Modeling human disease in the presence of model and parameter uncertainty: Atherosclerosis A-model
by
David Gomez Cabrero(Clinical Gene Networks, Karolinska Institutet)
→
Europe/Stockholm
RB35 (RB35)
RB35
RB35
Seminar room RB35 (Roslagstullsbacken 35, the SBC house)
Description
Experimental methods have made great progress recently but they are still inadequate to achieve a complete mechanistic understanding of complex diseases. Computational methods can be useful to integrate data from different levels and formulate distinct complex biological processes, but they need yet to overcome three major and related challenges. The first is to move beyond a pure molecular scale and to incorporate several vertical scales in the models. Secondly, it is necessary to assure the robustness of the model against minor changes in parameters, in order to define models consistent with biological systems. Finally, it is necessary to develop clinically relevant models.
We develop a "clinically relevant" computational approach to atherogenesis in the arterial wall based on a multi-scale model. The term "atherosclerosis" was introduced by Marchand to describe the association of fatty degeneration and vessel stiffening (see [1] and [2]). Atherosclerosis is characterized by the accumulation of lipids and fibrous elements in the large arteries; and atherogenesis consists of sub-endothelial accumulations of cholesterol-engorged macrophages, called "foam cells". Atherosclerosis is considered a progressive disease whose most devastating consequences, caused by superimposed thrombosis, are heart attack and stroke (see [3]). Biological experiments reveal a critical period where the disease accelerates rapidly (see [4]). Moreover, experiments reveal that lowering of the lipids is effective in reducing the disease progression provided that such a lipid lowering is delivered before the disease onset but not after. It is yet not clear why there is such a rapid switch in the disease development and why there is a time-dependent effect of lipid lowering.
Our model, named A-model, represents some of the key players (molecules, different cell-types, blood) in the disease development active in the arterial wall. A-model is flexible in terms of the quantitative interactions between the components in the arterial wall. We are interested in those sets of parameters that are consistent consistent with observed experimental data in a mouse model prone for atherosclerosis (see [4]).
In order to search among the parameter space, we use the Particle Swarm Optimization algorithm (PSO) and simulation power. As described by Kennedy and Eberhart (see [5] and [6]), PSO is an adaptive algorithm based on a social environment where a set of particles, called population, are visiting possible "positions" (in our case, sets of parameters) of a given dominion. Each position has a fitness value (and it can be computed). At each iteration particles will move returning stochastically toward the population's best fitness position and its own previous best fitness position. Particles of the population are sharing information of the best areas to search. Fitness function is defined using experimental data and knowledge from experts in the field.
Despite the high-dimensionality of the parameter space and the potential combinatorial complexity in the number of solutions, we find that from SET there are a surprisingly small number of classes of solutions that are consistent with the experimental data; we also find that each class represents an isolated area in the parameter space. The reason is that there are strong implicit correlations within the model which appear to highly constrain the possible behaviors in the system. The small number of possible solutions provided us with an explanation as to why there is a rapid switch in the disease development. Since the model spans several vertical layers we could use available gene expression data for experimental validation at the gene level.
[1] Aschoff L., (1933), Introduction. In: Arteriosclerosis: A survey of problem. Cowdry E.V. (Ed). Macmillan. New York.
[3] Falk E., (2006), Pathogenesis of Atherosclerosis J. Am. Coll. Cardiol. Vol. 47, N. 8, C7-C12.
[4] Skogsberg J, Lundström J, Kovacs A, Nilsson R, Noori P, et al., (2008), Transcriptional Profiling Uncovers a Network of Cholesterol-Responsive Atherosclerosis Target Genes, PLoS Genetics 4(3): e1000036 doi:10.1371/journal.pgen.1000036.
[5] Kennedy, J.,Eberhart, R.C., (1995), Particle Swarm Optimization, Proc. IEEE International Conference on Neural Networks, Piscataway, NJ, USA, pp. 1942-1948.
