Probabilistic reasoning with a bayesian DNA device based on strand displacement

We present a computing model based on the DNA strand displacement technique which performs Bayesian inference. The model will take single stranded DNA as input data, representing the presence or absence of a specific molecular signal (evidence). The program logic encodes the prior probability of a disease and the conditional probability of a signal given the disease playing with a set of different DNA complexes and their ratios. When the input and program molecules interact, they release a different pair of single stranded DNA species whose relative proportion represents the application of Bayes? Law: the conditional probability of the disease given the signal. The models presented in this paper can empower the application of probabilistic reasoning in genetic diagnosis in vitro

Probabilistic reasoning with an enzyme-driven DNA device

We present a biomolecular probabilistic model driven by the action of a DNA toolbox made of a set of DNA templates and enzymes that is able to perform Bayesian inference. The model will take single-stranded DNA as input data, representing the presence or absence of a specific molecular signal (the evidence). The program logic uses different DNA templates and their relative concentration ratios to encode the prior probability of a disease and the conditional probability of a signal given the disease. When the input and program molecules interact, an enzyme-driven cascade of reactions (DNA polymerase extension, nicking and degradation) is triggered, producing a different pair of single-stranded DNA species. Once the system reaches equilibrium, the ratio between the output species will represent the application of Bayes? law: the conditional probability of the disease given the signal. In other words, a qualitative diagnosis plus a quantitative degree of belief in that diagno- sis. Thanks to the inherent amplification capability of this DNA toolbox, the resulting system will be able to to scale up (with longer cascades and thus more input signals) a Bayesian biosensor that we designed previously

Hierarchical dynamics of individual RNA helix base pair formation and disruption

This thesis explores the RNA folding problem using single-molecule field effect transistors (smFETs) to measure the lifetimes of individual RNA base-pairing rearrangements. In the course of this research, considerable computational, chemical, and engineering contributions were developed so that the single-molecule measurements could be conducted and quantified. These advancements have allowed, on the basis of the smFET data collected herein, the quantification of a kinetic model for RNA stem-loop structures which has been generalized to quantitatively explore the phenomenological observation that an RNA found in the bacillus subtilis strain acts as a metabolite-sensing switch, allowing RNA polymerase to transcribe the messenger RNA when the metabolite is present and preventing transcription when the metabolite is absent. Together, the data presented quantify a simple model for the base pairing rearrangements that underlie RNA folding

Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo

Accurate single cell mutational profiles can reveal genomic cell-to-cell heterogeneity. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. The resulting data violates assumptions of variant callers developed for bulk sequencing. Thus, only dedicated models accounting for amplification bias and errors can provide accurate calls. We present ProSolo for calling single nucleotide variants from multiple displacement amplified (MDA) single cell DNA sequencing data. ProSolo probabilistically models a single cell jointly with a bulk sequencing sample and integrat

Spatial statistical modelling of epigenomic variability

Each cell in our body carries the same genetic information encoded in the DNA, yet the human organism contains hundreds of cell types which differ substantially in physiology and functionality. This variability stems from the existence of regulatory mechanisms that control gene expression, and hence phenotype. The field of epigenetics studies how changes in biochemical factors, other than the DNA sequence itself, might affect gene regulation. The advent of high throughput sequencing platforms has enabled the profiling of different epigenetic marks on a genome-wide scale; however, bespoke computational methods are required to interpret these high-dimensional data and investigate the coupling between the epigenome and transcriptome. This thesis contributes to the development of statistical models to capture spatial correlations of epigenetic marks, with the main focus being DNA methylation. To this end, we developed BPRMeth (Bayesian Probit Regression for Methylation), a probabilistic model for extracting higher order methylation features that precisely quantify the spatial variability of bulk DNA methylation patterns. Using such features, we constructed an accurate machine learning predictor of gene expression from DNA methylation and identified prototypical methylation profiles that explain most of the variability across promoter regions. The BPRMeth model, and its algorithmic implementation, were subsequently substantially extended both to accommodate different data types, and to improve the scalability of the algorithm. Bulk experiments have paved the way for mapping the epigenetic landscape, nonetheless, they fall short of explaining the epigenetic heterogeneity and quantifying its dynamics, which inherently occur at the single cell level. Single cell bisulfite sequencing protocols have been recently developed, however, due to intrinsic limitations of the technology they result in extremely sparse coverage of CpG sites, effectively limiting the analysis repertoire to a semi-quantitative level. To overcome these difficulties we developed Melissa (MEthyLation Inference for Single cell Analysis), a Bayesian hierarchical model that leverages local correlations between neighbouring CpGs and similarity between individual cells to jointly impute missing methylation states, and cluster cells based on their genome-wide methylation profiles. A recent experimental innovation enables the parallel profiling of DNA methylation, transcription and chromatin accessibility (scNMT-seq), making it possible to link transcriptional and epigenetic heterogeneity at the single cell resolution. For the scNMT-seq study, we applied the extended BPRMeth model to quantify cell-to-cell chromatin accessibility heterogeneity around promoter regions and subsequently link it to transcript abundance. This revealed that genes with conserved accessibility profiles are associated with higher average expression levels. In summary, this thesis proposes statistical methods to model and interpret epigenomic data generated from high throughput sequencing experiments. Due to their statistical power and flexibility we anticipate that these methods will be applicable to future sequencing technologies and become widespread tools in the high throughput bioinformatics workbench for performing biomedical data analysis

Biomolecular System Design: Architecture, Synthesis, and Simulation

The advancements in systems and synthetic biology have been broadening the range of realizable systems with increasing complexity both in vitro and in vivo. Systems for digital logic operations, signal processing, analog computation, program flow control, as well as those composed of different functions – for example an on-site diagnostic system based on multiple biomarker measurements and signal processing – have been realized successfully. However, the efforts to date tend to tackle each design problem separately, relying on ad hoc strategies rather than providing more general solutions based on a unified and extensible architecture, resulting in long development cycle and rigid systems that require redesign even for small specification changes.Inspired by well-tested techniques adopted in electronics design automation (EDA), this work aims to remedy current design methodology by establishing a standardized, complete flow for realizing biomolecular systems. Given a behavior specification, the flow streamlines all the steps from modeling, synthesis, simulation, to final technology mapping onto implementing chassis. The resulted biomolecular systems of our design flow are all built on top of an FPGA-like reconfigurable architecture with recurring modules. Each module is designed the function of eachmodule depends on the concentrations of assigned auxiliary species acting as the “tuning knobs.” Reconfigurability not only simplifies redesign for altered specification or post-simulation correction, but also makes post-manufacture fine-tuning – even after system deployment – possible. This flexibility is especially important in synthetic biology due to the unavoidable variations in both the deployed biological environment and the biomolecular reactions forming the designed system.In fact, by combining the system’s reconfigurability and neural network’s self-adaptiveness through learning, we further demonstrate the high compatibility of neuromorphic computation to our proposed architecture. Simulation results verified that with each module implementing a neuron of selected model (ex. spike-based, threshold-gate-like, etc.), accompanied by an appropriate choice of reconfigurable properties (ex. threshold value, synaptic weight, etc.), the system built from our proposed flow can indeed perform desired neuromorphic functions

Understanding of photosynthesis concepts related to students’ age

In Croatian schools, the complex photosynthesis concept is presented several times during primary and secondary school, each time with more detail. The problems in understanding photosynthesis processes are known from many previous studies and our own research ; thus we aimed to investigate how the students’ understanding of the basic photosynthesis concepts increases during the schooling period, and is it enhanced by gradual introduction of new contents. The present study was conducted on 269 students from 6 schools and 35 students preparing to be biology teachers. To test the students’ conceptual understanding, we implemented a question about the trends of O2 and CO2 gas concentrations during the night, which was expected to lead students to a correct explanation of photosynthesis, including the issues of the plants’ respiration and the absence of photosynthesis. Students of all age groups gave mainly incomplete explanations. The best result was achieved by the youngest participants in the age of 11, who have relied on the freshly acquired and well trained, but reproductive knowledge. Older students’ answers (aged 15, 17 and 22), which include more detail about the light-dependent and light-independent reactions, suggested that they developed misconceptions such as the belief that “oxygen is produced in Calvin cycle during the night” and that “CO2 converts to O2”. Student's explanations indicate the consistency of their understanding of the process, which does not change with gradual introduction of new contents as they are older. The observed misunderstanding could be linked to the cumulative introduction of the complex theoretical contents, but excluding research- based learning, as well as to inadequate time dedicated to establishing connections between students’ pre-conceptions and novel information. Our research results might be a strong argument supporting the upcoming change in the national curriculum