B I O C O M P U T a T I O N

نویسنده

  • JONATHAN SCHUG
چکیده

We now know that approximately 30,000 to 40,000 genes effectively control and regulate the human body. The recent completion of a rough draft of the human genome and the complete sequence of Drosophila melanogaster, Caenorhabditis elegans, Mycobacterium tuberculosis, and numerous other sequencing projects provide a vast amount of genomic data for further refinement and analysis.1 These landmarks in human scientific achievement promise remarkable advances in our understanding of fundamental biological processes. To achieve this goal, we must develop the ability to model, analyze, and predict the effect of the products of specific genes and genetic networks on cell and tissue function.2 Traditional models and simulations of metabolic and cellular control pathways are based on either continuous or discrete dynamics.3–5 However, many important biological systems are hybrid—they involve both discrete and continuous dynamics. At the molecular level, the fundamental process of inhibitor proteins turning off the transcription of genes by RNA polymerase reflects a switch between two continuous processes.6 This is perhaps most clearly manifested in the classic lambda switch system, where we see two biological processes. At the cellular level, we can best describe cell growth and division in a eukaryotic cell as a sequence of four processes, each one a continuous process triggered by a set of conditions.7 At the intercellular level, we can even view cell differentiation as a hybrid system.8 In all of these examples, a hybrid approach that combines elements of discrete and continuous dynamics is necessary to model, analyze, and simulate the system’s richness. To understand how a network of biochemical reactions implements and controls cellular functions and the genetic regulatory apparatus, we must develop a new set of theories, algorithms, and methodologies that combine the two fundamentally different ways of characterizing such systems. We advocate modeling biological systems as stochastic, networked hybrid systems that consist of discrete and continuous components with complex interactions.9–11 Even in many continuous biological systems characterized by differential equations, a hybrid model offers a computationally tractable approach to modeling, analysis, and synthesis. Networks model the inMODELING AND ANALYZING BIOMOLECULAR NETWORKS

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تاریخ انتشار 2009