ABSTRACT. The programmability of DNA and RNA base pairing has enabled the creation of a very wide range of synthetic nanostructures: it is possible to synthesize synthetic oligonucleotides such that a target structure, by design the most stable assembly product, forms spontaneously when these molecular components are mixed. More sophisticated design techniques can be used to control the kinetics as well as the thermodynamics of the interactions between nucleic acid molecules, creating the potential to improve yields through design of assembly pathways and allowing the construction of dynamic systems that process information and of synthetic molecular machinery. Techniques of simulation and verification are important in understanding and designing these increasingly complex systems. I shall present a broad review of this rapidly developing research field, with particular emphasis on our work on DNA origami assembly pathways, kinetic control of strand displacement reactions, molecular motors, and molecular machinery for the control of chemical synthesis.

ABSTRACT. With the advancement in nucleic-acid-based technology in general, and strand-displacement DNA computing in particular, 
a large class of abstract biochemical networks may be physically realized using nucleic acids. 
Mathematical and experimental methods for designing abstract biochemical circuits, and then physically realizing them, respectively, 
have been predominantly developed at the (less-detailed) deterministic level, when the circuits involve molecules in high-abundance and 
operate in well-mixed environments. A proof-of-concept is a recently in-vitro man-made 
chemical oscillator, called the displacillator. However, molecular circuits involving species in low-abundance, 
and operating in fluctuating environments, are increasingly found to play an important role in applications, 
such as when molecular computers are interfaced with cell-like vesicles, and when they are utilized in 
nanotechnology. In such circumstances, 
methods for controlling the intrinsic noise in the system are necessary for a successful network design 
at the (more-detailed) stochastic level. 

To bridge the gap, the noise-control algorithm for designing biochemical networks will be presented in
this talk. The algorithm structurally modifies any given reaction network under mass-action kinetics, 
in such a way that (i) controllable state-dependent noise is introduced into the stochastic dynamics 
(the chemical master equation), while (ii) the deterministic dynamics (reaction-rate equations) are preserved.  
The structural modification involves appropriately enlarging the input network, by adding suitable
auxiliary species and chemical reactions. This allows for a hybrid approach when constructing reaction networks: 
the deterministic model may be used to guide the design, while the noise-control algorithm may be applied to 
favorably reprogram the intrinsic noise in the stochastic model. The deterministic-stochastic hybrid approach 
allows one to reshape the probability distributions of target chemical species, and gain control over their sample-paths, 
while inheriting the fixed mean-field behaviors. The capabilities of the algorithm are demonstrated by redesigning 
test reaction systems, enriching them with stochastic phenomena, such as noise-induced multimodality/multistability 
(coexistenceof multiple maxima in the probability distributions) and oscillations.

ABSTRACT. The developmental process by which complex tissues, organs and organisms develop begins with pluripotency: the ability of so-called ‘naïve’ embryonic stem cells to generate the full spectrum of adult cell types, as well as the germline. Understanding how these cells differentiate to diverse fate-restricted lineages is key both to understand the biological programs that govern development, but also to utilise the power of these cells for regenerative medicine. Fate decisions arise as the consequence of a complex interplay between regulatory factors, and while experiments have revealed critical genes and possible interactions between them, our understanding of stem cell decision-making remains fragmentary. Against this backdrop, automated reasoning provides a powerful strategy to navigate this complexity and to derive interaction networks that are consistent with experimental ‘specifications’. These networks can subsequently be used to formulate predictions of untested behaviour that guide experiment and inform model refinement. In this talk, I will describe such a reasoning methodology, which has been applied to investigate stem cell pluripotency through an iterative computational and experimental strategy. Furthermore, I will show how this approach has generated insight into how fate-restricted cells can be ‘reprogrammed’ to the embryonic stem-like state.

ABSTRACT. We propose a systematic approach to approximate the behaviour of models of polymers synthesis/degradation. Our technique consists in discovering time-dependent lower and upper bounds for the concentration of some patterns of interest.  These bounds are obtained by approximating the state of the system by a hyper-box, with differential equations defining the evolution of the coordinates of each hyper-face. The equation of each hyper-face is obtained by pessimistically bounding the derivative with respect to the corresponding coordinate when the system state ranges over this hyper-face.

In order to synthesise these bounds, we use Kappa to describe our models of polymers. This provides symbolic equalities and inequalities which intentionally may be understood as algebraic constructions  over patterns, and extensionally as sound properties about the concentration of the bio-molecular species that contain these patterns.