Computers make sense of experiments on human disease

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Computers make sense of experiments on human disease

http://www.eurekalert.org/pub_releases/2008-11/esf-cms111208.php

Mathematical models resolve controversy over nicotine addiction

Increased use of computers to create predictive models of human

disease is likely following a workshop organised by the European

Science Foundation (ESF), which urged for a collaborative effort

between specialists in the field. Human disease research produces an

enormous amount of data from different sources such as animal models,

high throughput genetic screening of human tissue, and in vitro

laboratory experiments. This data operates at different levels and

scales including genes, molecules, cells, tissues and whole organs,

embodying a huge amount of potentially valuable insight that current

computer modelling approaches often fail to exploit properly.

However, significant advances in the modelling of a few specific

diseases, such as multiple sclerosis (MS), have been made. A major

aim of the ESF workshop was thus to generalise such work and create a

more coherent body of expertise across the whole field of

computational disease analysis, according to Albert Compte, co-

convenor of the ESF workshop, from the Computational and

physiological bases of cortical networks laboratory at the Institut

d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS) in

Barcelona. " A workshop like this one was useful in seeing how

advances in other research fields can be used more generally for

disease modelling, " said Compte. " So far, novel modelling approaches

have been confined to a specific disease or a particular level of

description " .

A model might be confined just to the molecular level or the cellular

level for example. The ESF workshop highlighted the benefits that

could be obtained from integrating data from different levels. This

can provide more detailed and flexible models, with greater power to

identify causes of diseases and predict possible cures in future.

However, one potential problem when building sophisticated disease

models operating at different levels is that they can become too

complex, with a lack of sufficient data for any useful analysis. This

can be resolved by selecting a simpler model that corresponds only to

the experimental data that actually exists. Delegates at the workshop

heard how in the case of MS, selection of the model could be tuned to

the data, to make best use of the actual experimental results

obtained in a particular study, as explained by Jesper Tegner,

another co-convenor of the ESF workshop, from the Atherosclerosis

Research Unit at the Karolinska Institute Centre for Molecular

Medicine (CMM) in Stockholm, Sweden.

" There was one exciting presentation on MS, " said Tegner. " The immune

system is clearly central for MS. However, the trick in the case of

MS is to represent different aspects of the immune system according

to the available data.The objective isn't to model the whole immune

system. One interesting level of abstraction was the presentation of

agent-based modelling of MS where individual cells operated as

agents, thus omitting the intracellular machinery. " In other words,

the detailed interior workings of the cells could be ignored in this

case because that would have made the model overcomplicated, with

insufficient data at the different levels to produce any useful

insights.

In other experiments, data about varying levels of gene expression

was obtained, which required very different models with networks of

graphs. These highlighted the patterns of gene expression associated

with a particular disease, such as MS.

Yet another valuable application of computer-based mathematical

disease models lies in studying the phenomenon of addiction to drugs

such as nicotine and helping to reconcile conflicting theories, as

Compte pointed out. " The neurobiology of nicotine addiction is a

hotly debated field. In particular, there are two contending views on

how neurons and their connections in subcortical nuclei are affected

by nicotine. This computer model allows us to reconcile the

apparently contradictory results obtained from in vitro and in vivo

experiments, and thus provides a single theoretical proposal of how

nicotine affects neuronal circuits in the brain and causes addiction,

compatible with most available experimental results. "

Tegner and others at the workshop were confident that a coherent

framework for building multi-level mathematical models on the basis

of available data will lead to better understanding of many diseases

and conditions such as drug addiction. This in turn, could lead to

better therapies.