Model annotation for synthetic biology: automating model to nucleotide sequence conversion.

MOTIVATION: The need for the automated computational design of genetic circuits is becoming increasingly apparent with the advent of ever more complex and ambitious synthetic biology projects. Currently, most circuits are designed through the assembly of models of individual parts such as promoters, ribosome binding sites and coding sequences. These low level models are combined to produce a dynamic model of a larger device that exhibits a desired behaviour. The larger model then acts as a blueprint for physical implementation at the DNA level. However, the conversion of models of complex genetic circuits into DNA sequences is a non-trivial undertaking due to the complexity of mapping the model parts to their physical manifestation. Automating this process is further hampered by the lack of computationally tractable information in most models. RESULTS: We describe a method for automatically generating DNA sequences from dynamic models implemented in CellML and Systems Biology Markup Language (SBML). We also identify the metadata needed to annotate models to facilitate automated conversion, and propose and demonstrate a method for the markup of these models using RDF. Our algorithm has been implemented in a software tool called MoSeC. AVAILABILITY: The software is available from the authors' web site http://research.ncl.ac.uk/synthetic_biology/downloads.html

Annotations for Rule-Based Models

The chapter reviews the syntax to store machine-readable annotations and describes the mapping between rule-based modelling entities (e.g., agents and rules) and these annotations. In particular, we review an annotation framework and the associated guidelines for annotating rule-based models of molecular interactions, encoded in the commonly used Kappa and BioNetGen languages, and present prototypes that can be used to extract and query the annotations. An ontology is used to annotate models and facilitate their description

In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as “The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention,” have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients’ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern

Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case

Background: Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized. Results: We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior. Conclusion: The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.Research in this area is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1-R). Yadira Boada thanks grant FPI/2013-3242 of Universitat Politecnica de Valencia; Gilberto Reynoso-Meza gratefully acknowledges the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) for the development of this work. We are grateful to Alejandra Gonzalez-Bosca for her collaboration on this topic while doing her Bachelor thesis, and to Dr. Jose Luis Pitarch from Universidad de Valladolid for his advise in algorithmic implementations and for proof reading the manuscript.Boada Acosta, YF.; Reynoso Meza, G.; Picó Marco, JA.; Vignoni, A. (2016). Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case. BMC Systems Biology. 10:1-19. https://doi.org/10.1186/s12918-016-0269-0S11910ERASynBio. Next steps for european synthetic biology: a strategic vision from erasynbio. 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Multiscale modeling of intracranial aneurysms: cell signaling, hemodynamics, and remodeling.

The genesis, growth, and rupture of intracranial aneurysms (IAs) involve physics at the molecular, cellular, blood vessel, and organ levels that occur over time scales ranging from seconds to years. Comprehensive mathematical modeling of IAs, therefore, requires the description and integration of events across length and time scales that span many orders of magnitude. In this letter, we outline a strategy for mulstiscale modeling of IAs that involves the construction of individual models at each relevant scale and their subsequent combination into an integrative model that captures the overall complexity of IA development. An example of the approach is provided using three models operating at different length and time scales: 1) shear stress induced nitric oxide production; 2) smooth muscle cell apoptosis; and 3) fluid-structure-growth modeling. A computational framework for combining them is presented. We conclude with a discussion of the advantages and challenges of the approach

Constraint-based genetic compilation

Synthetic biology aims at facilitating the design of new organisms via the standardization of biological parts and following engineering principles. We present atgc (Assistant To Genetic Compilation), a software tool that automatically builds a functional sequence of DNA from a minimal set of requirements. Through a simple language, the user provides in-house knowledge about their construct (e.g. relative placement of parts, number of restriction enzymes). atgc combines information from established biology, user knowledge and bioinformatics databases, and maps the problem to a constraint satisfaction setting. The solution is a functional DNA sequence ready to be assembled and transferred to a target organism