Regulatory motif discovery using a population clustering evolutionary algorithm

This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences

A hierarchical model of transcriptional dynamics allows robust estimation of transcription rates in populations of single cells with variable gene copy number

Motivation: cis-regulatory DNA sequence elements, such as enhancers and silencers, function to control the spatial and temporal expression of their target genes. Although the overall levels of gene expression in large cell populations seem to be precisely controlled, transcription of individual genes in single cells is extremely variable in real time. It is, therefore, important to understand how these cis-regulatory elements function to dynamically control transcription at single-cell resolution. Recently, statistical methods have been proposed to back calculate the rates involved in mRNA transcription using parameter estimation of a mathematical model of transcription and translation. However, a major complication in these approaches is that some of the parameters, particularly those corresponding to the gene copy number and transcription rate, cannot be distinguished; therefore, these methods cannot be used when the copy number is unknown. Results: Here, we develop a hierarchical Bayesian model to estimate biokinetic parameters from live cell enhancer–promoter reporter measurements performed on a population of single cells. This allows us to investigate transcriptional dynamics when the copy number is variable across the population. We validate our method using synthetic data and then apply it to quantify the function of two known developmental enhancers in real time and in single cells

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

TinkerCell: Modular CAD Tool for Synthetic Biology

Synthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. An application named TinkerCell has been created in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various C and Python programs that are hosted by TinkerCell via an extensive C and Python API. TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. Because TinkerCell associates parameters and equations in a model with their respective part, parts can be loaded from databases along with their parameters and rate equations. The modular network design can be used to exchange modules as well as test the concept of modularity in biological systems. The flexible modeling framework along with the C and Python API allows TinkerCell to serve as a host to numerous third-party algorithms. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at www.tinkercell.com.Comment: 23 pages, 20 figure

Promoter keyholes enable specific and persistent multi-gene expression programs in primary T cells without genome modification

Non-invasive epigenome editing is a promising strategy for engineering gene expression programs, yet potency, specificity, and persistence remain challenging. Here we show that effective epigenome editing is gated at single-base precision via 'keyhole' sites in endogenous regulatory DNA. Synthetic repressors targeting promoter keyholes can ablate gene expression in up to 99% of primary cells with single-gene specificity and can seamlessly repress multiple genes in combination. Transient exposure of primary T cells to keyhole repressors confers mitotically heritable silencing that persists to the limit of primary cultures in vitro and for at least 4 weeks in vivo, enabling manufacturing of cell products with enhanced therapeutic efficacy. DNA recognition and effector domains can be encoded as separate proteins that reassemble at keyhole sites and function with the same efficiency as single chain effectors, enabling gated control and rapid screening for novel functional domains that modulate endogenous gene expression patterns. Our results provide a powerful and exponentially flexible system for programming gene expression and therapeutic cell products