Synthesizing and tuning chemical reaction networks with specified behaviours

We consider how to generate chemical reaction networks (CRNs) from functional specifications. We propose a two-stage approach that combines synthesis by satisfiability modulo theories and Markov chain Monte Carlo based optimisation. First, we identify candidate CRNs that have the possibility to produce correct computations for a given finite set of inputs. We then optimise the reaction rates of each CRN using a combination of stochastic search techniques applied to the chemical master equation, simultaneously improving the of correct behaviour and ruling out spurious solutions. In addition, we use techniques from continuous time Markov chain theory to study the expected termination time for each CRN. We illustrate our approach by identifying CRNs for majority decision-making and division computation, which includes the identification of both known and unknown networks.Comment: 17 pages, 6 figures, appeared the proceedings of the 21st conference on DNA Computing and Molecular Programming, 201

Experimental Biological Protocols with Formal Semantics

Both experimental and computational biology is becoming increasingly automated. Laboratory experiments are now performed automatically on high-throughput machinery, while computational models are synthesized or inferred automatically from data. However, integration between automated tasks in the process of biological discovery is still lacking, largely due to incompatible or missing formal representations. While theories are expressed formally as computational models, existing languages for encoding and automating experimental protocols often lack formal semantics. This makes it challenging to extract novel understanding by identifying when theory and experimental evidence disagree due to errors in the models or the protocols used to validate them. To address this, we formalize the syntax of a core protocol language, which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols. We present both a deterministic and a stochastic semantics to this language, both defined in terms of hybrid processes. In particular, the stochastic semantics captures uncertainties in equipment tolerances, making it a suitable tool for both experimental and computational biologists. We illustrate how the proposed protocol language can be used for automated verification and synthesis of laboratory experiments on case studies from the fields of chemistry and molecular programming

CRNs Exposed: A Method for the Systematic Exploration of Chemical Reaction Networks

Formal methods have enabled breakthroughs in many fields, such as in hardware verification, machine learning and biological systems. The key object of interest in systems biology, synthetic biology, and molecular programming is chemical reaction networks (CRNs) which formalizes coupled chemical reactions in a well-mixed solution. CRNs are pivotal for our understanding of biological regulatory and metabolic networks, as well as for programming engineered molecular behavior. Although it is clear that small CRNs are capable of complex dynamics and computational behavior, it remains difficult to explore the space of CRNs in search for desired functionality. We use Alloy, a tool for expressing structural constraints and behavior in software systems, to enumerate CRNs with declaratively specified properties. We show how this framework can enumerate CRNs with a variety of structural constraints including biologically motivated catalytic networks and metabolic networks, and seesaw networks motivated by DNA nanotechnology. We also use the framework to explore analog function computation in rate-independent CRNs. By computing the desired output value with stoichiometry rather than with reaction rates (in the sense that X ? Y+Y computes multiplication by 2), such CRNs are completely robust to the choice of reaction rates or rate law. We find the smallest CRNs computing the max, minmax, abs and ReLU (rectified linear unit) functions in a natural subclass of rate-independent CRNs where rate-independence follows from structural network properties

On Chemical Reaction Network Design by a Nested Evolution Algorithm

International audienceOne goal of synthetic biology is to implement useful functions with biochemical reactions, either by reprogramming living cells or programming artificial vesicles. In this perspective, we consider Chemical Reaction Networks (CRN) as a programming language, and investigate the CRN program synthesis problem. Recent work has shown that CRN interpreted by differential equations are Turing-complete and can be seen as analog computers where the molecular concentrations play the role of information carriers. Any real function that is computable by a Turing machine in arbitrary precision can thus be computed by a CRN over a finite set of molecular species. The proof of this result gives a numerical method to generate a finite CRN for implementing a real function presented as the solution of a Polynomial Initial Values Problem (PIVP). In this paper, we study an alternative method based on artificial evolution to build a CRN that approximates a real function given on finite sets of input values. We present a nested search algorithm that evolves the structure of the CRN and optimizes the kinetic parameters at each generation. We evaluate this algorithm on the Heaviside and Cosine functions both as functions of time and functions of input molecular species. We then compare the CRN obtained by artificial evolution both to the CRN generated by the numerical method from a PIVP definition of the function, and to the natural CRN found in the BioModels repository for switches and oscillators

Control and Analysis for Sequential Information based on Machine Learning

Sequential information is crucial for real-world applications that are related to time, which is same with time-series being described by sequence data followed by temporal order and regular intervals. In this thesis, we consider four major tasks of sequential information that include sequential trend prediction, control strategy optimisation, visual-temporal interpolation and visual-semantic sequential alignment. We develop machine learning theories and provide state-of-the-art models for various real-world applications that involve sequential processes, including the industrial batch process, sequential video inpainting, and sequential visual-semantic image captioning. The ultimate goal is about designing a hybrid framework that can unify diverse sequential information analysis and control systems For industrial process, control algorithms rely on simulations to find the optimal control strategy. However, few machine learning techniques can control the process using raw data, although some works use ML to predict trends. Most control methods rely on amounts of previous experiences, and cannot execute future information to optimize the control strategy. To improve the effectiveness of the industrial process, we propose improved reinforcement learning approaches that can modify the control strategy. We also propose a hybrid reinforcement virtual learning approach to optimise the long-term control strategy. This approach creates a virtual space that interacts with reinforcement learning to predict a virtual strategy without conducting any real experiments, thereby improving and optimising control efficiency. For sequential visual information analysis, we propose a dual-fusion transformer model to tackle the sequential visual-temporal encoding in video inpainting tasks. Our framework includes a flow-guided transformer with dual attention fusion, and we observe that the sequential information is effectively processed, resulting in promising inpainting videos. Finally, we propose a cycle-based captioning model for the analysis of sequential visual-semantic information. This model augments data from two views to optimise caption generation from an image, overcoming new few-shot and zero-shot settings. The proposed model can generate more accurate and informative captions by leveraging sequential visual-semantic information. Overall, the thesis contributes to analysing and manipulating sequential information in multi-modal real-world applications. Our flexible framework design provides a unified theoretical foundation to deploy sequential information systems in distinctive application domains. Considering the diversity of challenges addressed in this thesis, we believe our technique paves the pathway towards versatile AI in the new era

Design and implementation of a mammalian synthetic gene oscillator

The core goal of synthetic biology as a discipline is to design, develop and characterize biological parts in order to precisely control cellular behaviour. Much of the research in this field has been focused on the development of gene regulatory networks, namely switches and oscillators. The study of synthetic gene oscillators has attracted significant attention in the past decade due to their intriguing dynamics and relevance in controlling inflammatory, metabolic and circadian signalling pathways. Additionally, the precise expression dynamics and molecular mechanisms that underlie the mammalian circadian clock structure are not fully understood. The work presented herein regards the design and implementation of a tuneable mammalian synthetic gene oscillator with a novel biological structure. To this end, an approach based on a combination of in silico design and in vivo part validation, in conjunction with a comparative analysis of previously implemented synthetic gene oscillators, was taken when assembling the proposed system. The topology of the system relies on a delayed negative feedback loop, consisting of the coupled regulatory activities of the transcription regulators LacI, tTA, and Gal4. The numerical solution and stability analysis of an ODE-based model describing the dynamics of the system are indicative that the proposed system is capable of generating sustained oscillations across a wide range of parameter values. The biological parts that comprise the system have been monitored and validated in HEK293T cells through time-lapse fluorescence microscopy and image analysis. The in vivo performance of the proposed mammalian synthetic gene oscillator was also assessed in the HEK293T cell line, and monitored using time-lapse fluorescence microscopy. Damped fluorescence oscillations were observed: these could be tuned by a differential IPTG concentration gradient and abolished by doxycycline. The proposed mammalian synthetic gene oscillator provides valuable insight into the gene expression regulatory processes leading to oscillatory behaviour, and has the potential to foster progress in future synthetic biology-based therapies.Open Acces

Development of 3D biocomposite aerogels for soft tissue engineering applications

Current approaches in developing porous 3D scaffolds face various challenges, such as failure of mimicking extracellular matrix’s (ECM) native building blocks and its functionality. Biopolymer based aerogels have shown to provide structural similarities to the ECM owing to their 3D format and a highly porous structure with interconnected pores. Utilising functional biopolymers (such as hydrophilic polysaccharides and proteins) to fabricate aerogels through freeze-drying technique is found to improve swelling degree, support cell growth and offer rapid enzymatic biodegradation, making such biomaterials appropriate as 3D scaffolds in tissue regeneration. Utilising hydrophilic natural biopolymers is associated with drawbacks such as poor mechanical properties and fast dissolvability. The present research focuses on using cellulose nanofibers (CNF) as a suspension to support the development of porous 3D aerogel biocomposites, compensating the drawbacks associated with natural biopolymers. To develop the biocomposites, CNF is blended with gelatine and starch to obtain aerogels with optimal physicochemical, mechanical and biological characteristics intended to be used as 3D scaffolds for tissue regeneration. The CNF biocomposites with various ratios of CNF: starch (CNF-starch) and CNF: gelatine (CNF-GEL)) were synthesized, and their properties were investigated in terms of physicochemical, mechanical and biological characteristics. Furthermore, Epichlorohydrin (EPH) was used to investigate the effect of chemical crosslinking on the molecular interaction of CNF-starch and CNF-GEL. Ultimately, chemical crosslinking helped to improve the mechanical resilience of the aerogels. The tunability of the physiochemical, mechanical and biological properties of the developed biocomposites makes such structure a great candidate as scaffolds for tissue engineering applications. Both in-vitro and in-vivo studies revealed satisfactory biocompatibility for the crosslinked CNF-GEL biocomposites using dermal fibroblasts. Furthermore, curcumin, a natural material with inherent antimicrobial properties, was added into the CNF-GEL biocomposite as an active molecule agent to improve the antimicrobial and anti-inflammatory responses of the scaffolds. The addition of curcumin was effective against both gram-positive and gram-negative due to the lack of existing antimicrobial characteristics in both CNF and gelatine

A generic approach to behaviour-driven biochemical model construction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Modelling of biochemical systems has received considerable attention over the last decade from bioengineering, biochemistry, computer science, and mathematics. This thesis investigates the applications of computational techniques to computational systems biology, for the construction of biochemical models in terms of topology and kinetic rates. Due to the complexity of biochemical systems, it is natural to construct models representing the biochemical systems incrementally in a piecewise manner. Syntax and semantics of two patterns are defined for the instantiation of components which are extendable, reusable and fundamental building blocks for models composition. We propose and implement a set of genetic operators and composition rules to tackle issues of piecewise composing models from scratch. Quantitative Petri nets are evolved by the genetic operators, and evolutionary process of modelling are guided by the composition rules. Metaheuristic algorithms are widely applied in BioModel Engineering to support intelligent and heuristic analysis of biochemical systems in terms of structure and kinetic rates. We illustrate parameters of biochemical models based on Biochemical Systems Theory, and then the topology and kinetic rates of the models are manipulated by employing evolution strategy and simulated annealing respectively. A new hybrid modelling framework is proposed and implemented for the models construction. Two heuristic algorithms are performed on two embedded layers in the hybrid framework: an outer layer for topology mutation and an inner layer for rates optimization. Moreover, variants of the hybrid piecewise modelling framework are investigated. Regarding flexibility of these variants, various combinations of evolutionary operators, evaluation criteria and design principles can be taken into account. We examine performance of five sets of the variants on specific aspects of modelling. The comparison of variants is not to explicitly show that one variant clearly outperforms the others, but it provides an indication of considering important features for various aspects of the modelling. Because of the very heavy computational demands, the process of modelling is paralleled by employing a grid environment, GridGain. Application of the GridGain and heuristic algorithms to analyze biological processes can support modelling of biochemical systems in a computational manner, which can also benefit mathematical modelling in computer science and bioengineering. We apply our proposed modelling framework to model biochemical systems in a hybrid piecewise manner. Modelling variants of the framework are comparatively studied on specific aims of modelling. Simulation results show that our modelling framework can compose synthetic models exhibiting similar species behaviour, generate models with alternative topologies and obtain general knowledge about key modelling features

Structure evaluation of computer human animation quality

The University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyThis work will give a wide survey for various techniques that are present in the field of character computer animation, which concentrates particularly on those techniques and problems involved in the production of realistic character synthesis and motion. A preliminary user study (including Questionnaire, online publishing such as flicker.com, interview, multiple choice questions, publishing on Android mobile phone, and questionnaire analysis, validation, statistical evaluation, design steps and Character Animation Observation) was conducted to explore design questions, identify users' needs, and obtain a "true story" of quality character animation and the effect of using animation as useful tools in Education. The first set of questionnaires were designed to accommodate the evaluation of animation from candidates from different walks of life, ranging from animators, gamers, teacher assistances (TA), students, teaches, professionals and researchers using and evaluating pre-prepared animated character videos scenarios, and the study outcomes has reviewed the recent advances techniques of character animation, motion editing that enable the control of complex animations by interactively blending, improving and tuning artificial or captured motions. The goal of this work was to augment the students learning intuition by providing ways to make education and learning more interesting, useful and fun objectively, in order to improve students’ respond and understanding to any subject area through the use of animation also by producing the required high quality motion, reaction, interaction and story board to viewers of the motion. We present a variety of different evaluation to the motion quality by measuring user sensitivity, observations to any noticeable artefact, usability, usefulness etc. to derive clear useful guidelines from the results, and discuss several interesting systematic trends we have uncovered in the experimental data. We also present an efficient technique for evaluating the capability of animation influence on education to fulfil the requirements of a given scenario, along with the advantages and the effect on those deficiencies of some methods commonly used to improve animation quality to serve the learning process. Finally, we propose a wide range of extensions and statistical calculation enabled by these evaluation tools, such as Wilcoxon, F-test, T-test, Wondershare Quiz creator (WQC), Chi square and many others explained with full details