable.

scholarly journals Modelling Biological Systems with Competitive Coherence

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Vic Norris ◽  
Maurice Engel ◽  
Maurice Demarty
Keyword(s):  
Dna Replication ◽  
Biological System ◽  
Biological Systems ◽  
Input Sequence ◽  
Living Systems ◽  
Small Subset ◽  
Learning Program ◽  
Evolutionary Advantage ◽  
Over Time

Many living systems, from cells to brains to governments, are controlled by the activity of a small subset of their constituents. It has been argued that coherence is of evolutionary advantage and that this active subset of constituents results from competition between two processes, a Next process that brings about coherence over time, and a Now process that brings about coherence between the interior and the exterior of the system at a particular time. This competition has been termed competitive coherence and has been implemented in a toy-learning program in order to clarify the concept and to generate—and ultimately test—new hypotheses covering subjects as diverse as complexity, emergence, DNA replication, global mutations, dreaming, bioputing (computing using either the parts of biological system or the entire biological system), and equilibrium and nonequilibrium structures. Here, we show that a program using competitive coherence, Coco, can learn to respond to a simple input sequence 1, 2, 3, 2, 3, with responses to inputs that differ according to the position of the input in the sequence and hence require competition between both Next and Now processes.

scholarly journals The Systemic Theory of Living Systems and Relevance to CAM

2005 ◽  
Vol 2 (1) ◽  
pp. 13-18 ◽  
Author(s):  
José A. Olalde Rangel
Keyword(s):  
Biological System ◽  
Treatment Strategy ◽  
Living Systems ◽  
State Of Health ◽  
Total Entropy ◽  
First Line ◽  
Available Energy ◽  
Systemic Theory ◽  
Or Organization ◽  
Normal Health

The Systemic Theory of Living Systems is being published in several parts in eCAM. The theory is axiomatic. It originates from the phenomenological idea that physiological health is based on three factors: integrity of its structure or organization,O, functional organic energy reserve,E, and level of active biological intelligence,I. From the theory is derived a treatment strategy called Systemic Medicine (SM). This is based on identifying and prescribing phytomedicines and/or other medications that strengthen each factor. Energy-stimulating phytomedicines increase available energy and decrease total entropy of an open biological system by providing negative entropy. The same occurs with phytomedicines that act as biological intelligence modulators. They should be used as the first line of treatment in all ailments, since all pathologies, by definition, imply a higher than normal organic entropy. SM postulates that the state of health,H, of an individual, is effectively equal to the product of the strength of each factorH=O×E×I. SM observes that when all three factors are brought back to ideal levels, patients' conditions begin the recovery to normal health.

scholarly journals BioNetLink - An Architecture for Working with Network Data

2014 ◽  
Vol 11 (2) ◽  
pp. 68-79
Author(s):  
Matthias Klapperstück ◽  
Falk Schreiber
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Regulatory Networks ◽  
Large Data ◽  
Biological Data ◽  
Network Data ◽  
Data Sets ◽  
Dynamic Views ◽  
Gene Regulatory ◽  
Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

Interaction of Point Defects in Ice

1978 ◽  
Vol 21 (85) ◽  
pp. 115-122
Author(s):  
J. H. Bilgram ◽  
H. Gränicher
Keyword(s):  
Experimental Data ◽  
Dielectric Properties ◽  
Temperature Dependence ◽  
Point Defects ◽  
Low Frequency ◽  
Defect Concentration ◽  
Dielectric Measurements ◽  
Long Time ◽  
Nmr Techniques ◽  
Bjerrum Defect

AbstractThe interaction of point detects in ice has been neglected for a long time. Experimental data obtained from dielectric measurements on HF-doped crystals stimulated a new evaluation of the possibility of an interaction between Bjerrum defects and ions. In a previous paper it has been shown that this leads us to assume the existence of aggregates of Bjerrum defects and ions. In this paper these aggregates and Bjerrum defects are used to explain the dielectric properties of ice, especially the temperature dependence of the product of the high and low frequency conductivity σ0σ∞.The interaction of Bjerrum defects and impurity molecules leads to a dependence of the concentration of frenkel pairs on Bjerrum-defect concentration. At HF concentrations above the native Bjerrum-defect concentration the formation of a Frenkel pair is enhanced. This leads to the fast out-diffusion which has been studied in highly doped crystals by means of NMR techniques.

scholarly journals Ut vis sic tensio

2018 ◽  
Vol 45 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Giuseppe Saccomandi
Keyword(s):  
Experimental Data ◽  
Mechanical Properties ◽  
Elastic Strain ◽  
Strain Energy ◽  
Nonlinear Response ◽  
Solid Mechanics ◽  
Functional Form ◽  
Elastic Strain Energy ◽  
Strongly Nonlinear ◽  
Long Time

The mechanical properties of rubber-like materials have been offering an outstanding challenge to the solid mechanics community for a long time. The behaviour of such materials is quite difficult to predict because rubber self-organizes into mesoscopic physical structures that play a prominent role in determining their complex, history-dependent and strongly nonlinear response. In this framework one of the main problems is to find a functional form of the elastic strain-energy that best describes the experimental data in a mathematical feasible way. The aim of this paper is to give a survey of recent advances aimed at solving such a problem.

scholarly journals Adaptive Intelligent Welding Manufacturing

2021 ◽  
Vol 100 (01) ◽  
pp. 63-83
Author(s):  
YUMING ZHANG ◽  
◽  
QIYUE WANG ◽  
YUKANG LIU
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Deep Learning ◽  
Welding Process ◽  
Welding Parameters ◽  
Weld Quality ◽  
Robotic Welding ◽  
Process Variables ◽  
Physical Constraints ◽  
Needed Information

Optimal design of the welding procedure gives the desired welding results under nominal welding conditions. During manufacturing, where the actual welding manufacturing conditions often deviate from the nominal ones used in the design, applying the designed procedure will produce welding results that are different from the desired ones. Adaption is needed to make corrections and adjust some of the welding parameters from those specified in the design. This is adaptive welding. While human welders can be adaptive to make corrections and adjustments, their performance is limited by their physical constraints and skill level. To be adaptive, automated and robotic welding systems require abilities in sensing the welding process, extracting the needed information from signals from the sensors, predicting the responses of the welding process to the adjustments on welding parameters, and optimizing the adjustments. This results in the application of classical sensing, modeling of process dynamics, and control system design. In many cases, the needed information for the weld quality and process variables of our concern is not easy to extract from the sensor’s data. Studies are needed to propose the phenomena to sense and establish the scientific foundation to correlate them to the weld quality or process variables of our concern. Such studies can be labor intensive, and a more automated approach is needed. Analysis suggests that artificial intelligence and machine learning, especially deep learning, can help automate the learning such that the needed intelligence for robotic welding adaptation can be directly and automatically learned from experimental data after the physical phenomena being represented by the experimental data has been appropriately selected to make sure they are fundamentally correlated to that with which we are concerned. Some adaptation abilities may also be learned from skilled human welders. In addition, human-robot collaborative welding may incorporate adaptations from humans with the welding robots. This paper analyzes and identifies the challenges in adaptive robotic welding, reviews efforts devoted to solve these challenges, analyzes the principles and nature of the methods behind these efforts, and introduces modern approaches, including machine learning/deep learning, learning from humans, and human-robot collaboration, to solve these challenges.

scholarly journals Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

2020 ◽  
Author(s):  
Claudio Tomi-Andrino ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Philippe Soucaille ◽  
Klaus Winzer ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Flux Analysis ◽  
In Silico ◽  
Physicochemical Parameters ◽  
Metabolic Flux ◽  
Living Systems ◽  
Flux Analysis ◽  
Flux Balance ◽  
Metabolic Fluxes ◽  
Balance Analysis

AbstractMetabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.Author summaryBiotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.

Parallel Multi-Cycle LES of an Optical Pent-Roof DISI Engine Under Motored Operating Conditions

2017 ◽  
Author(s):  
Noah Van Dam ◽  
Wei Zeng ◽  
Magnus Sjöberg ◽  
Sibendu Som
Keyword(s):  
Experimental Data ◽  
Direct Injection ◽  
Operating Conditions ◽  
New Strategy ◽  
Long Time ◽  
Order Of Magnitude ◽  
Structure Index ◽  
Number Of Cycles ◽  
The Many ◽  
Direct Injection Spark Ignition

The use of Large-eddy Simulations (LES) has increased due to their ability to resolve the turbulent fluctuations of engine flows and capture the resulting cycle-to-cycle variability. One drawback of LES, however, is the requirement to run multiple engine cycles to obtain the necessary cycle statistics for full validation. The standard method to obtain the cycles by running a single simulation through many engine cycles sequentially can take a long time to complete. Recently, a new strategy has been proposed by our research group to reduce the amount of time necessary to simulate the many engine cycles by running individual engine cycle simulations in parallel. With modern large computing systems this has the potential to reduce the amount of time necessary for a full set of simulated engine cycles to finish by up to an order of magnitude. In this paper, the Parallel Perturbation Methodology (PPM) is used to simulate up to 35 engine cycles of an optically accessible, pent-roof Direct-injection Spark-ignition (DISI) engine at two different motored engine operating conditions, one throttled and one un-throttled. Comparisons are made against corresponding sequential-cycle simulations to verify the similarity of results using either methodology. Mean results from the PPM approach are very similar to sequential-cycle results with less than 0.5% difference in pressure and a magnitude structure index (MSI) of 0.95. Differences in cycle-to-cycle variability (CCV) predictions are larger, but close to the statistical uncertainty in the measurement for the number of cycles simulated. PPM LES results were also compared against experimental data. Mean quantities such as pressure or mean velocities were typically matched to within 5–10%. Pressure CCVs were under-predicted, mostly due to the lack of any perturbations in the pressure boundary conditions between cycles. Velocity CCVs for the simulations had the same average magnitude as experiments, but the experimental data showed greater spatial variation in the root-mean-square (RMS). Conversely, circular standard deviation results showed greater repeatability of the flow directionality and swirl vortex positioning than the simulations.

Commutation Loss in Hydrostatic Pumps and Motors

2021 ◽  
Author(s):  
Robin Mommers ◽  
Peter Achten ◽  
Jasper Achten ◽  
Jeroen Potma
Keyword(s):  
Experimental Data ◽  
Energy Loss ◽  
Simulation Model ◽  
Operating Conditions ◽  
Chamber Pressure ◽  
Working Mechanism ◽  
Working Chamber ◽  
Lumped Parameter ◽  
Long Time ◽  
Pressure Changes

Abstract In mobile hydraulic applications, more efficient machinery generally translates to smaller batteries or less diesel consumption, and smaller cooling solutions. A key part of such systems are hydrostatic pumps and motors. While these devices have been around for a long time, some of the causes of energy loss in pump and motors are still not properly defined. This paper focuses on one of the causes of energy loss in pumps and motors, by identifying the energy loss as a result of the process of commutation. By nature, all hydrostatic pumps and motors have some form of commutation: the transition from the supply port to the discharge port of the machine (and vice versa). During commutation, the connection between the working chamber and the ports is temporarily closed. The chamber pressure changes by compression or decompression that is the result of the rotation of the working mechanism. Ideally, the connection to one of the ports is opened once the chamber pressure equals the port pressure. When the connection is opened too early or too late, energy is lost. This paper describes a method to predict the commutation loss using a lumped parameter simulation model. To verify these predictions, experimental data of a floating cup pump was compared to the calculated values, which show a decent match. Furthermore, the results show that, depending on the operating conditions, up to 50% of all losses in this pump are caused by improper commutation.

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