Global Lab Automation in Protein Engineering Market 2016 Analysis and Forecast to 2020

PUNE, INDIA, September 20, 2016 /EINPresswire.com/ -- Protein engineering involves various steps like macromolecular structure determination, from scaling data to calculating an electron density map. Softwares can be used to improve electron density maps through a statistical approach in combining experimental X-ray diffraction data with information about the expected characteristics of an electron map. Automation helps scientists to understand and solve the mysteries of protein dysfunction, including misfolding, aggregation, and abnormal movement. This will in turn help in monitoring the expression level of a target protein facilitating more effective drug discovery.

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The emergence of robotics has transformed the typical workday for scientists around the world. With the advent of automation techniques scientists are now able to set up, run, and analyze the results of experiments in a fraction of the time they needed in the past. In today's world, pulling maximum efficiency out of every department, including the R&D labs, is a major issue.
Automation of routine laboratory procedures, by the use of dedicated work stations and software to program instruments, allows associate scientists and technicians to think creatively about the implications of their experiments and to design effective follow-up projects or develop alternative approaches to their work instead of spending their days repeating tedious tasks.
Apart from reducing mundane tasks, the market for laboratory automation is also driven by the need for consistency in quality. Because, the cost of an error is very high, in a scientific paper or developing a drug. Even fairly low error rates can have a profound impact on the conclusions you make downstream.
It is believed that taking out human element helps in achieving more consistency. Great advances have been made in the moulding of plastics that has reduced the volumes of reagents used and enabled handling of smaller liquid volumes easy. These advancements has worked in favour of the development of Micro and Nano-litre plates to fit in with the automated liquid handling equipment.
Validation and data quality play a major role in modern life science. Legal validation, patents, and clinical testing have become crucial issues. Automation enables a much higher reproducibility and better documentation of data. This allows the production of more data points with great ease. It also ensures the safety of personnel in the presence of infectious or potentially hazardous material.

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Automation is used in a wide variety of life science applications ranging from Proteomics to Systems biology. Automation is a dominant feature in the diagnostics market followed by the discovery and research labs. In clinical diagnostics, where profits are based on the number of samples, high throughput is the core driving factor. Total automation is generally preferred in such labs and manufacturing setups. Research labs and academic institutions are generally opting for modular automation wherein they reduce the human intervention in tedious and repetitive tasks.
North America is clearly the market leader with a total market share of 60%, followed by Europe. Asia and Latin America are emerging as there has been a considerable increase in outsourcing pharmaceutical manufacturing to these regions due to the availability of cheaper labour and resources.
This report analyses the market across various geographies with the key trends in each region. It provides the market share and profiles of the top companies involved in Lab automation. The report also provides insights on how the market will vary within the next five years.

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Table of content
1. INTRODUCTION
1.1 Research Methodology
1.1.1 Definition of the Market
1.1.2 Report Description
1.1.3 Executive Summary
2. KEY FINDINGS OF THE STUDY
3. MARKET OVERVIEW
3.1 Market Segmentation
3.2 Overview
3.3 Industry Value Chain Analysis
3.4 Industry Attractiveness - Porter's 5 Force Analysis
4. MARKET DYNAMICS
4.1 Introduction
4.2 Drivers
4.3 Advancement in Plastic Molding Technology
4.4 Development of smaller automated machines.
4.5 Need for higher reproducibility and effective management of the vast amounts of data generated.
4.6 Constraints
4.7 Initial setup is expensive
4.8 Loss of flexibility in process
4.9 Inter Connecting various platforms across the lab.
5. Technology Overview
5.1 Technology Snapshot
5.2 Industry Applications
6. Different Equipment in Lab Automation
6.1 Automated Liquid Handlers
6.2 Automated Plate Handlers
6.3 Robotic Arms
6.4 Automated Storage & Retrieval Systems (ASRS)

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Norah Trent
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