Molecular programming aims to systematically engineer molecular and chemical systems of autonomous function and ever-increasing complexity. A key goal is to develop embedded control circuitry within a chemical system to direct molecular events. Here we show that systems of DNA molecules can be constructed that closely approximate the dynamic behavior of arbitrary systems of coupled chemical reactions. By using strand displacement reactions as a primitive, we construct reaction cascades with effectively unimolecular and bimolecular kinetics. Our construction allows individual reactions to be coupled in arbitrary ways such that reactants can participate in multiple reactions simultaneously, reproducing the desired dynamical properties. Thus arbitrary systems of chemical equations can be compiled into real chemical systems. We illustrate our method on the Lotka-Volterra oscillator, a limit-cycle oscillator, a chaotic system, and systems implementing feedback digital logic and algorithmic behavior. . Whereas the use of mass-action kinetics to describe existing chemical systems is well established, the inverse problem of experimentally implementing a given set of chemical reactions has not been considered in full generality. Here, we ask: Given a set of formal chemical reaction equations, involving formal species X 1 ; X 2 ; …; X n , can we find a set of actual molecules M 1 ; M 2 ; …; M m that interact in an appropriate buffer to approximate the formal system's mass-action kinetics? If this were possible, the formalism of chemical reaction networks (CRNs) could be treated as an effective programming language for the design of complex network behavior (5-9). Unfortunately, a formally expressed system of coupled chemical equations may not have an obvious realization in known chemistry. In a formal system of chemical reactions, a species may participate in multiple reactions, both as a reactant and/or as a product, and these reactions progress at relative rates determined by the corresponding rate constants, all of which imposes formidable constraints on the chemical properties of the species participating in the reactions. For example, it is likely hard to find a physical implementation of arbitrary chemical reaction equations using small molecules, because small molecules have a limited set of reactivities. Thus, formal CRNs may appear to be an unforgiving target for general implementation strategies. Indeed, most experimental work in chemical and biological engineering has started with particular molecular systems-genetic regulatory networks (10), RNA folding and processing (11), metabolic pathways (12), signal transduction pathways (13), cell-free enzyme systems (14, 15), and small molecules (16, 17)-and found ways to modify or rewire the components to achieve particular functions. Attempts to systematically understand what functional behaviors can be obtained by using such components have targeted connections to analog and digital electronic circuits Here we propose a method for compiling an arbitrary CRN into nucleic-acid-based chemistry. Given a formal specification of coupled chemical kinetics, we systematically design DNA molecules implementing an approximation of the system scaled to an appropriate temporal and concentration regime. Formal species are identified with certain DNA strands, whose interactions are mediated by a set of auxiliary DNA complexes. Nonconserving CRNs can be implemented because the auxiliary species implicitly supply energy and mass. Conveniently, the base sequence of nucleic acids can determine reactivity not only through direct hybridization of singlestranded species (29) but also through branch migration and strand displacement reaction pathways (30-32). These powerful reaction primitives have been used previously for designing nucleic-acid-based molecular machines with complex behaviors, such as motors, logic gates, and amplifiers (33-37). Here we use these reaction mechanisms as the basis for the implementation of arbitrary CRNs. Our work advances a systematic approach that aims to provide a general mechanism for implementing a wellspecified class of behaviors. Molecular Primitive: Strand Displacement Cascades Because simple hybridization reactions cannot be cascaded, we use the more flexible strand displacement reaction as a molecular primitive. [We use "strand displacement" as a shorthand for toehold-mediated branch migration and strand displacement A preliminary version of this work appeared as ref. 52.

Cells compute responses to external stimuli using networks of regulatory interactions. A major goal of biology is to understand the dynamics of these complex networks. 1–3 Great simplification

Abstract not found

likely to differ from the final, published version. Peer-reviewed and accepted for publication but not copyedited or typeset; preprint is service Email alerting click heretop right corner of the article or Receive free email alerts when new articles cite this article- sign up in the box at the object identifier (DOIs) and date of initial publication. by PubMed from initial publication. Citations to Advance online articles must include the digital publication). Advance online articles are citable and establish publication priority; they are indexed appeared in the paper journal (edited, typeset versions may be posted when available prior to final Advance online articles have been peer reviewed and accepted for publication but have not yet

Transcription factor binding sites (TFBS) are being discovered at a rapid pace1, 2. We must now begin to turn our attention towards understanding how these sites work in combination to influence gene expression. Quantitative models that accurately predict gene expression from promoter sequence3-5 will be a crucial part of solving this problem. Here we present such a model based on the analysis of synthetic promoter libraries in yeast. Thermodynamic models based only on the equilibrium binding of transcription factors to DNA and to each other captured a large fraction of the variation in expression in every library. Thermodynamic analysis of these libraries uncovered several phenomena in our system, including cooperativity and the effects of weak binding sites. When applied to the genome, a model of repression by Mig1, which was trained on synthetic promoters, predicts a number of Mig1 regulated genes that lack significant Mig1 binding sites in their promoters. The success of the thermodynamic approach suggests that the information encoded by combinations of cis-regulatory sites is interpreted primarily through simple protein-DNA and protein-protein interactions with complicated biochemical reactions, such as nucleosome modifications, being down stream events. Quantitative analyses of synthetic promoter

Abstract: The architecture of gene regulatory networks is reminiscent of electronic circuits. Modular building blocks that respond in a logical way to one or several inputs are connected to perform a variety of complex tasks. Gene circuit engineers have pioneered the construction of artificial gene regulatory networks with the intention to pave the way for the construction of therapeutic gene circuits for next-generation gene therapy approaches. However, due to the lack of a critical amount of eukaryotic cell-compatible gene regulation systems, the field has so far been limited to prokaryotes. Recent development of several mammalian cell-compatible expression control systems laid the foundations for the assembly of transcription control modules that can respond to several inputs. Herein, three approaches to evoke combinatorial transcription control have been followed: (i) construction of artificial promoters with up to three operator sites for regulatory proteins, and (ii) parallel and (iii) serial linking of two gene regulation systems. We have combined tetracycline-, streptogramin-, macrolide-, and butyrolactone transcription control sys-tems to engineer BioLogic gates of the NOT IF-, AND-, NOT

Abstract not found

Abstract Hematopoietic stem cell lineage choices are decided by genetic networks that are turned ON/OFF in a switch-like manner. However, prior to lineage commitment, genes are primed at low expression levels. Understanding the underlying molecular circuitry in terms of how it governs both a primed state and, at the other extreme, a committed state is of relevance not only to hematopoiesis but also to developmental systems in general. We develop a computational model for the hematopoietic erythroid-myeloid lineage decision, which is determined by a genetic switch involving the genes PU.1 and GATA-1. Dynamical models based upon known interactions between these master genes, such as mutual antagonism and autoregulation, fail to make the system bistable, a desired feature for robust lineage determination. We therefore suggest a new mechanism involving a cofactor that is regulated as well as recruited by one of the master genes to bind to the antagonistic partner that is necessary for bistability and hence switch-like behavior. An interesting fallout from this architecture is that suppression of the cofactor through external means can lead to a loss of cooperativity, and hence to a primed state for PU.1 and GATA-1. The PU.1-GATA-1 switch also interacts with another mutually antagonistic pair, C/EBPa-FOG-1. The latter pair inherits the state of its upstream master genes and further reinforces the decision due to several feedback loops, thereby leading to irreversible commitment. The genetic switch, which handles the erythroid-myeloid lineage decision, is an example of a network that implements both a primed and a committed state by regulating cooperativity through recruitment of cofactors. Perturbing the feedback between the master regulators and downstream targets suggests potential reprogramming strategies. The approach points to a framework for lineage commitment studies in general and could aid the search for lineage-determining genes.

Abstract: To understand most cellular processes, one must understand how genetic information is processed. A formidable challenge is the dissection of gene regulatory networks to delineate how eukaryotic cells coordinate and govern patterns of gene expression that ultimately lead to a phenotype. In this paper, we review several approaches for modeling eukaryotic gene regulatory networks and for reverse engineering such networks from experimental observations. Since we are interested in elucidating the transcriptional regulatory mechanisms of colon cancer progression, we use this important biological problem to illustrate various aspects of modeling gene regulation. We discuss four important models: gene networks, transcriptional regulatory systems, Boolean networks, and dynamical Bayesian networks. We review state-of-the-art functional genomics techniques, such as gene expression profiling, cis-regulatory element identification, TF target gene identification, and gene silencing by RNA interference, which can be used to extract information about gene regulation. We can employ this information, in conjunction with appropriately designed reverse engineering algorithms, to construct a computational model of gene regulation that sufficiently predicts experimental observations. In the last part of this review, we focus on the problem of reverse engineering transcriptional regulatory networks by gene perturbations. We mathematically formulate this problem and discuss the role of experimental resolution in our ability to reconstruct accurate models of gene regulation. We conclude, by discussing a promising approach for inferring a transcriptional regulatory system from microarray data obtained by gene perturbations. 1.

Current high-throughput experiments already generate enough data for retrieving the DNA sequence-dependent binding affinities of transcription factors (TF) and other chromosomal proteins throughout the complete genome. However, the reverse task of calculating binding maps in a chromatin context for a given set of concentrations and TF affinities appears to be even more challenging and computationally demanding. The problem can be addressed by considering the DNA sequence as a one-dimensional lattice with units of one or more base pairs. To calculate protein occupancies in chromatin, one needs to consider the competition of TF and histone octamers for binding sites as well as the partial unwrapping of nucleosomal DNA. Here, we consider five different classes of algorithms to compute binding maps that include the binary variable, combinatorial, sequence generating function, transfer matrix and dynamic programming approaches.The calculation time of the binary variable algorithm scales exponen-tially with DNA length, which limits its use to the analysis of very small genomic regions. For regulatory regions with many overlapping binding sites, potentially applicable algorithms reduce either to the transfer matrix or dynamic programming approach. In addition to the recently proposed transfer matrix formalism for TF access to the nucleosomal organized DNA, we develop here a dynamic programming algorithm that accounts for this feature. In the absence of nucleosomes, dynamic programming outperforms the transfer matrix approach, but the latter is faster when nucleosome unwrapping has to be considered. Strategies are discussed that could further facilitate calculations to allow computing genome-wideTF binding maps.