Model-Driven Designs of an Oscillating Gene Network

ABSTRACT The current rapid expansion of biological knowledge offers a great opportunity to rationally engineer biological systems that respond to signals such as light and chemical inducers by producing specific proteins. Turning on and off the production of proteins on demand holds great promise for creating significant biotechnological and biomedical applications. With successful stories already registered, the challenge still lies with rationally engineering gene regulatory networks which, like electronic circuits, sense inputs and generate desired outputs. From the literature, we have found kinetic and thermodynamic information describing the molecular components and interactions of the transcriptionally repressing lac, tet, and ara operons. Connecting these components in a model gene network, we determine how to change the kinetic parameters to make this normally nonperiodic system one which has well-defined oscillations. Simulating the designed lac-tet-ara gene network using a hybrid stochastic-discrete and stochastic-continuous algorithm, we seek to elucidate the relationship between the strength and type of specific connections in the gene network and the oscillatory nature of the protein product. Modeling the molecular components of the gene network allows the simulation to capture the dynamics of the real biological system. Analyzing the effect of modifications at this level provides the ability to predict how changes to experimental systems will alter the network behavior, while saving the time and expense of trial and error experimental modifications

Bistability versus Bimodal Distributions in Gene Regulatory Processes from Population Balance

In recent times, stochastic treatments of gene regulatory processes have appeared in the literature in which a cell exposed to a signaling molecule in its environment triggers the synthesis of a specific protein through a network of intracellular reactions. The stochastic nature of this process leads to a distribution of protein levels in a population of cells as determined by a Fokker-Planck equation. Often instability occurs as a consequence of two (stable) steady state protein levels, one at the low end representing the “off” state, and the other at the high end representing the “on” state for a given concentration of the signaling molecule within a suitable range. A consequence of such bistability has been the appearance of bimodal distributions indicating two different populations, one in the “off” state and the other in the “on” state. The bimodal distribution can come about from stochastic analysis of a single cell. However, the concerted action of the population altering the extracellular concentration in the environment of individual cells and hence their behavior can only be accomplished by an appropriate population balance model which accounts for the reciprocal effects of interaction between the population and its environment. In this study, we show how to formulate a population balance model in which stochastic gene expression in individual cells is incorporated. Interestingly, the simulation of the model shows that bistability is neither sufficient nor necessary for bimodal distributions in a population. The original notion of linking bistability with bimodal distribution from single cell stochastic model is therefore only a special consequence of a population balance model

OptCircuit: An optimization based method for computational design of genetic circuits

<p>Abstract</p> <p>Background</p> <p>Recent years has witnessed an increasing number of studies on constructing simple synthetic genetic circuits that exhibit desired properties such as oscillatory behavior, inducer specific activation/repression, etc. It has been widely acknowledged that that task of building circuits to meet multiple inducer-specific requirements is a challenging one. This is because of the incomplete description of component interactions compounded by the fact that the number of ways in which one can chose and interconnect components, increases exponentially with the number of components.</p> <p>Results</p> <p>In this paper we introduce OptCircuit, an optimization based framework that automatically identifies the circuit components from a list and connectivity that brings about the desired functionality. Multiple literature sources are used to compile a comprehensive compilation of kinetic descriptions of promoter-protein pairs. The dynamics that govern the interactions between the elements of the genetic circuit are currently modeled using deterministic ordinary differential equations but the framework is general enough to accommodate stochastic simulations. The desired circuit response is abstracted as the maximization/minimization of an appropriately constructed objective function. Computational results for a toggle switch example demonstrate the ability of the framework to generate the complete list of circuit designs of varying complexity that exhibit the desired response. Designs identified for a genetic decoder highlight the ability of OptCircuit to suggest circuit configurations that go beyond the ones compatible with digital logic-based design principles. Finally, the results obtained from the concentration band detector example demonstrate the ability of OptCircuit to design circuits whose responses are contingent on the level of external inducer as well as pinpoint parameters for modification to rectify an existing (non-functional) biological circuit and restore functionality.</p> <p>Conclusion</p> <p>Our results demonstrate that OptCircuit framework can serve as a design platform to aid in the construction and finetuning of integrated biological circuits.</p

Optimization in computational systems biology

Optimization aims to make a system or design as effective or functional as possible. Mathematical optimization methods are widely used in engineering, economics and science. This commentary is focused on applications of mathematical optimization in computational systems biology. Examples are given where optimization methods are used for topics ranging from model building and optimal experimental design to metabolic engineering and synthetic biology. Finally, several perspectives for future research are outlined

Design Constraints on a Synthetic Metabolism

A metabolism is a complex network of chemical reactions that converts sources of energy and chemical elements into biomass and other molecules. To design a metabolism from scratch and to implement it in a synthetic genome is almost within technological reach. Ideally, a synthetic metabolism should be able to synthesize a desired spectrum of molecules at a high rate, from multiple different nutrients, while using few chemical reactions, and producing little or no waste. Not all of these properties are achievable simultaneously. We here use a recently developed technique to create random metabolic networks with pre-specified properties to quantify trade-offs between these and other properties. We find that for every additional molecule to be synthesized a network needs on average three additional reactions. For every additional carbon source to be utilized, it needs on average two additional reactions. Networks able to synthesize 20 biomass molecules from each of 20 alternative sole carbon sources need to have at least 260 reactions. This number increases to 518 reactions for networks that can synthesize more than 60 molecules from each of 80 carbon sources. The maximally achievable rate of biosynthesis decreases by approximately 5 percent for every additional molecule to be synthesized. Biochemically related molecules can be synthesized at higher rates, because their synthesis produces less waste. Overall, the variables we study can explain 87 percent of variation in network size and 84 percent of the variation in synthesis rate. The constraints we identify prescribe broad boundary conditions that can help to guide synthetic metabolism design

Conformational biosensors reveal GPCR signalling from endosomes

A long-held tenet of molecular pharmacology is that canonical signal transduction mediated by G-protein-coupled receptor (GPCR) coupling to heterotrimeric G proteins is confined to the plasma membrane. Evidence supporting this traditional view is based on analytical methods that provide limited or non-subcellular resolution(1). It has been subsequently proposed that signalling by internalized GPCR is restricted to G-protein-independent mechanisms such as scaffolding by arrestins(2,3), or GPCR activation elicits a discrete form of persistent G protein activation(4–9), or that internalized GPCR can indeed contribute to the acute G protein-mediated response(10). Evidence supporting these various latter hypotheses is indirect or subject to alternate interpretation, and it remains unknown if endosome-localized GPCR are even present in an active form. Here we describe the application of conformation-specific single domain antibodies (nanobodies) to directly probe activation of the β(2)-adrenoceptor, a prototypical GPCR(11), and its cognate G protein, G(s) (ref. 12) in living mammalian cells. We show that the adrenergic agonist isoprenaline promotes receptor and G protein activation in the plasma membrane as expected, but also in the early endosome membrane; and that internalized receptors contribute to the overall cellular cyclic AMP response within several minutes after agonist application. These findings provide direct support for the hypothesis that canonical GPCR signalling occurs from endosomes as well as the plasma membrane, and suggest a versatile strategy for probing dynamic conformational change in vivo