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A feedback loop is a biological occurrence wherein the output of a system amplifies the system (positive feedback) or inhibits the system (negative feedback). Living organisms are able to maintain homeostasis through these feedback loops. This is the mechanism that enables us to keep our internal environment relatively constant. Examples of negative feedback include maintaining the blood glucose levels, maintaining body temperature, maintaining blood pH etc. when there is a change in the body (i.e. the blood glucose level increases), the nervous system detects the change, and stimulates an antidote hormonal response. While examples of positive feedback include the production of oxytocin hormone during child birth.
The difference between positive and negative feedback is their response to change: positive feedback amplifies change while negative feedback reduces change. This means that positive feedback will result in more of a product: more contractions, or more clotting platelets. While negative feedback will result in less of a product: less heat, less pressure, or less salt. Positive feedback moves away from a target point while negative feedback moves towards a target.
In feedback control, the systems outputs are measured and if they do not match the desired output, the controlled parameter is readjusted. If the input does not change, these differences usually come from disturbances. The controller has a feedback from the systems output which quantifies its deviation from the desired state, regardless of what causes this difference.
Feedback control is, for example, of a metabolic pathway by a metabolite of the pathway that acts in the direction opposite to metabolic flux, i.e. upstream or ‘earlier’ in the pathway. In feedforward control, the disturbances are measured and the controlled parameter is calculated based on a logical model. There is no feedback to see if the system is really in the desired state or is greatly deviated from the desired state. If disturbances that are not measured cause the systems outputs to differ from the desired one, the controller will not react. Feedforward control is, for example, of a metabolic pathway by a metabolite of the pathway that acts in the same direction as the metabolic flux, i.e. downstream or ‘later’ in the pathway, e.g. the activation of pyruvate kinase by fructose 1,6-bisphosphate.
Briefly explain the differences between negative control and positive control with suitable examples. Positive control is an experimental control which gives a positive result. It does not have the independent variable that researcher tests. However, it shows the desired effect which is expected from the independent variable. Positive control is a useful proof to show that the protocols, reagents and the equipment are functioning well without any errors. If experimental errors occur, positive control will not produce the correct outcome. In contrast, negative control does not give a response to the treatment. In experiments, negative control should be designed in a way that it does not produce the desired outcome of the experiment. Controls are essential elements of an experiment. Scientific experiments include them to eliminate experimental errors and biases. Results of the control experiments are useful for a validated statistical analysis of the experiment. Hence the reliability of the experiment can be increased by control treatments.
In the example where toxicity of a substance is tested, the positive control would be medium with cells and known toxic substance. While the negative control would be medium with cells and no toxic substance.
A biomarker is a characteristic that is measured and evaluated as an indicator of a normal biologic processes. To develop biomarkers, they are discovered, verified and then validated. Through clinical and medical examination like laboratory tests, physiological function tests and imaging tests biomarkers are discovered. Biomarker discovery requires high confidence identification of biomarker candidates with simultaneous quantitation information to indicate which proteins are changing to a statistically relevant degree in response to disease. Biomarker candidates identified in discovery need to be validated using larger sample sets covering a broad section of patient cohorts. To avoid a potential bottleneck associated with taking a large number of candidates to validation, a verification step is employed to screen potential biomarkers to ensure that only the highest quality leads from the discovery phase are taken into the costly validation stage. The verification stage requires a high throughput workflow with a minimum of sample preparation that provides both high specificity and sensitivity.
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