Evolutionary variance of gene network model via simulated annealing

The traditional approach of molecular biology research was on examining and collecting data on a single gene or a single reaction. However, recently, there has been much interest on the dynamics of gene regulatory networks (Klipp et al., 2005). We applied mathematical approach for modeling of gene network. The models depict the reaction kinetics of the constituent parts and the functions are ultimately made from basic principle of simple expressions derived from Michaelis-Menten enzymatic kinetics, and the functional forms are usually chosen as Hill functions that serve as an approximation for the real molecular dynamics (Klipp et al., 2005). These dynamics depend on many parameters and the parameters strongly influence the behavior of the resulting gene network. Thus, we used simulated annealing algorithm to calculate a high fitness and optimal parameters of the gene network. The simulated annealing algorithm is suitable for calculating many degree of freedom (Tomshine and Kaznessis, 2006), and is the closest analogy with the shifting balance theory of populations (Kirkpatrick et al., 1983). We developed 3 different models that have two genes and experience two different environments, and simulated to describe the behavior of evolutionary gene networks. From simulation, we could obtain a high fitness of each gene network model, and we could indicate how gene network is evolved in evolutionary time from tracks of parameters and a fitness. Also, we analyzed the relations of a high fitness and parameters. We think we can apply to design and optimize other gene network, and these findings are useful to analysis of the evolutionary gene network

Evolutionary variance of gene network model via simulated annealing

The traditional approach of molecular biology research was on examining and collecting data on a single gene or a single reaction. However, recently, there has been much interest on the dynamics of gene regulatory networks (Klipp et al., 2005). We applied mathematical approach for modeling of gene network. The models depict the reaction kinetics of the constituent parts and the functions are ultimately made from basic principle of simple expressions derived from Michaelis-Menten enzymatic kinetics, and the functional forms are usually chosen as Hill functions that serve as an approximation for the real molecular dynamics (Klipp et al., 2005). These dynamics depend on many parameters and the parameters strongly influence the behavior of the resulting gene network. Thus, we used simulated annealing algorithm to calculate a high fitness and optimal parameters of the gene network. The simulated annealing algorithm is suitable for calculating many degree of freedom (Tomshine and Kaznessis, 2006), and is the closest analogy with the shifting balance theory of populations (Kirkpatrick et al., 1983). We developed 3 different models that have two genes and experience two different environments, and simulated to describe the behavior of evolutionary gene networks. From simulation, we could obtain a high fitness of each gene network model, and we could indicate how gene network is evolved in evolutionary time from tracks of parameters and a fitness. Also, we analyzed the relations of a high fitness and parameters. We think we can apply to design and optimize other gene network, and these findings are useful to analysis of the evolutionary gene network.</p

Data from: Evolution of transcription networks in response to temporal fluctuations

Organisms respond to changes in their environment over a wide range of biological and temporal scales. Such phenotypic plasticity can involve developmental, behavioral, physiological, and genetic shifts. The adaptive value of a plastic response is known to depend on the nature of the information that is available to the organism as well as the direct and indirect costs of the plastic response. We modeled the dynamic process of simple gene regulatory networks as they responded to temporal fluctuations in environmental conditions. We simulated the evolution of networks to determine when genes that function solely as transcription factors, with no direct function of their own, are beneficial to the function of the network. When there is perfect information about the environment and there is no timing information to be extracted then there is no advantage to adding pure transcription factor genes to the network. In contrast, when there is either timing information that can be extracted or only indirect information about the current state of the environment then additional transcription factor genes improve the evolved network fitness

Evolution_data

All simulation data: Fitness and parameters and matlab source code

Quadratic Control of Stochastic Hybrid Systems with Renewal Transitions ⋆

We study the quadratic control of a class of stochastic hybrid systems with linear continuous dynamics for which the lengths of time that the system stays in each mode are independent random variables with given probability distribution functions. We derive a condition for finding the optimal feedback policy that minimizes a discounted infinite horizon cost. We show that the optimal cost is the solution to a set of differential equations with unknown boundary conditions. Furthermore, we provide a recursive algorithm for computing the optimal cost and the optimal feedback policy. The applicability of our result is illustrated through a numerical example, motivated by stochastic gene regulation in biology. Key words: Markov renewal processes, semi-Markov processes, optimal control, stochastic hybrid systems, renewal transitions

Mutation in the transcriptional regulator PhoP contributes to avirulence of Mycobacterium tuberculosis H37Ra strain.

Attenuated strains of mycobacteria can be exploited to determine genes essential for their pathogenesis and persistence. To this goal, we sequenced the genome of H37Ra, an attenuated variant of Mycobacterium tuberculosis H37Rv strain. Comparison with H37Rv revealed three unique coding region polymorphisms. One polymorphism was located in the DNA-binding domain of the transcriptional regulator PhoP, causing the protein's diminished DNA-binding capacity. Temporal gene expression profiles showed that several genes with reduced expression in H37Ra were also repressed in an H37Rv phoP knockout strain. At later time points, genes of the dormancy regulon, typically expressed in a state of nonreplicating persistence, were upregulated in H37Ra. Complementation of H37Ra with H37Rv phoP partially restored its persistence in a murine macrophage infection model. Our approach demonstrates the feasibility of identifying minute but distinct differences between isogenic strains and illustrates the consequences of single point mutations on the survival stratagem of M. tuberculosis