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Scarborough Biotechnology offers free guide to help people understand genetic engineering

Posted by mswayne on December 2, 2018 at 7:45 AM Comments comments (0)

Biotechnology terms, such as CRISPR, genome edits and GMOs, are making their way out of the labs and into everyday lives, proving that genetic engineering is now an undeniable force that will shape everything from what people eat to how long they live. Scarborough Biotechnology, a biotechnology company that is creating tools to master the genome, is offering a genetic engineering guide for students, business owners, and just people wondering how this technology will change the world, according to Macauley Coggins, director of Scarborough Biotechnology. “

The guide includes a brief history of genetic engineering and a summary of key terms and methods in the field. For example, the guide explains that transgenesis is when genetic engineers take a gene from one organism and insert it into another one. When researchers talk about gene knockins, they are referring to inserting a specific gene into a certain place in the chromosome, which is a string of nucleic acids and proteins that holds genetic information. Likewise, a gene knockout is the removal or replacement of a gene in the chromosome.

Coggins also said the guide helps people understand poly-omics and how computers can now be used to model the metabolism, also called in-silico.

“In the past, the only way you could study metabolism was to do it in the body -- in vivo -- or in a test tube -- in vitro,” said Coggins. “That is also very expensive. Now, we can use computers to model the human metabolism, which is much quicker, more efficient, and less expensive way to start the experimental process.”

Researchers can now, for example, use software, like Scarborough Biotechnology’s Snapshot, to study how much metabolite a protein can produce before they even begin to verify the process in the body, or in a test tube.

The guide also explains how gene therapy has made strides in medical cures and seems poised to make breakthroughs in diseases once thought to be incurable, or even untreatable. Gene therapy is being either studied, or used to treat patients with hemophilia and blindness.

“It’s an incredible and an exciting time to be involved in the genetic engineering industry and we believe that having more trained people will help create cures for some of the world’s most deadliest and most debilitating diseases,” said Coggins.

You can request your copy of the Guide to Genetic Engineering, or find out more information about Snapshot genetic engineering technology, by visiting www.scarboroughbiotech.com or send us an email at support@scarboroughbiotech.com.

7 Ways Exponential Technology is Leading to Exponential Medicine

Posted by mswayne on November 14, 2018 at 5:25 AM Comments comments (0)

The healthcare, pharmaceutical, and medical industries are investing heavily in cutting-edge software solutions and smarter machine learning and artificial intelligence technology. The full benefits of this tech revolution may lead to a revolution in disease treatment and extend the healthspan of millions of patients, according to those experts.

According to recent statistics, information technology budgets are increasing across the country, but experts say the healthcare IT spending, in particular, is spiking into double digits. Healthcare spending in big data, for example, is expected to grow 42 percent in 2019, according to Technavio.

These increased tech budgets may be just the spark that ignites a conflagration in healthcare. Here are just a few ways technology will change healthcare.

7. More Personalised Treatments

Currently, treatments are designed to treat not individuals, but massive populations of people for a few reasons. First, this is about as granular as medical research can go. Most of the keys to health -- the genome, for example -- are just starting to be explored and understood. Second, treating groups is economical. Creating a treatment for each person is too expensive.

However, as bioengineers using machine learning and AI discover more about the human genome -- and how to manipulate it -- they will be able to tailor treatments to individuals and do so inexpensively.

From Science Daily: “Scientists have developed a successful method to make truly personalized predictions of future disease outcomes for patients with certain types of chronic blood cancers. Researchers combined extensive genetic and clinical information to The research identified eight different genetic subgroups of the disease and could lead to personalized medicine for patients with these blood cancers.”

6. More Patient Engagement

We are in the initial stages of a massive disruption in medical treatment. The days of doctors, surgeons, and other medical professionals being solely responsible for administering care are starting to fade. In the near future, patients will be able to monitor, assess, and adjust their own treatments with help from machines. They will also have unmatched access to their human medical helpers.

The technology will also help doctors and their office workers connect with patients, according to HealthWorks Collective.

“Search Health Hit says that an AI-powered business phone system for communication will also help improve the patient experience and satisfaction even when patients are at home.” -- HealthWorks Collective.

5. More Opportunities for Mobile Health

With fitness apps and other wearables, AI will be a health professional that is on-duty 24-7. It can monitor fitness, rest, stress, and even sleep. Then, it can offer suggestions and recommendations.

These interactions can happen anywhere at anytime -- at work, at home, at play, and even in the bed.

As an example, a startup called Sweetch, uses AI to help predict, prevent and improve outcomes for people who have diabetes. The startup has recently partnered with WellSpan Health, a healthcare company to provide its mobile health app to the company’s 15,000 employees.

4. Telehealth

With smart speakers with screens, like Amazon’s Echo Show, the doctor will be able to see you anytime. And, with AI using voice assistance and smart speakers, doctors may only be on call for more serious subjects. AI chatbots can handle much of the routine healthcare inquiries and appointments.

But, it’s not just patients who will use smart speakers and smart screens. Doctors will be able to use them to set up appointments, learn new medical techniques on the fly, and reach out to patients and their office colleagues at any time and at any place.

3. Blockchained

Finally, expect the information drowned in the massive bureaucracy that swallows patient data like a tsunami to finally recede. Blockchain technology will soon put the power of their data back into their own hands. People will be able to loan their data to research efforts, sell their data to pharmaceuticals, and offer their personal data across the healthcare enterprise instantly.

Forms and long lines will be a thing of the past,many futurists predict.

2. Cheaper

Healthcare is expensive, pulling needed income from the wallets and pocketbooks of people hoping to get well. Sickness also makes people less productive and drags them out of the workforce.

The money invested in clinical trials for drug and treatment discovery is in the range of tens of billions of dollars each year, a figure that’s growing.

If this continues, health experts set the global cost of healthcare at more than $18 trillion by 2040.

That’s a huge drag on the economy.

These figures don’t take the impact of technology into consideration, however.

Technology may be able to make hospitals more efficient, take the often expensive guesswork out of treatment, and ease the strain on people’s budgets. As technology becomes increasingly cheaper, its impact on healthcare may become exponentially greater.

Scarborough Biotechnology’s Snapshot is one example. It can perform robust genetic analysis for prices that small- to medium-sized biotech firms can afford. This puts genetic breakthroughs in the hands of more researchers.

1. Quicker

Artificial intelligence will soon give doctors advice in seconds on a patient’s conditions. It will be able to immediately serve up treatment recommendations. AI and machine learning tools will be able to provide aid that speeds recovery. When they patient goes home, expect technology to monitor and provide rehabilitation services.

The recovery process for many diseases and operations will be reduced by weeks.

How long will all of this happen? The answers vary. Some say months. Some years. Some say a decade. Most experts say, though, that you don’t have to wait. These things are happening right now. The healthcare revolution has arrived. It’s just not evenly distributed yet.


New Visualization Tool Makes Snapshot Data Come Alive

Posted by mswayne on October 30, 2018 at 5:00 PM Comments comments (0)

Visualizing data is an important part of understanding the data.


In the latest version of Snapshot, scientists can now see visual representations of gene expressions through the visual analytics function, said Macauley Coggins, director of Scarborough Biotechnology. Snapshot is the company’s premier software solution that allows genome-scale model of human metabolism.


“We think that building this visualization feature will make studying gene expressions far simpler and more immersive,” said Coggins. “It will also help collaboration with other members of your research teams, or fellow students.”


The following image shows the visualization of gene expression and reaction activity for Stage 1 breast cancer using real patient data.



“This really gives the cancer researcher the best of both worlds,” said Coggins. “Often, scientists are able to immediately detect something out of the ordinary in a chart and then examine the exact data, which also easily accessed in Snapshot’s regular display.”


Coggins added that seeing the data in this form also helps with comparing data from several charts.


Snapshot is designed to help researchers:


  • Explore Gene knockin / knockout
  • Highlight potential diseases based upon cell conditions
  • Investigate drug modelling including Ibuprofen and how it affects metabolic pathways
  • Predict cellular functionality from TPM gene expression data, as well as predict a range of genetic disorders from gene expression data

To learn more about how Snapshot is helping scientists and students master genetic engineering, see us at www.scarboroughbiotech.com or send us an email at support@scarboroughbiotech.com

4 Ways Snapshot Is Disrupting Genetic Engineering

Posted by mswayne on October 3, 2018 at 7:20 AM Comments comments (0)

When you think genetic engineering, you tend to think of large companies and well-funded research laboratories. But, with breakthroughs such as CRISPR becoming more common, smaller companies and teams now have the same access to the power of genetic engineering that once was only in the hands of these labs and big companies.

However, genetic engineering tools and equipment is still expensive. But, that was before the disruptive power of Snapshot came along.

Scarborough Biotechnology’s Snapshot is one of the first commercial in-silico models that currently models 92 genes, 53 reactions, 109 metabolites, 3 cellular compartments -- different parts within the cell -- and 7 diseases, a list, by the way, that is growing each day.

Here are four ways that Snapshot is disrupting genetic engineering and unleashing the next wave of biotechnology discoveries:


Disruption 1 Instant and Accurate Analysis

Rather than performing expensive, time-consuming experiments to analyse the effects of gene edits, Snapshot can accurately predict cellular functionality from real gene expression data so that users can see how their gene edits would affect cells almost instantly.


Disruption 2 Avoiding Deadly Disorders

You never want to introduce mistakes when you’re editing. You definitely never want to introduce mistakes when you’re performing genetic edits. With Snapshot, you can highlight potential genetic disorders based on genomic conditions to help users avoid making gene edits that may lead to such disorders.


Disruption 3 Modelling Medicine

Pharmaceutical companies and research groups invest a lot of money in their drug candidates, but, how these drugs might cause side-effects are often unknown. Snapshot models certain drugs, such as painkillers -- like ibuprofen -- and antidepressants -- SSRIs, and their effects on metabolic pathways.


Disruption 4 Powerful and Inexpensive

To find out how genes affect the function of cells used to take a considerable investment. That puts it out of the hands of all but the biggest companies and research institutions. Snapshot, however, can provide a cheap model that allows anyone -- small groups, student labs, individuals, anyone! -- to explore how genes affect cellular functionality without having to buy any expensive equipment.


To learn more about how Snapshot is disrupting genetic engineering, or, rather, how Snapshot can help YOU disrupt genetic engineering, see us at https://www.scarboroughbiotech.com/ or send us an email at support@scarboroughbiotech.com.



Modelling breast cancer in Snapshot

Posted by mcoggins on September 24, 2018 at 5:35 AM Comments comments (0)


With  Snapshot it is easier than ever to explore cancer functionality and to predict reaction activity and metabolic flux from cancer samples. 

In this demonstration I will show you how to model breast cancer from real gene expression data from breast cancer samples. 

Step 1 : Download the breast cancer dataset found on our website: https://www.scarboroughbiotech.com/genome-datasets

Step 2: Open Snapshot 

Step 3: Click the import button and open the breast cancer dataset

Step 4: You will see that code has been loaded in to the code box from the dataset. Now just click Run. 

and that's it! Now you will see that reaction activity and metabolic flux has been predicted! 

Snapshot: Updates and Developments #1

Posted by mcoggins on September 3, 2018 at 4:25 AM Comments comments (0)


Update 1.3.0 is probably the largest update to come to Snapshot so far by adding 18 genes, 8 reactions, and 14 metabolites as well as a host of improvements to metabolite prediction. 



Current model:

  • 73 Genes
  • 38 Reactions
  • 85 Metabolites
  • 3 Cellular Compartments
  • 7 Diseases

An entire new metabolic pathway

This latest update adds the citric acid cycle which is a core pathway in all aerobic oeganisms to release stored energy. 



Improvements to metabolite prediction

Further improvements have been made to predicting metabolite production. In earlier updates metabolite production was based upon whether a reaction were active or not. Now however it is linked directly to gene expression given a much more accurate prediction of the quantity of products and reactants. 

Predicting diseases from patient data using snapshot

Posted by mcoggins on July 24, 2018 at 10:10 AM Comments comments (0)

Introduction


According to Genetic Disorders UK.org there are between 4,000 to 6,000 diagnosed genetic disorders ranging from common disorders such as sickle cell disease (1 in 500 africans) to more rare disorders such as lactate dehydrogenase A deficiency which has a prevelance of only 1 in 1 milion. Given the amount of potential disorders there is a push for more rapid yet accurate diagnosis for patients. 


With Snapshot we have recently released a function called realconv  that can take gene expression data from patients and highlight potential diseases based upon that data. 


Lactate Dehydrogenase Example


Imagine we have a patient whose symptoms include brown urine and rhabdomyolysis (breakdown of skeletal muscle). We obtain a gene expression profile from the patient from which we will see if there are any genetic conditions which maybe associated with the patients symptoms. Then we can put the gene expression data in to snapshot. 


To do this we need to use the realconv function. This function takes gene expression values and will knockout any genes whose expression falls below a certain threashold value determined by the user. This value must be low such that it will turn off any genes whose expressions would be  associated with an enzyme deficiency caused by that gene. In the code below we have set this value to 0.3 where 0 would be a complete knockdown of that gene and 1 would be normal expressions and anything higher would be determined to be overexpression. After we ahve determined the threshold we can start to add in the expressions of each gene from the expression profile. To add these expressions we must first declare the HGNC ID of that gene followed by it's expression on the next line below. 


For example:


6541

0.8 


Here is the code:


knockin 

start:realconv

threshold 

0.3 

21481 

19708

1

30866

0.3

6541

0.1

6535

0.1

9040

1

61

1

66

1

2625

0.4

1122

0.8

1516

0.8

end:realconv

cal 




From the disease table we can see that it has highlighted Exerctional Myglobinuria due to deficiency of HGNC 6535.  





How snapshot can help optimise production of proteins for pharmaceuticals

Posted by mcoggins on July 16, 2018 at 10:55 AM Comments comments (0)

With new cutting edge pharmaceuticals relying heavily on recombinant proteins there has been a big push by bioprocessing companies to optimise and increase production of recombinant proteins. These proteins are typically produced in bacterial cells such as e-coli and for more complex proteins mamalian cell lines such as CHO.


However growing proteins in mamalian cells like CHO is challenging as they require optimal enviromental conditions to even produce a small amount of the desired protein. This is why recently the industry has looke towards genetic engineering to alter cell functionality in order to make these cells overproduce the target protein of interest. However altering a cells genome can be expensive and challenging requriing utting edge laboratories. 


Our genetic engineering program Snapshot which models the genes and reactions within cells could be a powerful tool for companies that want to edit a cells genome to produce proteins of interest. This is because snaptshot has built in functions that highlight genes associated with reactions that produce metabolites in different cellular compartments.


For example if you wanted to find the genes associated with reactions that produce ATP then you would enter max:atp_c (where c denotes the cytoplasm compartment). This would then highlight all genes associated with producing ATP. Likewise if you wanted to minimise production of a certain metabolite that was deemed toxic or redundant then you would enter min:. For example to minimise lactic_acid which is deemed a waste product you would enter min:l_lactic_acid_c. 




Screenshot above showing snapshot highlighting HGNC:9021 and 9020 associated with producing ATP




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