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. 2011 Sep-Oct;8(5):1170-82.
doi: 10.1109/TCBB.2011.18.

A novel knowledge-driven systems biology approach for phenotype prediction upon genetic intervention

Rui Chang  1 , Robert Shoemaker, Wei Wang
Affiliations

A novel knowledge-driven systems biology approach for phenotype prediction upon genetic intervention

Rui Chang et al. IEEE/ACM Trans Comput Biol Bioinform. 2011 Sep-Oct.

Abstract

Deciphering the biological networks underlying complex phenotypic traits, e.g., human disease is undoubtedly crucial to understand the underlying molecular mechanisms and to develop effective therapeutics. Due to the network complexity and the relatively small number of available experiments, data-driven modeling is a great challenge for deducing the functions of genes/proteins in the network and in phenotype formation. We propose a novel knowledge-driven systems biology method that utilizes qualitative knowledge to construct a Dynamic Bayesian network (DBN) to represent the biological network underlying a specific phenotype. Edges in this network depict physical interactions between genes and/or proteins. A qualitative knowledge model first translates typical molecular interactions into constraints when resolving the DBN structure and parameters. Therefore, the uncertainty of the network is restricted to a subset of models which are consistent with the qualitative knowledge. All models satisfying the constraints are considered as candidates for the underlying network. These consistent models are used to perform quantitative inference. By in silico inference, we can predict phenotypic traits upon genetic interventions and perturbing in the network. We applied our method to analyze the puzzling mechanism of breast cancer cell proliferation network and we accurately predicted cancer cell growth rate upon manipulating (anti)cancerous marker genes/proteins.

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Figures

Fig. 1
Fig. 1
Dynamic Bayesian Example. (a) DBN example. (b) 2TBN.
Fig. 2
Fig. 2
Demonstration and pipeline of our algorithm.
Fig. 3
Fig. 3
An example of applying the inequality constraints and Monte Carlo method to draw samples in joint probability space and conditional probability space. (a) Samples in Joint Probability Space. (b) Samples in Conditional Probability Space.
Fig. 4
Fig. 4
Mammary Cell Proliferation Network. Blue arrow means activation and red arrow means inhibition. Cytokine TGFβ inhibits cell growth promoter, c-MYC. In additional, c-MYC promotes cell proliferation by repressing several cell growth suppressor proteins, p15, p21. TGFβ elevates activity of three cyclin-dependent kinase’s inhibitor: p15, p21 and p27. p15, p21 and p27 inhibit the complex formation between cyclinD and CDK4,6 and p27, p21 further prevent cyclinE-CDK2’s activation. TGFβ elevates expression of CDK4/6-specific inhibitor p15. p27 binds to CDK4,6 to form a complex, meanwhile, p27 is released from this protein complex under the presence of p15. p15 indirectly stimulates the surge of p27. CyclinD1 and CDKs 4,6 form a complex which drives the cell proliferation in combination with complex formed by cyclinE and CDK2. Besides TGFβ pathway, hyperactive Ras signaling regulates cell developments and promotes cell growth. (a) Dynamic Bayesian Network. (b) 2-Time-Slice Bayesian Network.
Fig. 5
Fig. 5
The parameters in the dynamic Bayesian network of mammary cell proliferation program are denoted by joint probability tables of child nodes and their parents. The joint condition probability is listed in the rightmost column of each table. The secondary rightmost column denotes the child node and its left columns indicates the parent nodes.
Fig. 6
Fig. 6
Prediction on Cell Proliferation Efficiency with Interfered TGFβ and cyclinD1 in Breast Cancer. (a) Prediction on Cell Proliferation in MCF-7 cell. (b) Correlation between predictions and experiments. (c) Prediction on Cell Proliferation in three breast normal and cancer cell. (d) Correlation between predictions and experiments.
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