Constructing explanatory process models from biological data and knowledge

Langley, Pat; Oren, Shiran; Shrager, Jeff; Todorovski, Ljupčo; Pohorille, Andrew

doi:10.1016/j.artmed.2006.04.003

Cited by 28 publications

(15 citation statements)

References 10 publications

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“…Another efficient approach formalizes a priori knowledge as partially specified models. Fitting models to data is obtained by means of various techniques, depending on the class of models, that can be discrete (Bay et al, 2003;Zupan et al, 2001;Bryant et al, 2001;Reiser et al, 2001), continuous (Batt et al, 2005;Boyer and Viari, 2003;King et al, 2005) or hybrid (Calzone et al, 2005;Langley et al, 2005). Qualitative reasoning, hybrid system, constraint programming or model-checking allow either to identify a subset of active processes explaining experimental time-series data (Bay et al, 2003;Zupan et al, 2001;Bryant et al, 2001;Reiser et al, 2001) or to correct the models and infer some parameters from data (Batt et al, 2005;Chabrier-Rivier et al, 2004).…”

Section: Systems Biology: Models and Datamentioning

confidence: 99%

Qualitative analysis of the relation between DNA microarray data and behavioral models of regulation networks

et al. 2006

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We introduce a mathematical framework that allows to test the compatibility between differential data and knowledge on genetic and metabolic interactions. Within this framework, a behavioral model is represented by a labeled oriented interaction graph; its predictions can be compared to experimental data. The comparison is qualitative and relies on a system of linear qualitative equations derived from the interaction graph. We show how to partially solve the qualitative system, how to identify incompatibilities between the model and the data, and how to detect competitions in the biological processes that are modeled. This approach can be used for the analysis of transcriptomic, metabolic or proteomic data.

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Section: Systems Biology: Models and Datamentioning

confidence: 99%

Qualitative analysis of the relation between DNA microarray data and behavioral models of regulation networks

et al. 2006

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“…IPM was successfully applied in the domain of biochemical kinetics [29 ] constructing a network model of glycolysis from measured data [30] and PBDK. Some details on this case study are given in Figure 4.…”

Section: Figurementioning

confidence: 99%

“…Figure 4b defines the generic processes corresponding to each of the above classes of reactions, as well as the process of flux combination which combines the effects of different reactions on a single compound. Figure 4d gives an excerpt from a PBM for glycolysis, consisting of three specific processes: two of The use of inductive process modeling (IPM) [28 ] in the domain of biochemical kinetics [29 ], addressing the task of modeling glycolysis from measured data [30]. these are irreversible reactions and one is a flux combination.…”

Section: Figurementioning

confidence: 99%

Equation discovery for systems biology: finding the structure and dynamics of biological networks from time course data

Džeroski

Todorovski

2008

Current Opinion in Biotechnology

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“…Equation discovery is such a technique that tries to automate the discovery of equations from measured data [13,21]. This technique is closely related to inductive process modelling [12,4] in which models are automatically constructed drawing heavily on system knowledge encoded in, for example, a library of candidate model structures or substructures. Much progress has been made in this field of research, especially on how to incorporate domain knowledge into the algorithms, e.g.…”

Section: Introductionmentioning

confidence: 99%

Incorporating model identifiability into equation discovery of ODE systems

Pauw

Baets

2008

Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation

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Equation discovery is a machine learning technique that tries to automate the discovery of equations from measured data. In this contribution an equation discovery system based on genetic programming was developed in order to generate mechanistic models for systems described by ordinary differential equations. A problem often encountered with automatic model generation is that overly complex models are generated that "overfit" the measured data. This issue was addressed by incorporating a model identifiability measure (expressing which fraction of the model parameters can be given a unique value given the available data) into the fitness function of the individuals. Using noisy artificially generated data for a river water quality example case, it was shown that the developed system was able to generate model equations that fitted the data well and were also fully identifiable. Correct model equations were generated when starting from a model with minimum prior knowledge but also when starting from an overly complex model. As such, it was demonstrated that the developed equation discovery system is able to generate models with optimal complexity with regard to the available data

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Constructing explanatory process models from biological data and knowledge

Cited by 28 publications

References 10 publications

Qualitative analysis of the relation between DNA microarray data and behavioral models of regulation networks

Qualitative analysis of the relation between DNA microarray data and behavioral models of regulation networks

Equation discovery for systems biology: finding the structure and dynamics of biological networks from time course data

Incorporating model identifiability into equation discovery of ODE systems

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