scholarly journals A Methodology for Vertical Translation Between Molecular and Organismal Level in Biological Feedback Loops

2021 ◽  
Author(s):  
Johannes W Dietrich
Keyword(s):  
Rate Constants ◽  
Feedback Loop ◽  
Competitive Inhibition ◽  
Feedback Loops ◽  
Mass Action ◽  
Nonlinear Dependence ◽  
Time Interval ◽  
Biological Feedback ◽  
Biochemical Basis ◽  
Vertical Translation

Feedback loops are among the primary network motifs in living organisms, ensuring survival via homeostatic control of key metabolites and physical properties. However, from a scientific perspective, their characterization is unsatisfactory since the usual modelling methodology is incompatible with the physiological and biochemical basis of metabolic networks. Therefore, any "vertical translation", i.e. the study of the correspondence between molecular and organismal levels of causality, is difficult and in most cases impossible. As a viable solution, we demonstrate an alternative modelling platform for biological feedback loops that is based on key biochemical principles, including mass action law, enzyme kinetics, binding of mediators to transporters and receptors, and basic pharmacological properties. Subsequently, we show how this framework can be used for translating from molecular to systems-level behaviour. Basic elements of the proposed modelling platform include Michaelis-Menten kinetics defining nonlinear dependence of the output y(t) on an input signal x(t) with the Hill-Langmuir equation y(t) = G * x(t)n / (D + x(t)n), non-competitive inhibition for linking stimulatory and inhibitory inputs with y(t) = G + x1(t) / ((D + x1(t) * (1 + x2(t) / KI)) and processing structures for distribution and elimination. Depending on the structure of the feedback loop, its equifinal (steady-state) behaviour can be solved in form of polynomials, with a quadratic equation for the simplest case with one feedback loop and a Hill exponent of 1, and higher-grade polynomials for additional feedback loops and/or integer Hill exponents > 1. As a companion to the analytical solution, a flexible class library (CyberUnits) facilitates computer simulations for studying the transitional behaviour of the feedback loop. Unlike other modelling strategies in biocybernetics and systems biology, this platform allows for straightforward translation from the statistical properties of single molecules on a "microscopic" level to the behaviour of the whole feedback loop on an organismal "macroscopic" level. An example is the Michaelis constant D, which is equivalent to (k-1 + k2) / k1, where k1, k-1 and k2 denote the rate constants for the association and dissociation of the enzyme-substrate or receptor-hormone complex, respectively. From the perspective of a single molecule the rate constants represent the probability (per unit time) that the corresponding reaction will happen in the subsequent time interval. Therefore 1/k represents the mean lifetime of the complex. Very similar considerations apply to the other described constants of the feedback loop. In summary, this modelling technique renders the translation from a molecular level to a systems perspective possible. In addition to providing new insights into the physiology of biological feedback loops, it may be a valuable tool for multiple disciplines of biomedical research, including drug design, molecular genetics and investigations on the effects of endocrine disruptors.

scholarly journals KINETICS OF PALM OIL TRANSESTERIFICATION IN METHANOL WITH POTASSIUM HYDROXIDE AS A CATALYST

2010 ◽  
Vol 8 (2) ◽  
pp. 219-225
Author(s):  
Yoeswono Yoeswono ◽  
Triyono Triyono ◽  
Iqmal Tahir
Keyword(s):  
Activation Energy ◽  
Palm Oil ◽  
Rate Constants ◽  
Potassium Hydroxide ◽  
Catalyst Concentration ◽  
Time Interval ◽  
Pseudo First Order Reaction ◽  
Exponential Factor ◽  
Potassium Methoxide ◽  
First Order

A study on palm oil transesterification to evaluate the effect of some parameters in the reaction on the reaction kinetics has been carried out. Transesterification was started by preparing potassium methoxide from potassium hydroxide and methanol and then mixed it with the palm oil. An aliquot was taken at certain time interval during transesterification and poured into test tube filled with distilled water to stop the reaction immediately. The oil phase that separated from the glycerol phase by centrifugation was analyzed by 1H-NMR spectrometer to determine the percentage of methyl ester conversion. Temperature and catalyst concentration were varied in order to determine the reaction rate constants, activation energies, pre-exponential factors, and effective collisions. The results showed that palm oil transesterification in methanol with 0.5 and 1 % w/w KOH/palm oil catalyst concentration appeared to follow pseudo-first order reaction. The rate constants increase with temperature. After 13 min of reaction, More methyl esters were formed using KOH 1 % than using 0.5 % w/w KOH/palm oil catalyst concentration. The activation energy (Ea) and pre-exponential factor (A) for reaction using 1 % w/w KOH was lower than those using 0.5 % w/w KOH.   Keywords: palm oil, transesterification, catalyst, first order kinetics, activation energy, pre-exponential factor

The Analysis of Economic and Environmental Feedback System and Proposed Management Policies in Jiuzhaigou

2012 ◽  
Vol 573-574 ◽  
pp. 251-255
Author(s):  
Qi Sheng Chen ◽  
Peng Ge ◽  
Pei Yu Ren
Keyword(s):  
Sustainable Development ◽  
Positive Feedback ◽  
Feedback Loop ◽  
Feedback System ◽  
Feedback Loops ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
The Sustainable Development ◽  
Scenic Spot ◽  
Management Policies

Jiuzhaigou scenic spot (Jiuzhaigou) is an ecological and economic system. The constraints and feedbacks between the economic subsystem and environmental subsystem are very important to the sustainable development. To study this system, the paper creates the system dynamics model about the constraints and feedbacks between economic subsystem and environmental subsystem. The model is made of one growth positive feedback loop of tourism development and two confine positive feedback loops of space constraint and pollution constraint. Then the paper brings up sustainable development management policies in Jiuzhaigou.

Kinetics of sulfur and pyrite oxidation by Thiobacillus thiooxidans. Competitive inhibition by increasing concentrations of cells

1991 ◽  
Vol 37 (3) ◽  
pp. 182-187 ◽  
Author(s):  
Hector M. Lizama ◽  
Isamu Suzuki
Keyword(s):  
Rate Constants ◽  
Elemental Sulfur ◽  
Competitive Inhibition ◽  
Pyrite Oxidation ◽  
Laboratory Strain ◽  
Sulfur Oxidation ◽  
Pulp Density ◽  
Strain Atcc ◽  
Thiobacillus Thiooxidans ◽  
Kinetics Of

The oxidation of elemental sulfur by two strains of Thiobacillus thiooxidans was studied by measuring the rate of O2 consumption at various concentrations of substrate and cells. In both the laboratory strain ATCC 8085 and the mine isolate SM-6, sulfur oxidation was competitively inhibited by T. thiooxidans cells; the Ki values were 0.65 and 0.05 mg wet cells∙mL−1, respectively. The rate constants were 500 and 143 μM O2∙min−1∙mg wet cells−1∙mL−1 and the Km values for sulfur concentration were 7.5 and 0.32% pulp density, respectively. Mine isolate SM-6 was used also to study pyrite (FeS2) oxidation by measuring the rate of O2 consumption. Oxidation of both washed and unwashed pyrite samples was competitively inhibited by increasing concentrations of cells; with each sample the Ki values was 0.05 mg wet cells∙mL−1. The rate constants for each sample were also the same (100 μM O2∙min−1∙mg wet cells−1∙mL−1), but the Km values were different (1.11% pulp density for washed pyrite and 2.81% pulp density for unwashed pyrite). Based on the rate of Fe solubilization from the washed pyrite sample, T. thiooxidans cells oxidized the sulfide released from pyrite dissolution beyond the oxidation state of elemental sulfur. Key words: Thiobacillus thiooxidans, sulfur, pyrite, oxidation, kinetics.

scholarly journals Kinetics of enzymes with iso-mechanisms: analysis of product inhibition

1993 ◽  
Vol 296 (2) ◽  
pp. 355-360 ◽  
Author(s):  
K L Rebholz ◽  
D B Northrop
Keyword(s):  
Rate Constants ◽  
Graphical Representation ◽  
Competitive Inhibition ◽  
Relative Rate ◽  
Additional Term ◽  
Product Inhibition ◽  
Free Enzyme ◽  
Approach Infinity ◽  
Relative Rate Constants ◽  
Kinetic Pattern

Isomerizations of free enzyme can be detected in kinetic patterns of product inhibition when the isomerization is partially rate-limiting. The kinetic pattern is non-competitive, owing to binding of substrate and product to different forms of free enzyme. This adds an additional term to the rate equation, sometimes represented as KSP. Several kineticists have noted that, as the rate of isomerization becomes high in relation to catalytic turnover, the intercept effect will become small, KSP will approach infinity, and the pattern will look competitive. Britton [(1973) Biochem. J. 133, 255-261] asserted that KSP will also approach infinity when the rate of isomerization becomes low. This second assertion is incorrect and can be traced to the particular model and graphical representation used to examine KSP as a function of relative rate constants. The function portrayed as a parabola with two roots for KSP is, instead, a straight line with one root. The algebraic condition justifying the second root obtains in the limit of zero in the rate of reaction and thus is not experimentally relevant, and the appearance of competitive inhibition, based on KSP alone, is not valid. Using a more general model, new equations are derived and presented which provide direct calculations of the apparent rate constants for free enzyme isomerizations from product-inhibition data when the equilibrium of the isomerization is near 1, and useful limits for the rate constants when greater than or less than 1.

FEEDBACK LOOPS, STABILITY AND MULTISTATIONARITY IN DYNAMICAL SYSTEMS

1995 ◽  
Vol 03 (02) ◽  
pp. 409-413 ◽  
Author(s):  
ERIK PLAHTE ◽  
THOMAS MESTL ◽  
STIG W. OMHOLT
Keyword(s):  
Dynamical Systems ◽  
Nonlinear Systems ◽  
Negative Feedback ◽  
Feedback Loop ◽  
Feedback Loops ◽  
Necessary Condition ◽  
Negative Feedback Loop ◽  
Positive Feedback Loop ◽  
First Order ◽  
First Order Differential Equations

By fairly simple considerations of stability and multistationarity in nonlinear systems of first order differential equations it is shown that under quite mild restrictions a negative feedback loop is a necessary condition for stability, and that a positive feedback loop is a necessary condition for multistationarity.

scholarly journals Conceptual Framework for Modeling Business Capabilities

10.28945/3148 ◽  
2007 ◽  
Author(s):  
Jean-Pierre Brits ◽  
Gerrit Botha ◽  
Marlien Herselman
Keyword(s):  
Conceptual Framework ◽  
Feedback Loop ◽  
Enterprise Architecture ◽  
Research Study ◽  
Feedback Loops ◽  
Organizational Analysis ◽  
Conceptual Approach ◽  
Business Environments ◽  
Agile Organization ◽  
Insight Into

Staying competitive in today’s fast changing markets and business environments has become a big issue in organizations these days. To be able to foresee the future of the industry and have insight into customer’s articulated and unarticulated needs are critical capabilities that organizations need to acquire in order to stay competitive. The objective of this research project is to provide a conceptual approach to analyze an organization and to provide a foundation that would support the architecture of an agile organization. Enterprise architecture, business capabilities, organizational analysis and innovation are the main practices that contribute towards the construction of capabilities and the development of the conceptual business capability framework. The most significant findings from this research study were the development of a conceptual framework that is later utilized to construct business capabilities. A business capability model has also been produced to visually depict a business capability. This study also provided two feedback loops, namely the organizational feedback loop and the innovative feedback loop.

Numerical analysis of a comprehensive model of M-phase control in Xenopus oocyte extracts and intact embryos

1993 ◽  
Vol 106 (4) ◽  
pp. 1153-1168 ◽  
Author(s):  
B. Novak ◽  
J.J. Tyson
Keyword(s):  
Negative Feedback ◽  
Positive Feedback ◽  
Xenopus Oocyte ◽  
Feedback Loop ◽  
Phase Control ◽  
Feedback Loops ◽  
Negative Feedback Loop ◽  
Time Lags ◽  
Tyrosine Residue ◽  
M Phase

To contribute to a deeper understanding of M-phase control in eukaryotic cells, we have constructed a model based on the biochemistry of M-phase promoting factor (MPF) in Xenopus oocyte extracts, where there is evidence for two positive feedback loops (MPF stimulates its own production by activating Cdc25 and inhibiting Wee1) and a negative feedback loop (MPF stimulates its own destruction by indirectly activating the ubiquitin pathway that degrades its cyclin subunit). To uncover the full dynamical possibilities of the control system, we translate the regulatory network into a set of differential equations and study these equations by graphical techniques and computer simulation. The positive feedback loops in the model account for thresholds and time lags in cyclin-induced and MPF-induced activation of MPF, and the model can be fitted quantitatively to these experimental observations. The negative feedback loop is consistent with observed time lags in MPF-induced cyclin degradation. Furthermore, our model indicates that there are two possible mechanisms for autonomous oscillations. One is driven by the positive feedback loops, resulting in phosphorylation and abrupt dephosphorylation of the Cdc2 subunit at an inhibitory tyrosine residue. These oscillations are typical of oocyte extracts. The other type is driven by the negative feedback loop, involving rapid cyclin turnover and negligible phosphorylation of the tyrosine residue of Cdc2. The early mitotic cycles of intact embryos exhibit such characteristics. In addition, by assuming that unreplicated DNA interferes with M-phase initiation by activating the phosphatases that oppose MPF in the positive feedback loops, we can simulate the effect of addition of sperm nuclei to oocyte extracts, and the lengthening of cycle times at the mid-blastula transition of intact embryos.

scholarly journals Learning Mode and Strategic Concept for the 4th Industrial Revolution

2018 ◽  
Vol 4 (3) ◽  
pp. 32 ◽  
Author(s):  
Fumio Kodama
Keyword(s):  
System Integration ◽  
Feedback Loop ◽  
Industrial Revolution ◽  
Feedback Loops ◽  
Total System ◽  
Technological Learning ◽  
Industrial Sectors ◽  
System Solution ◽  
Disruptive Innovations ◽  
It Revolution

Compared to information technology (IT) revolutions, which are characterized by disruptive innovations, the innovations required for the 4th Industrial Revolution will be characterized by the cumulativeness of the innovations. Therefore, we will need new modes of technological learning and new strategic concepts. The IT revolution can be divided into two parts: system integration and component supplying. Notice that all IT giants such as Apple and Google emerged only after Intel emerged as the dominant microprocessor unit (MPU) supplier. Therefore, we can ascertain that Intel emerged as a dominant MPU supplier by accommodating the different specifications made by Japanese customers in different industrial sectors. Thus, we will come to the concept of learning-by-accommodation as the dominant learning mode of IT module supplying, while the aforementioned IT giants implemented the mode of learning-by integration. The 4th Industrial Revolution will be characterized by technological evolutions rather than by disruptive innovations. They can be also decomposed into module supplying and the system integration. As to the mode of learning in the modules, we will find that the mode of learning will be prevalent in the same manner as IT module supplying, but in a more pre-emptive way. As for the feedback loops of the system integration, we will divide them into edge computing and the overall system solution. As for the edge solution, it is possible for latecomers to make a leapfrogging. In this context, we will make an analysis on the Chinese development of the “capacitor trolley bus” and come to a totally different mode of learning in the edge solution. We will name this mode “learning-by-porting”, using the terminology derived from the analysis of the design rules of a modular structure. By assuming that an overall feedback loop consists of a hierarchical structured collection of edge solutions, we can conclude that the overall solution is developed by multiple applications of the learning-by-porting of edge solutions. Additionally, the total system is a hierarchical structure, thus, a strategy toward the realization of the 4th industrial revolution will be a multilayered porting strategy.

From ‘ah’ to ‘bah’: social feedback loops for speech sounds at key points of developmental transition

2017 ◽  
Vol 45 (3) ◽  
pp. 807-825 ◽  
Author(s):  
Julie GROS-LOUIS ◽  
Jennifer L. MILLER
Keyword(s):  
Feedback Loop ◽  
Developmental Change ◽  
Relative Proportion ◽  
Feedback Loops ◽  
Speech Development ◽  
Social Feedback ◽  
Vocal Production ◽  
Developmental Transition ◽  
Key Points ◽  
First Words

AbstractSocial feedback is a driving force for speech development. A recent study provided a key finding to explain how contingent responses influence developmental change: infant speech-related vocalizations are contingent on responses to prior speech-related vocalizations (Warlaumont et al., 2014). However, the study did not distinguish between different speech-related vocalizations, vowel-like (V) and consonant–vowel (CV) vocalizations, which is important because CV vocalizations are a precursor to words. The present study explored parents’ responses to infants’ vocalizations and infants’ subsequent vocal production at a point when vocalizations become more like adult speech. The relative proportion of CVs following contingent responses to CV did not differ between 10- and 12-months-olds; however, there was only a significant contingent relationship between responses to CV and subsequent CV production in 12-month-olds. Results suggest a developmental transition and a social feedback loop for the production of more developmentally advanced sounds when infants are learning their first words.

Sign in / Sign up

Export Citation Format

Share Document