Recently in Systems biology Category

The blog Lab Rat writes about a paper published in Nature Reviews Microbiology by Michael Kohanski and colleagues at Boston University that proposes the use of synthetic biology techniques to look at systems biology problems, something that Wendell Lim at UCSF has done in systems such as signalling networks and scaffold proteins.

The paper looks at the complexity of drug-target interactions and how a network-based approach, coupled with synthetically assembled combinations of genes introduced into bacteria using phages, could be used to probe how the cellular network behaves when hit by drugs and combinations of them.

From Lab Rat:

“By using synthetic genes to disrupt or alter the proposed antibiotic network novel drug targets could be discovered. If turned into a high-throughput system this would be far more useful than the current screening system which tests for a potential drugs interaction with a target, rather than the ability of this interaction to lead to cell death.”

The added genes might themselves form part of a longer-lasting antibiotic (or a family of them), Lab Rat concludes:

“Using combinations of drugs at lower concentrations, or aiding antibiotics by introducing them along with synthetic genes in bacteriophages allows an increased shelf-life of the drugs that we currently possess as well as providing potential systems to aid the discovery of new antibiotics.”

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The models of biological processes that appear in scientific papers often contain serious errors that make it impossible to use them as is. And it's the system that is to blame.

Catherine Lloyd, who works in Peter Hunter's group at the University of Auckland, arrived at the BioSysBio conference in Cambridge today to argue for scientists to not just publish papers but the executable models that they used to create or explain their results.

The Auckland group has been working closely with Dennis Noble's team at the University of Oxford for many years. Noble pioneered the use of computer models in biology with his work on the electrical signals that move around the heart. Recently that work been assembled into animated models that can guide surgeons on where to operate on a diseased heart.

Although models are central to systems biology, the system for publishing research is not really set up to deal with them. Lloyd, who curates the models held by the Auckland team, said the current publishing process introduces problems. "To publish their research, [scientists] have to translate their model into text and equations for publication," she said.

One answer is to submit the model itself, or at least one that works the same way. Right now, researchers use Matlab, Mathematica and a grab bag of other tools to write theirs. The Auckland proposal is to use a derivative of XML, called not surprisingly CellML, to hold the guts of the model.

Lloyd said one problem with using something like Matlab is that there is a lot of procedural code in the typical model needed just to get it going. What researchers really want is just the the core of the model: the differential equations that replicate a biological system's behaviour.

It would streamline a lot of the work for the team at Auckland. According to Lloyd, of the almost 400 models in the cellml.org repository, only four made it from the textual description to executable model in one go. The source papers for most of the others – the majority, but not all appeared in journal papers – contained typos and other mistakes that meant the model did not behave as expected. Albert Goldbeter gets the award for providing two of the error-free models.

"Sometimes we get errors where we have to contact the model author and for some models we will never be able to access the code," said Lloyd.

In some cases, how universities license IP can cause problems with access to the actual models, even if they are only used for testing a CellML derivative. And sometimes, the model just isn't available, possibly because the original paper and model don't quite agree.

"It is surprising how many researchers 'lose' their code. They just can't find it despite all the years they have worked on it," said Lloyd.

According to Lloyd, some journals are interested in the idea of publishing CellML models alongside papers. One possible incentive for scientists to do it is to provide an additional reference for the model so teams wind up getting two citations for the price of one. Or journals could simply refuse to publish papers based on models that don't turn up with the model itself.

Although publishing a model along with a paper means extra work, it could streamline things as running the CellML version acts as a kind of proofreading process for the underlying equations. Getting it into CellML is another matter, but work is underway on a Matlab to CellML converter and there are already tools such as COR and PCEnv for writing and running CellML models.

Squid's sucker rings point to synthetic biomaterials

"The sucker rings have long been assumed to be made of the sugar-based polymer chitin that comprises the beak of the Humboldt squid and is a common structural material in invertebrates such as arthropods and other mollusks. It was therefore surprising that the researchers could not find any trace of chitin..." - they seem to be made from a protein complex.

Standards and BioBricks

The Scientist looks at the iGEM competition and standards work in the BioBricks Foundation for synthetic biology.

Two scientists talk (briefly) about their systems biology careers

Short profiles and interviews with Malcolm Young, e-Therapeutics, and Hiroaki Kitano, SBI, in Nature Reviews Drug Discovery.

Professor Hans Westerhoff's hypothesis of fragility in biological networks is gradually picking up steam. Science News covered it today and I described it as part of a feature on systems biology at the start of the month for the IET's Engineering & Technology.

Westerhoff's approach is deceptively simple. If you look at all the chemical kinetics model for a cell, you can calculate how robust each reaction by analysing the change in production of a target compound based on how much you reduce the amount of enzyme that supports that step. If you cut enzyme concentration by 1 per cent and the production of chemical barely budges, it's robust.

There is no principle of conservation of robustness in a cell: some simply are better at producing material than others. However, if you invert the numbers and call them the fragility of the step, they always add up to one. In a sense, fragility is always conserved. That provides drug designers with a new set of non-obvious targets. In cancers, for example, many drugs try to hit the effects of an oncogene. But the pathways controlled by those genes are often strengthened in a cancer, so they are actually poor targets. Better to look at a precursor that may have a weak link.

Writing in last week's Nature, Nobel laureate Paul Nurse came down on the side of systems biology but was careful to distance the concept from the 'big biology' tag that the field's critics are attaching to it.

For Nurse, "biology stands at an interesting juncture". Previous advances, he argued were based mostly on molecular biology: "applying the ideas that the gene is the fundamental unit of biological information and that chemistry provides effective mechanistic explanations of biological processes". But he warned:

"...comprehensive understanding of many higher-level biological phenomena remains elusive. Even at the level of the cell, phenomena such as general cellular homeostasis and the maintenance of cell integrity, the generation of spatial and temporal order, inter- and intracellular signalling, cell 'memory' and reproduction are not fully understood."

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