The Dynamics of Disease – University of Manchester

Continuing to get myself out and about during unemployment, I managed to get on to the dynamics of disease workshop at University of Manchester this week.

The conference covers the application of systems biology and systems approaches to studying disease, and is being organised by Professor Gerold Baier of the Manchester Interdisciplinary Biocentre doctoral training centre.  Gerold has provided a lot of support to me during my Ph.D, particularly in the first year, and I am very grateful that he allowed me to attend the event.

Day 1  was entitled “Systems approaches to Health and Disease” and provided a general introduction to how systems biology is being used in academic research into diseases, as well as pharmaceutical drug discovery programs.

Pedro Mendes of University of Manchester presented a general introduction to modeling biochemical networks, and described the process of moving from annotated genome sequence data to dynamic – models, with metabolic network reconstruction.  The process of building models was described, using Yeast 5 as an example “big model”.  There was some examples of analysis using FBA and MCA to interrogate these models as “static models” for hypothesis generation, and then build rate laws to create dynamic models.

Later on, Jeurgen Pahle gave a talk on stochastic modeling, and demonstrated stochastic modeling using Copasi, and the benefits it provides in observing non-linear dynamic behaviors in models, using the Lotke-Voltaire model as an example, where discreet events in stochastic simulations lead to extinction, which are not observed from averaging in deterministic modeling.

Mike White presented his groups work on NF-Kappa B signaling in cancer, and how a combined experimental and modeling approach has lead to the identification of feedback loops in the network and an understanding of the network architecture.  The modeling work provides hypothesis on how signaling functions as an oscillation in NF-Kappa B activity, and regulation by TNF-alpha can change the amplitude and periodicity of the oscillations, leading to heterogeneity in the signaling response of the population.  The work shows the real benefit of a systems level approach, and understanding the interaction of the components of the network at the cellular level, as a network embedded in many other networks, and also in a cell population level, with cells signaling to each other as a tissue, or an organ and how each cell in a component of a larger system.

Jonathan Swinton presented his work modeling cardiac action potentials for Astra Zeneca’s drug discovery programs.  Again, it was demonstrated how basic mechanistic models can provide a frame work for understanding a system, and then parameter estimation using experimental data can refine the model behavior, providing in vivo relevant simulation tools.  The library size for drug screening programs can be significantly reduced by investigating systems level interactions with the computer, leading to cheaper and faster drug discovery in the future.

In the afternoon, Ed Kent (Mendes group) provided a demonstration of sensitivity analysis and how it can be used to infer control points in metabolic networks, and his web interface for Copasi to run simulations on the Condor distributed computing network to reduce years of computation time to hours, enabling global sensitivity analysis in increasingly large models.

In addition to the speakers, there was a skype call to Joseph Loscalzo of Harvard Medical School, who is applying systems biology as “systems physiology”, attempting to understand the complexity of disease beyond simple cause and effect and Occams Razor, to understanding complex combinations of symptoms, and links between different diseases, such as Crohns disease and type II diabetes, using genome wide association studies, creating what he calls a “Pathophenome”.  He also highlighted the complexity of targeting diseases such as cancer with single drug therapies, where knocking out one interaction in the pathway can be compensated for further down the pathway, or in connected pathways, resulting in the disease returning.  Cheaper whole genome sequencing in the future will possibly enable clinicians to identify multi-point purturbations with combinations of drugs, personalised for the individual disease case.

It was great to be back among the scientific community again.  I couldn’t make day 2 due to travel costs, but I will be headed back for day 3 for “multi-scale modeling and systems dermatology”.