Inference of genetic networks using linear programming machines: Application of a priori knowledge

IEEE Congress on Evolutionary Computation

Amano

Matsumura

et al. 2010

An S-system model is considered as an ideal model for describing genetic networks. As one of effective techniques for inferring S-system models of genetic networks, the problem decomposition strategy has been proposed. This strategy defines the inference of a genetic network consisting of N genes as N subproblems, each of which is a 2(N + 1)-dimensional function optimization problem. When we try to infer large-scale genetic networks consisting of many genes, however, it is not always easy for function optimization algorithms to solve 2(N + 1)-dimensional problems. In this study, we thus propose a new technique that transforms the 2(N + 1)-dimensional S-system parameter estimation problems into (N + 2)-dimensional problems. The proposed technique reduces the search dimensions of the problems by solving linear programming problems. The transformed problems are then optimized using evolutionary algorithms. Finally, through numerical experiments on an artificial genetic network inference problem, we show that the proposed dimension reduction approach is more than 3 times faster than the problem decomposition approach.

Section: B Resultsmentioning

confidence: 99%

Effective parameter estimation for S-system models using LPMs and evolutionary algorithms

IEEE Congress on Evolutionary Computation

Amano

Matsumura

et al. 2010

“…The design of the above knowledge stems from the biological knowledge that the phosphorylated forms of signaling proteins and receptors can form cascades to transduce extracellular signals to transcription factors [20]. We introduced this knowledge into the BS-LPM inference method using the technique proposed in reference [21]. Thus, we introduced the knowledge above on the basis of the framework for "using a priori knowledge while inferring genetic networks."…”

Section: Methodsmentioning

confidence: 99%

Using A Priori Knowledge after Genetic Network Inference: Integrating Multiple Kinds of Knowledge

Kitazawa

Tokuhisa

et al. 2017

CBIJ

Several researchers have focused on the inference of genetic networks as a process for extracting useful information from gene expression data. Their work has led to the proposal of a number of methods for genetic network inference. Yet the genetic networks inferred by these methods often contain large numbers of false-positive regulations along with the true-positives. One effective way to reduce the number of erroneous regulations is to apply inference methods that use a priori knowledge on the properties of the genetic networks. The existing inference methods adopting this approach generally use a priori knowledge and the observed gene expression data simultaneously to determine whether or not the target genetic network actually contains each of the candidate regulations. In this study, we establish a new framework for "using a priori knowledge after genetic network inference." The framework uses a priori knowledge only to modify the genetic network that has already been inferred by the other inference method. Based on this framework, we propose a new inference method that uses multiple kinds of a priori knowledge about genetic networks. The proposed method effectively combines multiple kinds of knowledge and computes the confidence values of regulations. Here, we confirm the effectiveness of the proposed method by applying it to artificial and actual genetic network inference problems. While only a small improvement is gained from the use of multiple kinds of a priori knowledge, we can improve the performance of many other existing inference methods by combining them with the method we propose here.

“…To infer a protein regulation network, we applied the proteomic quantitative data measured by Western blotting (Figs 1 and S1) into the inference method using LPMs (40,41). The method using the LPMs defines the inference of a biochemical network consisting of N network components into N linear programming problems.…”

Section: Methodsmentioning

confidence: 99%

“…Inference methods have been found useful to identify mutual regulation of genes and proteins from quantitative data in time‐course studies or in the analysis of multiple cellular conditions (37–39). Particularly, the dynamic behaviour of time‐course gene expression can be reasonably analysed by the inference method using linear programming machines (LPMs) with a low computation time (40,41). The inferred network in this study indicated two major pathways from EGFR Y845‐c‐MYC and EGFR Y1068‐STAT3 to regulate specific AP‐1 protein expression during the keratinocyte differentiation.…”

Section: Introductionmentioning

confidence: 99%

An ErbB receptor–mediated AP‐1 regulatory network is modulated by STAT3 and c‐MYC during calcium‐dependent keratinocyte differentiation

Saeki

Nagashima

Experimental Dermatology

et al. 2012

A calcium gradient in skin epidermis is known to regulate epidermal differentiation. In cultures of human epidermal keratinocytes (NHEK), induction of calcium-dependent differentiation is associated with phosphorylation of ErbB receptors, including the epidermal growth factor receptor (EGFR). The activation of EGFR triggers the induction of activator protein 1 (AP-1) proteins necessary for keratinocyte terminal differentiation. Interestingly, an in vitro long-term calcium treatment revealed the activation of different ErbB receptors with different timings, which is consistent with the differential localization of each receptor in the skin layers in vivo. In the current study, the regulatory relationship between ErbB receptor activation and induction of AP-1 proteins in calcium-dependent keratinocyte differentiation was analysed. For this purpose, we used a mathematical method to predict molecular regulations from time-course proteomic data of 30 target components. The predicted network showed that the ErbB receptor might regulate AP-1 protein expression via two pathways: positive regulation by c-MYC and negative regulation by signal transducer and activator of transcription 3 (STAT3) pathways. Experimental validation analysis revealed that ErbB receptor inhibition resulted in defective induction of AP-1 proteins and suppressed terminal differentiation of keratinocytes. Inhibition of STAT3, however, affected expression of a partial set of AP-1 proteins and resulted in premature differentiation. Studies using RNAi to diminish expression of each ErbB receptor and the AP-1 proteins strongly suggested that STAT3 established a balance that maintains the appropriate set of AP-1 proteins and participates in a complex network for regulating keratinocyte differentiation.