End-to-end Molecular Communication Channels in Cell Metabolism: an Information Theoretic Study

The opportunity to control and fine-tune the behavior of biological cells is a fascinating possibility for many diverse disciplines, ranging from medicine and ecology, to chemical industry and space exploration. While synthetic biology is providing novel tools to reprogram cell behavior from their genetic code, many challenges need to be solved before it can become a true engineering discipline, such as reliability, safety assurance, reproducibility and stability. This paper aims to understand the limits in the controllability of the behavior of a natural (non-engineered) biological cell. In particular, the focus is on cell metabolism, and its natural regulation mechanisms, and their ability to react and change according to the chemical characteristics of the external environment. To understand the aforementioned limits of this ability, molecular communication is used to abstract biological cells into a series of channels that propagate information on the chemical composition of the extracellular environment to the cell’s behavior in terms of uptake and consumption of chemical compounds, and growth rate. This provides an information-theoretic framework to analyze the upper bound limit to the capacity of these channels to propagate information, which is based on a well-known and computationally efficient metabolic simulation technique. A numerical study is performed on two human gut microbes, where the upper bound is estimated for different environmental compounds, showing there is a potential for future practical applications

Probing the limits to microRNA-mediated control of gene expression

According to the `ceRNA hypothesis', microRNAs (miRNAs) may act as mediators of an effective positive interaction between long coding or non-coding RNA molecules, carrying significant potential implications for a variety of biological processes. Here, inspired by recent work providing a quantitative description of small regulatory elements as information-conveying channels, we characterize the effectiveness of miRNA-mediated regulation in terms of the optimal information flow achievable between modulator (transcription factors) and target nodes (long RNAs). Our findings show that, while a sufficiently large degree of target derepression is needed to activate miRNA-mediated transmission, (a) in case of differential mechanisms of complex processing and/or transcriptional capabilities, regulation by a post-transcriptional miRNA-channel can outperform that achieved through direct transcriptional control; moreover, (b) in the presence of large populations of weakly interacting miRNA molecules the extra noise coming from titration disappears, allowing the miRNA-channel to process information as effectively as the direct channel. These observations establish the limits of miRNA-mediated post-transcriptional cross-talk and suggest that, besides providing a degree of noise buffering, this type of control may be effectively employed in cells both as a failsafe mechanism and as a preferential fine tuner of gene expression, pointing to the specific situations in which each of these functionalities is maximized.Comment: 16 page

Modelling and Visualizing Selected Molecular Communication Processes in Biological Organisms: A Multi-Layer Perspective

The future pervasive communication and computing devices are envisioned to be tightly integrated with biological systems, i.e., the Internet of Bio-Nano Things. In particular, the study and exploitation of existing processes for the biochemical information exchange and elaboration in biological systems are currently at the forefront of this research direction. Molecular Communication (MC), which studies biochemical information systems with theory and tools from computer communication engineering, has been recently proposed to model and characterize the aforementioned processes. Combined with the rapidly growing field of bio-informatics, which creates a rich profusion of biological data and tools to mine the underlying information, this investigation direction is set to produce interesting results and methodologies not only for systems engineering but also for novel scientific discovery. The multidisciplinary nature of this work presents an interesting challenge in terms of creating a structured approach to combine the aforementioned disciplines for the study of information propagation processes in biological organisms, and their relationship with information for their control, optimization, and exploitation. In this thesis, we study a selection of these processes, through different and independent contributions, at the system layer, cellular layer and pathway layer. First, we model the overall functionality of a multicellular metabolic system, the human digestion, in terms of energy production from major nutrients in the food. Second, we analyze metabolic processes in a single cell and their adaptability to incoming nutrient availability information from the environment. Third, we model and characterize the processes that enable information to propagate from the external environment and be processed by the cell. Numerical results are presented to provide a first proof-of-concept characterization of all these processes in terms of communication theory. While it may be possible to connect each of these layers in future work, this goes beyond the scope of the work reported in this thesis. Adviser: Massimiliano Pierobo

Field-control, phase-transitions, and life's emergence

Instances of critical-like characteristics in living systems at each organizational level as well as the spontaneous emergence of computation (Langton), indicate the relevance of self-organized criticality (SOC). But extrapolating complex bio-systems to life's origins, brings up a paradox: how could simple organics--lacking the 'soft matter' response properties of today's bio-molecules--have dissipated energy from primordial reactions in a controlled manner for their 'ordering'? Nevertheless, a causal link of life's macroscopic irreversible dynamics to the microscopic reversible laws of statistical mechanics is indicated via the 'functional-takeover' of a soft magnetic scaffold by organics (c.f. Cairns-Smith's 'crystal-scaffold'). A field-controlled structure offers a mechanism for bootstrapping--bottom-up assembly with top-down control: its super-paramagnetic components obey reversible dynamics, but its dissipation of H-field energy for aggregation breaks time-reversal symmetry. The responsive adjustments of the controlled (host) mineral system to environmental changes would bring about mutual coupling between random organic sets supported by it; here the generation of long-range correlations within organic (guest) networks could include SOC-like mechanisms. And, such cooperative adjustments enable the selection of the functional configuration by altering the inorganic network's capacity to assist a spontaneous process. A non-equilibrium dynamics could now drive the kinetically-oriented system towards a series of phase-transitions with appropriate organic replacements 'taking-over' its functions.Comment: 54 pages, pdf fil

Optimizing information flow in small genetic networks. I

In order to survive, reproduce and (in multicellular organisms) differentiate, cells must control the concentrations of the myriad different proteins that are encoded in the genome. The precision of this control is limited by the inevitable randomness of individual molecular events. Here we explore how cells can maximize their control power in the presence of these physical limits; formally, we solve the theoretical problem of maximizing the information transferred from inputs to outputs when the number of available molecules is held fixed. We start with the simplest version of the problem, in which a single transcription factor protein controls the readout of one or more genes by binding to DNA. We further simplify by assuming that this regulatory network operates in steady state, that the noise is small relative to the available dynamic range, and that the target genes do not interact. Even in this simple limit, we find a surprisingly rich set of optimal solutions. Importantly, for each locally optimal regulatory network, all parameters are determined once the physical constraints on the number of available molecules are specified. Although we are solving an over--simplified version of the problem facing real cells, we see parallels between the structure of these optimal solutions and the behavior of actual genetic regulatory networks. Subsequent papers will discuss more complete versions of the problem

Characterization of Molecular Communication Based on Cell Metabolism Through Mutual Information and Flux Balance Analysis

Synthetic biology is providing novel tools to engineer cells and access the basis of their molecular information processing, including their communication channels based on chemical reactions and molecule exchange. Molecular communication is a discipline in communication engineering that studies these types of communications and ways to exploit them for novel purposes, such as the development of ubiquitous and heterogeneous communication networks to interconnect biological cells with nano and biotechnology-enabled devices, i.e., the Internet of Bio-Nano Things. One major problem in realizing these goals stands in the development of reliable techniques to control the engineered cells and their behavior from the external environment. A possible solution may stem from exploiting the natural mechanisms that allow cells to regulate their metabolism, the complex network of chemical reactions that underlie their growth and reproduction, as a function of chemical compounds in the environment. In this thesis, molecular communication concepts are applied to study the potential of cell metabolism, and its regulation, to channel information from the outside environment into the cell as function of chemical compounds in the environment, and quantify how much information of the internal state of the metabolic network can be perceived from the outside environment. For this, cell metabolism is characterized in this work through two abstractions, namely, as a molecular communication encoder and a modulator, respectively. The former models the cell metabolism as a binary encoder of the mechanisms underlying the regulation of the cell metabolic network state in function of the chemical composition of the external environment. The latter models the metabolic network inside the cell as a digital modulator of metabolite exchange/growth according to the information contained in its state. Based on these abstractions, the aforementioned potential of cell metabolism is quantified with the information theoretic mutual information parameter obtained through the use of a well-known and computationally efficient metabolic simulation technique. Numerical results are obtained through simulation of cell metabolism based on the standard processes of Genome Scale Modeling (GEM) and Flux Balance Analysis (FBA). These preliminary proof-of-concept results are based on the following three main cellular species: Escherichia coli (E. coli), the “standard organism in microbiology, and two important human gut microbes studied in our collaborators\u27 lab, namely, the Bacteroides thetaiotaomicron (B. theta) and the Methanobrevibacter smithii (M. smithii), which provide a direct connection of this work to future practical applications. Adviser: Massimiliano Pierobo

Integrative gene-metabolite network with implemented causality deciphers informational fluxes of sulphur stress response

The systematic accumulation of gene expression data, although revolutionary, is insufficient in itself for an understanding of system-level physiology. In the post-genomic era, the next cognitive step is linking genes to biological processes and assembling a mosaic of data into global models of biosystem function. A dynamic network of informational flows in Arabidopsis plants perturbed by sulphur depletion is presented here. With the use of an original protocol, the first blosystem response network was reconstructed from a time series of transcript and metabolite profiles, which, on the one hand, integrates complex metabolic and transcript data and, on the other hand, possesses a causal relationship. Using the informational fluxes within this reconstruction, it was possible to link system perturbation to response endpoints. Robustness and stress tolerance, as consequences of scale-free network topology, and hubs, as potential controllers of homeostasis maintenance, were revealed. Communication paths of propagating system excitement directed to physiological endpoints, such as anthocyanin accumulation and enforced root formation were dissected from the network. An auxin regulatory circuit involved in the control of a hypo-sulphur stress response was uncovered

Towards Autopoietic Computing

A key challenge in modern computing is to develop systems that address complex, dynamic problems in a scalable and efficient way, because the increasing complexity of software makes designing and maintaining efficient and flexible systems increasingly difficult. Biological systems are thought to possess robust, scalable processing paradigms that can automatically manage complex, dynamic problem spaces, possessing several properties that may be useful in computer systems. The biological properties of self-organisation, self-replication, self-management, and scalability are addressed in an interesting way by autopoiesis, a descriptive theory of the cell founded on the concept of a system's circular organisation to define its boundary with its environment. In this paper, therefore, we review the main concepts of autopoiesis and then discuss how they could be related to fundamental concepts and theories of computation. The paper is conceptual in nature and the emphasis is on the review of other people's work in this area as part of a longer-term strategy to develop a formal theory of autopoietic computing.Comment: 10 Pages, 3 figure