Towards a whole-cell modeling approach for synthetic biology (original) (raw)

A Computational Model of Gene Expression in an Inducible Synthetic Circuit

Biocomputing 2010, 2009

Synthetic biology aims to the rational design of gene circuits with predictable behaviours. Great efforts have been done so far to introduce in the field mathematical models that could facilitate the design of synthetic networks. Here we present a mathematical model of a synthetic gene-circuit with a negative feedback. The closed loop configuration allows the control of transcription by an inducer molecule (IPTG). Escherichia coli bacterial cells were transformed and expression of a fluorescent reporter (GFP) was measured for different inducer levels. Computer model simulations well reproduced the experimental induction data, using a single fitting parameter. Independent genetic components were used to assemble the synthetic circuit. The mathematical model here presented could be useful to predict how changes in these genetic components affect the behaviour of the synthetic circuit.

Modular, rule-based modeling for the design of eukaryotic synthetic gene circuits

Background: The modular design of synthetic gene circuits via composable parts (DNA segments) and pools of signal carriers (molecules such as RNA polymerases and ribosomes) has been successfully applied to bacterial systems. However, eukaryotic cells are becoming a preferential host for new synthetic biology applications. Therefore, an accurate description of the intricate network of reactions that take place inside eukaryotic parts and pools is necessary. Rule-based modeling approaches are increasingly used to obtain compact representations of reaction networks in biological systems. However, this approach is intrinsically non-modular and not suitable per se for the description of composable genetic modules. In contrast, the Model Description Language (MDL) adopted by the modeling tool ProMoT is highly modular and it enables a faithful representation of biological parts and pools. Results: We developed a computational framework for the design of complex (eukaryotic) gene circuits by generating dynamic models of parts and pools via the joint usage of the BioNetGen rule-based modeling approach and MDL. The framework converts the specification of a part (or pool) structure into rules that serve as inputs for BioNetGen to calculate the part's species and reactions. The BioNetGen output is translated into an MDL file that gives a complete description of all the reactions that take place inside the part (or pool) together with a proper interface to connect it to other modules in the circuit. In proof-of-principle applications to eukaryotic Boolean circuits with more than ten genes and more than one thousand reactions, our framework yielded proper representations of the circuits' truth tables. Conclusions: For the model-based design of increasingly complex gene circuits, it is critical to achieve exact and systematic representations of the biological processes with minimal effort. Our computational framework provides such a detailed and intuitive way to design new and complex synthetic gene circuits.

Mathematical Modeling: Bridging the Gap between Concept and Realization in Synthetic Biology

Journal of Biomedicine and Biotechnology, 2010

Mathematical modeling plays an important and often indispensable role in synthetic biology because it serves as a crucial link between the concept and realization of a biological circuit. We review mathematical modeling concepts and methodologies as relevant to synthetic biology, including assumptions that underlie a model, types of modeling frameworks (deterministic and stochastic), and the importance of parameter estimation and optimization in modeling. Additionally we expound mathematical techniques used to analyze a model such as sensitivity analysis and bifurcation analysis, which enable the identification of the conditions that cause a synthetic circuit to behave in a desired manner. We also discuss the role of modeling in phenotype analysis such as metabolic and transcription network analysis and point out some available modeling standards and software. Following this, we present three case studies—a metabolic oscillator, a synthetic counter, and a bottom-up gene regulatory n...

Foundations for the design and implementation of synthetic genetic circuits

Nature Reviews Genetics, 2012

Synthetic gene circuits are designed to program new biological behaviour, dynamics and logic control. For all but the simplest synthetic phenotypes, this requires a structured approach to map the desired functionality to available molecular and cellular parts and processes. In other engineering disciplines, a formalized design process has greatly enhanced the scope and rate of success of projects. When engineering biological systems, a desired function must be achieved in a context that is incompletely known, is influenced by stochastic fluctuations and is capable of rich nonlinear interactions with the engineered circuitry. Here, we review progress in the provision and engineering of libraries of parts and devices, their composition into large systems and the emergence of a formal design process for synthetic biology.

Engineering Gene Circuits: Foundations and Applications

Nanotechnology in Biology and Medicine, 2007

Synthetic biology has emerged as a useful approach to decoding fundamental laws underlying biological control. Recent efforts have produced many exciting systems and generated substantial insights. These progresses highlight the potential of synthetic biology to impact diverse areas including biology, computation, engineering, and medicine. 20.1 Introduction Biological systems often function reliably in diverse environments despite internal or external perturbations. This behavior is often characterized as ''robustness.'' Based on extensive studies over the last several decades, much of this robustness can be attributed to the control of gene expression through complex cellular networks [1-4]. These networks are known to consist of various regulatory modules, including feedback [5] and feed-forward [6] regulation and cell-cell communication [7]. With these basic regulatory modules and motifs, researchers are now constructing artificial networks that mimic nature to gain fundamental biological insight and understanding [8]. In addition, other artificial networks that are engineered with novel functions will serve as building blocks for future practical applications. These efforts form the foundation of the recent emergence of synthetic biology [3,9,10]. These artificial networks are interchangeably called ''synthetic gene circuits'' or ''engineered gene circuits.'' Recent accomplishments in synthetic biology include engineered switches [11-14], oscillators [15,16], logic gates [17-19], metabolic control [20], reengineered translational machinery [21], population control [22] and pattern formation [23] using natural or synthetic [24] cell-cell communication, reengineered viral genome [25], and hierarchically complex circuits built upon smaller, well-characterized

Modeling synthetic gene oscillators

Genetic oscillators have long held the fascination of experimental and theoretical synthetic biologists alike. From an experimental standpoint, the creation of synthetic gene oscillators represents a yardstick by which our ability to engineer synthetic gene circuits can be measured. For theorists, synthetic gene oscillators are a playground in which to test mathematical models for the dynamics of gene regulation. Historically, mathematical models of synthetic gene circuits have varied greatly. Often, the differences are determined by the level of biological detail included within each model, or which approximation scheme is used. In this review, we examine, in detail, how mathematical models of synthetic gene oscillators are derived and the biological processes that affect the dynamics of gene regulation.

Mathematical modeling and synthetic biology

Drug Discovery Today: Disease Models, 2008

Synthetic biology is an engineering discipline that builds on our mechanistic understanding of molecular biology to program microbes to carry out new functions. Such predictable manipulation of a cell requires modeling and experimental techniques to work together. The modeling component of synthetic biology allows one to design biological circuits and analyze its expected behavior. The experimental component merges models with real systems by providing quantitative data and sets of available biological 'parts' that can be used to construct circuits. Sufficient progress has been made in the combined use of modeling and experimental methods, which reinforces the idea of being able to use engineered microbes as a technological platform.

Designing and encoding models for synthetic biology

Journal of The Royal Society Interface, 2009

A key component of any synthetic biology effort is the use of quantitative models. These models and their corresponding simulations allow optimization of a system design, as well as guiding their subsequent analysis. Once a domain mostly reserved for experts, dynamical modelling of gene regulatory and reaction networks has been an area of growth over the last decade. There has been a concomitant increase in the number of software tools and standards, thereby facilitating model exchange and reuse. We give here an overview of the model creation and analysis processes as well as some software tools in common use. Using markup language to encode the model and associated annotation, we describe the mining of components, their integration in relational models, formularization and parametrization. Evaluation of simulation results and validation of the model close the systems biology ‘loop’.

Experimentally Driven Verification of Synthetic Biological Circuits

2015

Abstract—We present a framework that allows us to construct and formally analyze the behavior of synthetic gene circuits from specifications in a high level language used in describing electronic circuits. Our back-end synthesis tool automatically gen-erates genetic-regulatory network (GRN) topology realizing the specifications with assigned biological “parts ” from a database. We describe experimental procedures to acquire characterization data for the assigned parts and construct mathematical models capturing all possible behaviors of the generated GRN. We delineate algorithms to create finite abstractions of these models, and novel analysis techniques inspired from model-checking to verify behavioral specifications using Linear Temporal Logic (LTL) formulae. I.