A control theoretic three timescale model for analyzing energy management in mammalian cancer cells.

Abhijit Dasgupta,Nirmalya Chowdhury,Abhisek Bakshi,Rajat K De

doi:10.1016/j.csbj.2020.12.019

Abstract

Interaction among different pathways, such as metabolic, signaling and gene regulatory networks, of cellular system is responsible to maintain homeostasis in a mammalian cell. Malfunctioning of this cooperation may lead to many complex diseases, such as cancer and type 2 diabetes. Timescale differences among these pathways make their integration a daunting task. Metabolic, signaling and gene regulatory networks have three different timescales, such as, ultrafast, fast and slow respectively. The article deals with this problem by developing a support vector regression (SVR) based three timescale model with the application of genetic algorithm based nonlinear controller. The proposed model can successfully capture the nonlinear transient dynamics and regulations of such integrated biochemical pathway under consideration. Besides, the model is quite capable of predicting the effects of certain drug targets for many types of complex diseases. Here, energy and cell proliferation management of mammalian cancer cells have been explored and analyzed with the help of the proposed novel approach. Previous investigations including in silico/in vivo/in vitro experiments have validated the results (the regulations of glucose transporter 1 (glut1), hexokinase (HK), and hypoxia-inducible factor-1 (HIF-1 ) among others, and the switching of pyruvate kinase (M2 isoform) between dimer and tetramer) generated by this model proving its effectiveness. Subsequently, the model predicts the effects of six selected drug targets, such as, the deactivation of transketolase and glucose-6-phosphate isomerase among others, in the case of mammalian malignant cells in terms of growth, proliferation, fermentation, and energy supply in the form of adenosine triphosphate (ATP).

Highlights

Cellular decision making and responses are orchestrated by a set of complex biochemical pathways/networks
We develop a three timescale multiple input and multiple output (MIMO) model based on support vector regression (SVR) and genetic algorithm based controller to simulate the dynamic behavior of integrated signaling, metabolic and gene regulatory networks responsible for mammalian carbon metabolism in normal and cancer cells
We have monitored the concentrations of key molecules during simulation of the proposed three timescale state space model for normal as well as perturbed integrated biochemical pathways related to carbon metabolism

Summary

Introduction

Cellular decision making and responses are orchestrated by a set of complex biochemical pathways/networks. Biochemical pathways/networks can be categorized as metabolic pathways, gene regulatory networks (GRNs), and signaling pathways. A metabolic pathway is a coherent set of biochemical reactions catalyzed by a number of enzymes. It helps a living organism to transform an initial (source) compound into a final (target) compound and energy. Fundamental information processing and control mechanisms in a cell are performed by GRNs. Regulatory genes code for proteins that activate or inhibit the expression of other genes.

Objectives

Methods

Results

Conclusion