Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

Overcoming Metabolic Burden in Synthetic Biology: a CRISPR interference approach

Synthetic Biology is gaining an increasingly important role in the scientific community and dedicated research centers are rising all over the world. This discipline introduced the engineering principles of abstraction, modularity and standardization in the biology world; the application of these principles is allowing the design of complex biological systems to program living cells, realizing all sorts of desired function in many fields. These systems consist of DNA sequences, rationally combined to program the genetic instructions for cell behavior customization. Each part should behave as a biological brick for the design of complex genetic programs through functional building blocks; each module undergoes an extensive characterization to provide documentation on its functioning, enabling the rational design of complex circuits. Mathematical modeling accompanies all the design procedure as a tool to describe the behavior of each single genetic module, in a bottom-up fashion that should allow the prediction of more complex systems obtained by the interconnection of pre-characterized parts. However, many unpredictability sources hamper the ideally rational design of those synthetic genetic devices, mainly due to the tangled context-dependency behavior of those parts once placed into an intrinsically complex biological living system. Among others, the finite amount of translational resources in prokaryotic cells leads to an effect called metabolic burden, as a result of which hidden interactions between protein synthesis rates arise, leading to unexpected counterintuitive behaviors. To face this issue, two actions have been proposed in this study: firstly, a recently proposed mathematical modeling solution that included a description of the metabolic load exerted by the expression of recombinant genes have been applied on a case study, highlighting its worth of use and working boundaries; second, a CRISPR interference-based architecture have been developed to be used as an alternative to high resource usage transcriptional protein regulators, studying the underlying mechanism in several circuital configurations and optimizing each forming part in order to achieve the desired specifications. In Chapter 1, an introduction on synthetic biology is presented; in the second part, a brief overview on CRISPR technology and the overall aim of the study are reported. In Chapter 2, a case study evaluating the use of mathematical modeling to properly include metabolic burden in rational design of a set of transcriptional regulator cascades is reported. Firstly, the circuits and expected behavior are introduced, along with the discussion about experimental data, dissenting from what initially predicted. Secondly, the comparison between the use of a classical Hill equation-based model and an improved version that explicitly consider the translational load exerted by the expression of recombinant genes is reported. In Chapter 3, the design and deep characterization of a BioBrick$^{TM}$-compatible CRISPR interference-based repression set of modules is shown; expression optimization of the molecular players is reported and its usability as a low-burden alternative is demonstrated with experimental data and mathematical modeling. Working boundaries, peculiar aspects and rooms for improvements are then highlighted. In Chapter 4, preliminary studies aimed to improve the CRISPR interference system are reported and some of its context-dependencies are highlighted. Effects on repression efficiency due to alteration in the sequence of the RNA molecules addressing the CRISPR machinery to the desired target are discussed; evaluation of problems and opportunities related to the expression of more of this RNA guides are then highlighted. Lastly, an example of behavior of the system in presence of a competitor transcriptional regulator is reported. In Chapter 5 the overall conclusions of this thesis work are drawn

Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.Comment: review article, 29 pages, 7 figure

Aging and Health

Aging is a major risk factor for chronic diseases, which in turn can provide information about the aging of a biological system. This publication serves as an introduction to systems biology and its application to biological aging. Key pathways and processes that impinge on aging are reviewed, and how they contribute to health and disease during aging is discussed. The evolution of this situation is analyzed, and the consequences for the study of genetic effects on aging are presented. Epigenetic programming of aging, as a continuation of development, creates an interface between the genome and the environment. New research into the gut microbiome describes how this interface may operate in practice with marked consequences for a variety of disorders. This analysis is bolstered by a view of the aging organism as a whole, with conclusions about the mechanisms underlying resilience of the organism to change, and is expanded with a discussion of circadian rhythms in aging

Modelling heterogeneous intracellular networks at the cellular scale

Cell function relies on the coordinated action of heterogeneous interconnected networks of biomolecules. Mathematical models help us explore the dynamics and behaviour of these intracellular networks in greater detail. Models of increasing scale and complexity are being developed to probe cellular processes, often necessitating the use of several types of mathematical representation in hybrid models. Here we review recent efforts to incorporate the influences of stochasticity and spatial heterogeneity into cellular level models, ranging from abstract coarse-grained representations to large-scale hybrid models comprising thousands of biological components. We discuss the key challenges involved in, and recent mathematical advances enabling the development and analysis of mathematical models of complex intracellular processes

Synthesis of Biological and Mathematical Methods for Gene Network Control

abstract: Synthetic biology is an emerging field which melds genetics, molecular biology, network theory, and mathematical systems to understand, build, and predict gene network behavior. As an engineering discipline, developing a mathematical understanding of the genetic circuits being studied is of fundamental importance. In this dissertation, mathematical concepts for understanding, predicting, and controlling gene transcriptional networks are presented and applied to two synthetic gene network contexts. First, this engineering approach is used to improve the function of the guide ribonucleic acid (gRNA)-targeted, dCas9-regulated transcriptional cascades through analysis and targeted modification of the RNA transcript. In so doing, a fluorescent guide RNA (fgRNA) is developed to more clearly observe gRNA dynamics and aid design. It is shown that through careful optimization, RNA Polymerase II (Pol II) driven gRNA transcripts can be strong enough to exhibit measurable cascading behavior, previously only shown in RNA Polymerase III (Pol III) circuits. Second, inherent gene expression noise is used to achieve precise fractional differentiation of a population. Mathematical methods are employed to predict and understand the observed behavior, and metrics for analyzing and quantifying similar differentiation kinetics are presented. Through careful mathematical analysis and simulation, coupled with experimental data, two methods for achieving ratio control are presented, with the optimal schema for any application being dependent on the noisiness of the system under study. Together, these studies push the boundaries of gene network control, with potential applications in stem cell differentiation, therapeutics, and bio-production.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Preface: BITS2014, the annual meeting of the Italian Society of Bioinformatics

This Preface introduces the content of the BioMed Central journal Supplements related to BITS2014 meeting, held in Rome, Italy, from the 26th to the 28th of February, 2014

Machine and deep learning meet genome-scale metabolic modeling

Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process