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In this paper we propose an extension of a systems/synthetic biology modelling framework based on P systems that explicitly includes modularity. Modularisation in cellular systems can be produced by chemical specificity, spatial localisation and/or temporal modulation within cellular compartments. The first two of these modularisation features, the focus of this paper, can be easily specified and analysed in P systems using sets of rewriting rules to describe chemical specificity and membranes to represent spatial localisation. Our methodology enables the assembly of cell systems biology models by combining modules which represent functional subsystems. A case study consisting of a bacterial colony system is presented to illustrate our approach.

In this paper, we present general methods that can be used to explore the information processing potential of a medium composed of oscillating (self-exciting) droplets. Networks of Belousov–Zhabotinsky (BZ) droplets seem especially interesting as chemical reaction-diffusion computers because their time evolution is qualitatively similar to neural network activity. Moreover, such networks can be self-generated in microfluidic reactors. However, it is hard to track and to understand the function performed by a medium composed of droplets due to its complex dynamics. Corresponding to recurrent neural networks, the flow of excitations in a network of droplets is not limited to a single direction and spreads throughout the whole medium. In this work, we analyze the operation performed by droplet systems by monitoring the information flow. This is achieved by measuring mutual information and time delayed mutual information of the discretized time evolution of individual droplets. To link the model with reality, we use experimental results to estimate the parameters of droplet interactions. We exemplarily investigate an evolutionary generated droplet structure that operates as a NOR gate. The presented methods can be applied to networks composed of at least hundreds of droplets.

Rapid progress of experimental biology has provided a huge flow of quantitative data, which can be analyzed and understood only through the application of advanced techniques recently developed in theoretical sciences. On the other hand, synthetic biology enabled us to engineer biological models with reduced complexity. In this review we discuss that a multidisciplinary approach between this sciences can lead to deeper understanding of the underlying mechanisms behind complex processes in biology. Following the mini symposia "Noise and oscillations in biological systems" on Physcon 2011 we have collected different research examples from theoretical modeling, experimental and synthetic biology.

One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

In this contribution, a design of a synthetic calibration genetic circuit to characterize the relative strength of different sensing promoters is proposed and its specifications and performance are analyzed via an effective mathematical model. Our calibrator device possesses certain novel and useful features like modularity (and thus the possibility of being used in many different biological contexts), simplicity, being based on a single cell, high sensitivity and fast response. To uncover the critical model parameters and the corresponding parameter domain at which the calibrator performance will be optimal, a sensitivity analysis of the model parameters was carried out over a given range of sensing protein concentrations (acting as input). Our analysis suggests that the half saturation constants for repression, sensing and difference in binding cooperativity (Hill coefficients) for repression are the key to the performance of the proposed device. They furthermore are determinant for the sensing speed of the device, showing that it is possible to produce detectable differences in the repression protein concentrations and in turn in the corresponding fluorescence in less than two hours. This analysis paves the way for the design, experimental construction and validation of a new family of functional genetic circuits for the purpose of calibrating promoters.

Nanject: Drug Delivery Cancer Cells Detection.

Potential Thrills and Perils of Synthetic Biology.

A Novel Technology to Protect from Airborne Viruses.

Creating A New, Safer Generation of Hyaluronic Acid for Pharmaceutical Applications.

The Benefits of Refractive Index Detection for UHPLC.

China's Synthetic Biology Research.

Synthetic Biology in Japan.

Synthetic Biology in Singapore.

The UK Synthetic Biology Scene.

Indian Pharma Industry: Trends, Predictions and Challenges.

RFID Smart Sensing.

Rational Design for the Next Generation of Vaccines.

Is Aging a Risk Factor for Cancer?

China's Synthetic Biology Research.

Ethical Responsibility to Manage Incidental Findings in Brain Imaging Research.

Translating Research at SingHealth: Cardiometabollic Diseases.

Universities: A Treasure Trove of Technologies.

The Increasing Need for Bioinformatics in Life Science and Biotechnology Research.

Site Mapping the Innovative Way.

New Antifungals or Old?

SINGAPORE – Intelligent Sensor Informs You to Change a Diaper via SMS

JAPAN – Tokyo Institute of Technology research: Key genetic event underlying fin-to-limb evolution

ISRAEL – Independent Results Show that BiondVax's Universal Flu Vaccine Administered in a Trial 3 Years ago Improves Immunogenicity against Current Flu H3N2 Epidemic

UNITED KINGDOM – Imperial Innovations Launches Orthonika: A Novel Knee Meniscus Replacement

UNITED KINGDOM – New Vaccine For Chlamydia to Use Synthetic Biology

CANADA – Aeterna Zentaris Announces Data and Safety Monitoring Board Scheduled to Complete Second Interim Analysis of the ZoptEC Phase 3 Trial in Endometrial Cancer in Early October

UNITED STATES – NueMD Launches Free ICD-10 Training Tool Ahead of October 1 Deadline

UNITED STATES – A new hope for Moderate and Severe Dementia: Upsher-Smith receives FDA approval for generic version of Namenda (Memantine HCL) Tablets

UNITED STATES – FDA Approves U.S. Product Labeling Update for Sprycel^® (dasatinib) to Include Five-Year First-Line and Seven-Year Second-Line Efficacy and Safety Data in Chronic Myeloid Leukemia in Chronic Phase

The future of pharma: Digital, personalized, and dynamic.

Using real world evidence in the development of new medicine.

Hunting for new drugs to treat mental illness.

Clean cow starts now.

China-Germany sci-tech cooperation continue to bring more opportunities.

Study reveals mechanism behind ginger’s warming effect on body.

Doctors in Guangzhou conduct 5G-assisted remote ultrasound scan.

China proposes major changes to pharmaceutical regulations.

Hangzhou attracts RMB1.5 billion investments in bio-pharmaceutical projects.

Genetic testing encouraged to prevent ovarian cancer.

New technology enables large-scale production of artemisinin for malaria.

Shanghai wants to develop top health innovation zone.

New synthetic biology research center launched in Shanghai.

Chinese scientists suggest new thoughts in lupus treatment.

We present an exact algorithm, based on techniques from the field of Model Checking, for finding control policies for Boolean Networks (BN) with control nodes. Given a BN, a set of starting states, I, a set of goal states, F, and a target time, t, our algorithm automatically finds a sequence of control signals that deterministically drives the BN from I to F at, or before time t, or else guarantees that no such policy exists. Despite recent hardness-results for finding control policies for BNs, we show that, in practice, our algorithm runs in seconds to minutes on over 13,400 BNs of varying sizes and topologies, including a BN model of embryogenesis in Drosophila melanogaster with 15,360 Boolean variables. We then extend our method to automatically identify a set of Boolean transfer functions that reproduce the qualitative behavior of gene regulatory networks. Specifically, we automatically learn a BN model of D. melanogaster embryogenesis in 5.3 seconds, from a space containing 6.9 × 10¹⁰ possible models.

Synthetic biology is a rapidly emerging discipline with long-term ramifications that range from single-molecule detection within cells to the creation of synthetic genomes and novel life forms. Truly phenomenal results have been obtained by pioneering groups — for instance, the combinatorial synthesis of genetic networks, genome synthesis using BioBricks, and hybridization chain reaction (HCR), in which stable DNA monomers assemble only upon exposure to a target DNA fragment, biomolecular self-assembly pathways, etc. Such work strongly suggests that nanotechnology and synthetic biology together seem poised to constitute the most transformative development of the 21st century. In this paper, we present a Constraint Programming (CP) approach to solve the RNA inverse folding problem. Given a target RNA secondary structure, we determine an RNA sequence which folds into the target structure; i.e. whose minimum free energy structure is the target structure. Our approach represents a step forward in RNA design — we produce the first complete RNA inverse folding approach which allows for the specification of a wide range of design constraints. We also introduce a Large Neighborhood Search approach which allows us to tackle larger instances at the cost of losing completeness, while retaining the advantages of meeting design constraints (motif, GC-content, etc.). Results demonstrate that our software, RNAiFold, performs as well or better than all state-of-the-art approaches; nevertheless, our approach is unique in terms of completeness, flexibility, and the support of various design constraints. The algorithms presented in this paper are publicly available via the interactive webserver http://bioinformatics.bc.edu/clotelab/RNAiFold; additionally, the source code can be downloaded from that site.

A central challenge in the field of metabolic engineering is the efficient identification of a metabolic pathway genotype that maximizes specific productivity over a robust range of process conditions. Here we review current methods for optimizing specific productivity of metabolic pathways in living cells. New tools for library generation, computational analysis of pathway sequence-flux space, and high-throughput screening and selection techniques are discussed.

This paper presents a collection of computational modules implemented with chemical reactions: an inverter, an incrementer, a decrementer, a copier, a comparator, and a multiplier. Unlike previous schemes for chemical computation, ours produces designs that are dependent only on coarse rate categories for the reactions ("fast" vs. "slow"). Given such categories, the computation is exact and independent of the specific reaction rates. We validate our designs through stochastic simulations of the chemical kinetics. Although conceptual for the time being, our methodology has potential applications in domains of synthetic biology such as biochemical sensing and drug delivery. We are exploring DNA-based computation via strand displacement as a possible experimental chassis.

Synthetic biology can be defined as a combinatorial approach where the basic elements, governing the operational functionality of a living cell, are placed together to generate life forms with novel characteristics and properties. One of the most exciting aspects of synthetic biology is the diversity of applications it may enable. New applications may be found in energy, medicine, environment and materials. In this paper, we focus on the definition of synthetic biology as well as the potential applications and key challenges, then the role of synthetic biology in producing biofuels are discussed. Finally, several key points to achieve sustainable development are summarized.