Biomolecular Circuits Research Articles

Character-State Reconstruction to Infer Ancestral Protein-Protein Interaction Patterns

Protein-protein interactions are at the core of a plethora of developmental, physiological and biochemical processes. Consequently, insights into the origin and evolutionary dynamics of protein-protein interactions may provide information on the constraints and dynamics of specific biomolecular circuits and their impact on the organismal phenotype. This protocol describes how ancestral protein-protein interaction patterns can be inferred using a set of known protein interactions from phylogenetically informative species. Although this protocol focuses on protein-protein interaction data, character-state reconstructions can in general be performed with other kinds of binary data in the same way.

Design of a biochemical circuit motif for learning linear functions.

Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective.

Enhancing spontaneous stem cell healing (Review).

Adult stem cells are distributed throughout the human body and are responsible to a great extent for the body's ability to maintain and heal itself. Accumulating data since the 1990s regarding stem cells have demonstrated that the beneficial effects of stem cells are not restricted to their ability to differentiate and are more likely due to their ability to release a multitude of molecules. Recent studies indicated that ≤80% of the therapeutic benefit of adult stem cells is manifested by the stem cell released molecules (SRM) rather than the differentiation of the stem cells into mature tissue. Stem cells may release potent combinations of factors that modulate the molecular composition of the cellular milieu to evoke a multitude of responses from neighboring cells. A multitude of pathways are involved in cellular and tissue function and, when the body is in a state of disease or trauma, a multitude of pathways are involved in the underlying mechanisms of that disease or trauma. Therefore, stem cells represent a natural systems-based biological factory for the production and release of a multitude of molecules that interact with the system of biomolecular circuits underlying disease or tissue damage. Currently, efforts are aimed at defining, stimulating, enhancing and harnessing SRM mechanisms, in order to develop systems-based methods for tissue regeneration, develop drugs/biologics or other therapeutics and enhance the release of SRM into the body for natural healing through proper dietary, exercise and other lifestyle strategies.

Sensitivity Analysis of Oscillatory Circuits

The period of oscillation in natural and designed biomolecular oscillator networks can depend on underlying reaction rate parameters. Although there has been work in investigating the sensitivity of period to parameters, understanding this dependence for other oscillators and relations between these parametric sensitivities is generally unclear. Here, we address these issues by applying tools of sensitivity analysis on models of oscillating networks. We compute the period sensitivities in standard electrical circuit models and find that an increase in one sensitivity can be accompanied by a decrease in another sensitivity. Next, we compute sensitivities for a biomolecular circuit design and notice similar trends. In particular, we find that the constraints are also apparent in how the state sensitivities vary for different parts of the oscillation cycle. This analysis presents foundational work towards understanding parametric dependence of oscillation period with applications to biomolecular circuit design.

The Systems Biology of Stem Cell Released Molecules—Based Therapeutics

Most therapeutics are based on the traditional method of reductionism where a clinically defined condition is broken down into a defined biochemical pathway underlying the condition, then a target in the pathway is identified, followed by developing a drug to interact with the target, modifying the target such that the disease is ameliorated. Biology acts as a system, therefore reductionist approaches to developing therapeutics are limited in therapeutic value because disease or traumatized tissue involves multiple underlying pathways, only a part of the pathways underlying the disease is manipulated by the traditional therapeutic. Much data regarding stem cells shows that their beneficial effects are not restricted to their ability to differentiate, but is more likely due in large part to their ability to release a multitude of molecules. Stem cells release potent combinations of factors that modulate the composition of the cellular milieu to evoke a multitude of responses from neighboring cells. Therefore, stem cells represent a natural systems-based biological factory for the production and release of a multitude of molecules that interact with the system of biomolecular circuits underlying an indication. Current research includes efforts to define, stimulate, enhance, and harness stem cell released molecules (SRM) to develop systems-therapeutics.

Retroactivity Controls the Temporal Dynamics of Gene Transcription

Just like in many engineering systems, impedance-like effects, called retroactivity, arise at the interconnection of biomolecular circuits, leading to unexpected changes in a circuit's behavior. In this paper, we provide a combined experimental and theoretical study to characterize the effects of retroactivity on the temporal dynamics of a gene transcription module in vivo. The response of the module to an inducer was measured both in isolation and when the module was connected to downstream clients. The connected module, when compared to the isolated module, responded selectively to the introduction of the inducer versus its withdrawal. Specifically, a "sign-sensitive delay" appeared, in which the connected module displayed a time delay in the response to induction and anticipation in the response to de-induction. The extent of these effects can be made larger by increasing the amounts of downstream clients and/or their binding affinity to the output protein of the module. Our experimental results and mathematical formulas make it possible to predict the extent of the change in the dynamic behavior of a module after interconnection. They can be employed to both recover the predictive power of a modular approach to understand systems or as an additional design tool to shape the temporal behavior of gene transcription.

Synthetic gene circuits for metabolic control: design trade-offs and constraints

A grand challenge in synthetic biology is to push the design of biomolecular circuits from purely genetic constructs towards systems that interface different levels of the cellular machinery, including signalling networks and metabolic pathways. In this paper, we focus on a genetic circuit for feedback regulation of unbranched metabolic pathways. The objective of this feedback system is to dampen the effect of flux perturbations caused by changes in cellular demands or by engineered pathways consuming metabolic intermediates. We consider a mathematical model for a control circuit with an operon architecture, whereby the expression of all pathway enzymes is transcriptionally repressed by the metabolic product. We address the existence and stability of the steady state, the dynamic response of the network under perturbations, and their dependence on common tuneable knobs such as the promoter characteristic and ribosome binding site (RBS) strengths. Our analysis reveals trade-offs between the steady state of the enzymes and the intermediates, together with a separation principle between promoter and RBS design. We show that enzymatic saturation imposes limits on the parameter design space, which must be satisfied to prevent metabolite accumulation and guarantee the stability of the network. The use of promoters with a broad dynamic range and a small leaky expression enlarges the design space. Simulation results with realistic parameter values also suggest that the control circuit can effectively upregulate enzyme production to compensate flux perturbations.

Intracellular Therapy Tool

A synthetic biomolecular circuit identifies abnormal cell states by the integration of multiple endogenous microRNA inputs.

Multi-Input RNAi-Based Logic Circuit for Identification of Specific Cancer Cells

Engineered biological systems that integrate multi-input sensing, sophisticated information processing, and precisely regulated actuation in living cells could be useful in a variety of applications. For example, anticancer therapies could be engineered to detect and respond to complex cellular conditions in individual cells with high specificity. Here, we show a scalable transcriptional/posttranscriptional synthetic regulatory circuit--a cell-type "classifier"--that senses expression levels of a customizable set of endogenous microRNAs and triggers a cellular response only if the expression levels match a predetermined profile of interest. We demonstrate that a HeLa cancer cell classifier selectively identifies HeLa cells and triggers apoptosis without affecting non-HeLa cell types. This approach also provides a general platform for programmed responses to other complex cell states.

Neural network computation with DNA strand displacement cascades

The impressive capabilities of the mammalian brain--ranging from perception, pattern recognition and memory formation to decision making and motor activity control--have inspired their re-creation in a wide range of artificial intelligence systems for applications such as face recognition, anomaly detection, medical diagnosis and robotic vehicle control. Yet before neuron-based brains evolved, complex biomolecular circuits provided individual cells with the 'intelligent' behaviour required for survival. However, the study of how molecules can 'think' has not produced an equal variety of computational models and applications of artificial chemical systems. Although biomolecular systems have been hypothesized to carry out neural-network-like computations in vivo and the synthesis of artificial chemical analogues has been proposed theoretically, experimental work has so far fallen short of fully implementing even a single neuron. Here, building on the richness of DNA computing and strand displacement circuitry, we show how molecular systems can exhibit autonomous brain-like behaviours. Using a simple DNA gate architecture that allows experimental scale-up of multilayer digital circuits, we systematically transform arbitrary linear threshold circuits (an artificial neural network model) into DNA strand displacement cascades that function as small neural networks. Our approach even allows us to implement a Hopfield associative memory with four fully connected artificial neurons that, after training in silico, remembers four single-stranded DNA patterns and recalls the most similar one when presented with an incomplete pattern. Our results suggest that DNA strand displacement cascades could be used to endow autonomous chemical systems with the capability of recognizing patterns of molecular events, making decisions and responding to the environment.

DNA and the brain

The idea that artificial neural networks could be based on molecular components is not new, but making such a system has been difficult. A network of four artificial neurons made from DNA has now been created. See Letter p.368 Before neuron-based brains evolved, complex biomolecular circuits must have endowed individual cells with the intelligent behaviour that ensures survival. But the study of how molecules can 'think' has not yet produced useful molecule-based computational systems that mimic even a single neuron. In a study that straddles the fields of DNA nanotechnology, DNA computing and synthetic biology, Qian et al. use DNA as an engineering material to construct computing circuits that exhibit autonomous brain-like behaviour. The team uses a simple DNA gate architecture to create reaction cascades functioning as a 'Hopfield associative memory', which can be trained to 'remember' DNA patterns and recall the most similar one when presented with an incomplete pattern. The challenge now is to use the strategy to design autonomous chemical systems that can recognize patterns or molecular events, make decisions and respond to the environment.

Factor Graph-Based Biomolecular Circuit Analysis for Designing Forward Error Correcting Biosensors

We previously reported the fabrication and the verification of novel biomolecular transistors where electrical conductivity of a ldquopolyaniline nanowiresrdquo channel is controlled by antigen-antibody interactions. In this paper, we present a simulation framework for analyzing the reliability of biosensor circuits constructed by using these biomolecular transistors. At the core of the proposed framework is a library of electrical circuit models that capture the stochastic interaction between biomolecules and their variability to environmental conditions and experimental protocols. Reliability analysis is then performed by exploiting probabilistic dependencies between multiple circuit elements by using a factor graph-based decoding technique. The proposed computational approach facilitates rapid evaluation of forward error correction (FEC) strategies for biosensors without resorting to painstaking and time-consuming experimental procedures. The analysis presented in this paper shows that an asymmetric FEC biosensor code outperforms a repetition FEC biosensor code which has been proposed for microarray technology. In addition, we also show that the proposed analysis leads to a novel ldquoco-detectionrdquo protocol that could be used for reliable detection of trace quantities of pathogens.