Systems biology is not new and can be traced back to the middle of the last century, but at that time it was conceptually complex, and highly theoretical. There are four fundamental aspects of life as stated by Sir Paul Nurse "i) the gene is the basis for heredity, (ii) the cell is the fundamental unit of organisms, (iii) biology is based on chemistry, and (iv) species evolve by natural selection." However, these views of one gene, one cell does not address the question of "What is life?"

It wasn't until the Human Genome Project, and the sequencing of genes did we have information to develop "omics" technology and their application to all areas of biological investigation. Thus transcriptomics, proteomics, and metabolomic and so on evolved. Today, some define systems biology as the "fifth prism"  where "no life form can be imagined without complex systems formed by interacting genes and macromolecules...(that) underlie most biological processes." It promotes the understanding of how these networks in the biological system interact to produce the behavior of that system, how proteins interact with each other inside a cell to respond to hormones or other external stimuli. 

What does this all mean? The intent of this newsletter is not to cover this subject in detail, but to give you the basics as to why systems biology is important in understanding diseases better and the benefits to drug development. Please read the next section for more details.

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The complexity of systems biology can be divided into two categories: 1) Large scale networks such as genomics, proteomics, or metabolomics represented by graphs of thousands of nodes (transcription, proteins, enzymes) and edges (binding, interactions, metabolites), and 2) small subcircuits of network involving few proteins and function as an amplifier, a switch or a logic gate.

The goal is to combine the macro and cellular levels of networks in identifying interactions, the integration of other networks at each level and provide a simpler framework that explains why cells behave the way they do. However, systems biology are non-linear interactions that have "emergent" properties where the function of two proteins system is unique to itself verses the function of three protein system and so forth. For example: "Three proteins connected in a simple negative-feedback loop (A → B → C -| A) can function as an oscillator; two proteins (A → B-|A) can not. Two proteins connected in a simple negative-feedback loop can convert constant inputs into pulsatile outputs; a one-protein loop (A -| A) cannot. So pulse generation emerges at the level of a two-protein system and oscillations emerge at the level of a three-protein system."

The negative -feedback loop was the first emergent property to be known, but as we understand the biological system better other emergent properties were discovered; positive feedback systems where A activates B, and B activates A and feed forward systems where A directly affects C, but A also regulates B, which regulates C. Feed-forward systems can not bea negative feedback system nor toggle switch.

Graphing these complex networks of non-linear functions is conceptually too complex for the human mind. Mathematics and computational modeling helped to capture the complexity of many variables in systems biology. A simple example of this would be Figure 1 (V. A. Likic et el, Adv. Bioinformatics): "A conceptualization of biochemical networks showing genome, transcriptome, proteome, and metabolome-level networks, highlighting their complexity and mutual interdependence."

Figure 1: "Direct conversions of species shown in solid lines, while some possible interactions (not necessarily one-step) are designated in dashed lines."

In understanding and mapping protein-protein interactions, two types of interactions were found, direct and indirect associations between proteins which are highly organized (scale-free) and not random. In a scale-free network, most nodes have few interactions and coexist with a few nodes that are highly connected called hubs that hold the whole network together.

To understand the role of the hubs and how it relates to human diseases, one needs to understand essential genes and disease-related genes. Essential genes needed for early development tend to be associated with hubs and expressed in multiple tissues. Disease-related genes tend not to be associated with hubs and are tissue specific. Hubs are thought to be gatekeepers and responsible for phenotypic outcome.

In the clinical setting, obesity can leads to diabetes and some cancers. If one views human diseases as perturbations of highly interlinked cellular networks, then diseases are highly interconnected and not independent of each other. Therefore, it can be theorized that "molecular defects responsible for one of a pair of diseases can spread along the edges in cellular networks, affecting the activity of related gene products and causing or affecting the outcome of the other disease."  

Scientists have been successful in modeling the behavior of cells in the lab, and now, they have been successful modeling cells inside live mice. Researchers at MIT and Massachusetts General Hospital have developed a new computational model that describes how mice's intestinal cells respond to a natural chemical called tumor necrosis factor (TNF).

"The work demonstrates that systems biology offers a way to get a handle on the complexity of living systems and raises the possibility that it could be used to model cancer and other complex diseases," said Douglas Lauffenburger, head of MIT's Department of Biological Engineering and a senior author of the paper.

"You're not likely to explain most diseases in terms of one genetic deficit or one molecular impairment," said Lauffenburger. "You need to understand how many molecular components, working in concert, give rise to how cells and tissues are formed - either properly or improperly."

In their study, the researchers developed their computational model by treating normal mice with TNF, then determined whether the cells proliferated or died. "They found that cell fate depended on the cell's location in the intestine - cells in the ileum proliferated, while those in the duodenum died."

"In cell culture, you would have gotten one or the other," said Lauffenburger. Demonstrating that environment is an important factor of cell interactions.

Based on this finding, they were able to apply it to dozens of other protein interactions and discovered that cell outcomes depended on quantitative combinations of key signaling pathways when modeled for drug treatment. The researchers tested these models and were accurate.

The systems biology approach can benefit drug development in six (6) ways:

1. Better understanding of diseases
2. Faster discovery of biomarkers and companion diagnostics
3. Improved pre-clinical trials
4. Improved efficacy
5. Smaller clinical trials and reduced drug development cost
6. Drugs available to patients sooner

1. Better understanding of diseases

"Systems biology is fundamentally a study of networks of biological macromolecules. Understanding information flow through those networks and how local perturbations of the network contribute to disease relates directly to more efficient and efficacious drug development. Coupling a systems approach with knowledge of the network will allow one to better identify causal relationships more directly and to predict therapeutic outcomes." said David E. Hill, Associate Director of the Center for Cancer Systems Biology (CCSB) at the Dana-Farber Cancer Institute (DFCI).

Until we understand diseases better, will we be able to find their cures. Systems biology can help scientists to understand the critical protein-protein interactions that are directly and indirectly involved in various diseases. Then they can predict behavior resulting from environmental changes such as the introduction or inhibition of an enzyme and how it affects the pathway and the rest of the interconnected networks.

This approach may take a little longer on the research side, but once we understand the behavior of diseases this will speed up the drug development process on the back end.

2. Faster discovery of biomarkers and companion diagnostics

By understanding the complexity of diseases, scientist will be able to discover biomarkers faster as oppose to trial and error testing. Systems biology can identify subsets of phenotypes, thus truly giving rise to personalized medicine.

Biomarkers automatically lead to companion diagnostic and drug development together. This helps physicians not only diagnose patients by phenotype, but prescribe the right drug for that patient. The trend in the medical industry is already moving towards companion diagnostics with therapeutics.

Scientists at The Children's Hospital of Philadelphia and McGill University believed they had found three SNPs (single nucleotide polymorphisms, single-base changes in DNA sequence that serve as signposts for gene mutations associated with them) in patients with type 1 diabetes using a meta analysis of type 1 genetic data.  

"Our study found SNPs that we had not expected to have any connection to type 1 diabetes," said Dr. Hakon Hakonarson, the study leader and director of the Center for Applied Genomics at the children's hospital, in a release. "The strongest association among the three SNPs was in the region of the LMO7 gene on chromosome 13. We previously associated another member of the LMO gene family with the childhood cancer neuroblastoma. This gene family plays an important role in protein-protein interactions, but it would not have occurred to anyone that it may be active in type 1 diabetes. GWAS (genome-wide association study) continues to turn up surprising biological associations."

3. Improved pre-clinical trials

Researchers at the institute at Virginia Tech who developed the Enteric Immunity Simulator (ENISI) software simulated how a mouse's immune system reacts to Helicobacter pylori infection in the gut. "ENISI is unique because it's specific to the gut, simulating each individual cell rather than creating broad mathematical models," said Kate Wendelsdorf, a Ph.D. student in the genetics, bioinformatics and computational biology program at Virginia Tech. "Thus, it's more faithful to a living system and allows us to simulate a million individual cells, more than any other simulator. It's a powerful tool for understanding interactions between gut pathogens and the mucosal immune system."

By combining computational modeling and a pilot animal study, one can better predict what will happen in mice rather than conducting animal studies to determine efficacy and safety parameters.

4. Improved efficacy

Identifying biomarkers in diseases and then developing drugs that target these biomarkers results in better efficacy and minimal side effects. In addition, computer modeling will predict how cells react with related co-morbidities in helping to predict efficacy.

Side effects will be minimal, since researchers will be able to predict the side effects that may occur, thus eliminating any potential drug path that would likely have major side effects and find another path that would have fewer side effects.

5. Smaller clinical trials and reduced drug development costs

Personalize medicines makes a strong case for the FDA to require fewer homogenous patients than the 1000s of patients enrolled in standard trials of heterogeneous patients. If the number of patients required for all trials are smaller, the trial costs will be significantly less and the time to recruit, conduct the trials and submit results will be significantly shorter.

6. Drugs available to patients sooner

Fewer and fewer drugs are being approved by the FDA today because of major side effects or drugs not meeting their primary endpoints. Drugs are held at a higher standard because we have better drugs (standard of care) on the market. Using the systems biology approach, drugs have a higher probability of being approved because efficacy and safety would not be an issue, even if the time frame for drug development remained the same.

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Researchers  at University of Bradford, England have developed a "smart" drug that will only work when it reaches the site of the tumor thus causes less toxic side effects anywhere else. "The drug uses a feature of solid tumors (matrix metalloproteinase MMP) to trigger an attack on the blood vessels feeding the cancer."

The drug was tested in five types of cancer; breast, colon, lung, sarcoma and prostate in mice and in one study, half of the mice had their tumor shrink to where it was undetectable. They are now planning a Phase I trial.

The scientists chemically modified the anti-cancer drug cholchicine with a short protein tail to make it inactive. Once the drug reaches the tumor site, the MMP enzyme snips off the tail of the drug and becomes active. The drug breaks down the blood vessels in the tumor, thus starving the cancer and halting further growth.

Seventeen Medical Device and Twenty Pharma/Biotech Funding Deals

Funding of all types for both Medical Device and Pharma. Companies represented here are US and European companies. A few US companies are funded by European companies and a few European companies are backed by US investors. As a global economy funding can come from anywhere in the world. 

$0 = No financial terms disclosed. For more information, read more.....

There were 13 medical device acquisitions and four (4) pharma acquisitions with one company rescinding their offer. As mentioned the medical device activity is higher because they are a little behind the pharma acquisitions. Due to slow global economy, the terms of many deals are not being disclosed.

$0 = No financial terms disclosed. For more information, read more.....

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