Kaemika app, Integrating protocols and chemical simulation

Kaemika is an app available on the four major app stores. It provides deterministic and stochastic simulation, supporting natural chemical notation enhanced with recursive and conditional generation of chemical reaction networks. It has a liquid-handling protocol sublanguage compiled to a virtual digital microfluidic device. Chemical and microfluidic simulations can be interleaved for full experimental-cycle modeling. A novel and unambiguous representation of directed multigraphs is used to lay out chemical reaction networks in graphical form

Reactamole: Functional Reactive Molecular Programming

Chemical reaction networks (CRNs) are an important tool for molecular programming, a field that is rapidly expanding our ability to deploy computer programs into biological systems for a variety of applications. However, CRNs are also difficult to work with due to their massively parallel nature, leading to the need for higher-level languages that allow for easier computation with CRNs. Recently, research has been conducted into a variety of higher-level languages for deterministic CRNs but modeling CRN parallelism, managing error accumulation, and finding natural CRN representations are ongoing challenges. We introduce Reactamole, a higher-level language for deterministic CRNs that utilizes the functional reactive programming (FRP) paradigm to represent CRNs as a reactive dataflow network. Reactamole equates a CRN with a functional reactive program, implementing the key primitives of the FRP paradigm directly as CRNs. The functional nature of Reactamole makes reasoning about molecular programs easier, and its strong static typing allows us to ensure that a CRN is well-formed by virtue of being well-typed. In this paper, we describe the design of Reactamole and how we use CRNs to represent the common datatypes and operations found in FRP. We also demonstrate the potential of this functional reactive approach to molecular programming by giving an extended example where a CRN is constructed using FRP to modulate and demodulate an amplitude modulated signal

Design and analysis of genetic feedback architectures for synthetic biology

Synthetic Biology seeks to design and assemble novel biological systems with favourable properties. It allows us to comprehend and modify the fundamental mechanisms of life and holds significant promise in revolutionizing current technologies ranging from medicine and biomanufacturing to energy and environmental protection. Biological processes constitute remarkably complex dynamical systems operating impeccably well in messy and constantly changing environments. Their ability to do so is rooted in sophisticated molecular control architectures crafted by natural evolutionary innovation over billions of years. Such control architectures, often blended with human-engineering approaches, are the key to realizing efficient and reliable synthetic biological systems. Aiming to accelerate the development of the latter, the present thesis addresses some fundamental challenges in biomolecular systems and control design. We begin by elucidating biological mechanisms of temporal gradient computation, enabling cells to adjust their behaviour in response to anticipated environmental changes. Specifically, we introduce biomolecular motifs capable of functioning as highly tunable and accurate signal differentiators to input molecular signals around their nominal operation. We investigate strategies to deal with high-frequency input signal components which can be detrimental to the performance of most differentiators. We ascertain the occurrence of such motifs in natural regulatory networks and demonstrate the potential of synthetic experimental realizations. Our motifs can serve as reliable speed biosensors and can form the basis for derivative feedback control. Motivated by the pervasiveness of Proportional-Integral-Derivative (PID) controllers in modern technological applications, we present the realization of a PID controller via biomolecular reactions employing, among others, our differentiator motifs. This biomolecular architecture represents a PID control law with set point weighting and filtered derivative action, offering robust regulation of a single-output biological process with enhanced dynamic performance and low levels of stochastic noise. It is characterized by significant ease of tuning and can be of particular experimental interest in molecular programming applications. Finally, we investigate efficient regulation strategies for multi-output biological processes with internal coupling interactions, expanding previously established single-output control approaches. More specifically, we propose control schemes allowing for robust manipulation of the outputs in various ways, namely manipulation of their product/ratio, linear combinations of them as well as manipulation of each of the outputs independently. Our analysis is centered around two-output biological processes, yet the scalability of the proposed regulation strategies to processes with a higher number of outputs is highlighted. In parallel, their experimental implementability is explored in both in vivo and in vitro settings

Kaemika app: Integrating protocols and chemical simulation

Kaemika is an app available on the four major app stores. It provides deterministic and stochastic simulation, supporting natural chemical notation enhanced with recursive and conditional generation of chemical reaction networks. It has a liquid-handling protocol sublanguage compiled to a virtual digital microfluidic device. Chemical and microfluidic simulations can be interleaved for full experimental-cycle modeling. A novel and unambiguous representation of directed multigraphs is used to lay out chemical reaction networks in graphical form