Design of dynamic genetic memory

In electronic systems, dynamic random access memory (DRAM) is one of the core modules in the modern silicon computer. As for a bio‐computer, one would need a mechanism for storage of bio‐information named ‘data’, which, in binary logic, has two levels, logical high and logical low, or in the normalised form, ‘1’ and ‘0’. This study proposes a possible genetic DRAM based on the modified electronic configuration, which uses the biological reaction to fulfil an equivalent RC circuit constituting a memory cell. The authors implement fundamental functions of the genetic DRAM by incorporating a genetic toggle switch for data hold. The results of simulation verify that the basic function can be used on a bio‐storage module for the future bio‐computer.Inspec keywords: DRAM chips, genetic engineering, biocomputers, bioinformatics, equivalent circuits, RC circuitsOther keywords: dynamic genetic memory design, electronic systems, dynamic random access memory, modern silicon computer, biocomputer, bioinformation, binary logic, logical high level, logical low level, normalised form, genetic DRAM, modified electronic configuration, biological reaction, equivalent RC circuit, memory cell, fundamental functions, genetic toggle switch, data hold, biostorage module     

The evolution of developmental patterning under genetic duplication constraints

Of considerable interest are the evolutionary and developmental origins of complex, adaptive structures and the mechanisms that stabilize these structures. We consider the relationship between the evolutionary process of gene duplication and deletion and the stability of morphogenetic patterns produced by interacting activators and inhibitors. We compare the relative stability of patterns with a single activator and inhibitor (two-dimensional system) against a ‘redundant’ system with two activators or two inhibitors (three-dimensional system). We find that duplication events can both expand and contract the space of patterns. We study developmental robustness in terms of stochastic escape times from this space, also known as a ‘canalization potential’. We embed the output of pattern formation into an explicit evolutionary model of gene duplication, gene loss and variation in the steepness of the canalization potential. We find that under all constant conditions, the system evolves towards a preference for steep potentials associated with low phenotypic variability and longer lifespans. This preference leads to an overall decrease in the density of redundant genotypes as developmental robustness neutralizes the advantages of genetic robustness.     

The organization of biological sequences into constrained and unconstrained parts determines fundamental properties of genotype–phenotype maps

Biological information is stored in DNA, RNA and protein sequences, which can be understood as genotypes that are translated into phenotypes. The properties of genotype–phenotype (GP) maps have been studied in great detail for RNA secondary structure. These include a highly biased distribution of genotypes per phenotype, negative correlation of genotypic robustness and evolvability, positive correlation of phenotypic robustness and evolvability, shape-space covering, and a roughly logarithmic scaling of phenotypic robustness with phenotypic frequency. More recently similar properties have been discovered in other GP maps, suggesting that they may be fundamental to biological GP maps, in general, rather than specific to the RNA secondary structure map. Here we propose that the above properties arise from the fundamental organization of biological information into ‘constrained'' and ‘unconstrained'' sequences, in the broadest possible sense. As ‘constrained'' we describe sequences that affect the phenotype more immediately, and are therefore more sensitive to mutations, such as, e.g. protein-coding DNA or the stems in RNA secondary structure. ‘Unconstrained'' sequences, on the other hand, can mutate more freely without affecting the phenotype, such as, e.g. intronic or intergenic DNA or the loops in RNA secondary structure. To test our hypothesis we consider a highly simplified GP map that has genotypes with ‘coding'' and ‘non-coding'' parts. We term this the Fibonacci GP map, as it is equivalent to the Fibonacci code in information theory. Despite its simplicity the Fibonacci GP map exhibits all the above properties of much more complex and biologically realistic GP maps. These properties are therefore likely to be fundamental to many biological GP maps.     

A tractable genotype–phenotype map modelling the self-assembly of protein quaternary structure

The mapping between biological genotypes and phenotypes is central to the study of biological evolution. Here, we introduce a rich, intuitive and biologically realistic genotype–phenotype (GP) map that serves as a model of self-assembling biological structures, such as protein complexes, and remains computationally and analytically tractable. Our GP map arises naturally from the self-assembly of polyomino structures on a two-dimensional lattice and exhibits a number of properties: redundancy (genotypes vastly outnumber phenotypes), phenotype bias (genotypic redundancy varies greatly between phenotypes), genotype component disconnectivity (phenotypes consist of disconnected mutational networks) and shape space covering (most phenotypes can be reached in a small number of mutations). We also show that the mutational robustness of phenotypes scales very roughly logarithmically with phenotype redundancy and is positively correlated with phenotypic evolvability. Although our GP map describes the assembly of disconnected objects, it shares many properties with other popular GP maps for connected units, such as models for RNA secondary structure or the hydrophobic-polar (HP) lattice model for protein tertiary structure. The remarkable fact that these important properties similarly emerge from such different models suggests the possibility that universal features underlie a much wider class of biologically realistic GP maps.     

First principle approach towards logic design using hydrogen‐doped single‐strand DNA

Molecular logic gate has been proposed using single‐strand DNA (ssDNA) consisting of basic four nucleobases. In this study, density functional theory and non‐equilibrium Green''s function based first principle approach is applied to investigate the electronic transmission characteristics of ssDNA chain. The heavily hydrogen‐doped‐ssDNA (H‐ssDNA) chain is connected with gold electrode to achieve enhanced quantum‐ballistic transmission along 〈1 1 1〉 direction. Logic gates OR, Ex‐OR, NXOR have been implemented using this analytical model of H‐ssDNA device. Enhanced logic properties have been observed for ssDNA after H adsorption due to improved electronic transmission. Dense electron cloud is considered as logic ‘high’ (1) output in presence of hydrogen molecule and on the contrary sparse cloud indicate logic ‘low’ (0) in the absence of hydrogen molecule. Device current is significantly increased from 0.2 nA to 2.4 µA (approx.) when ssDNA chain is heavily doped with hydrogen molecule. The current–voltage characteristics confirm the formation of various Boolean logic gate operations.Inspec keywords: molecular electronics, Green''s function methods, hydrogen, logic gates, density functional theory, adsorption, DNA, logic design, logic circuitsOther keywords: hydrogen molecule, contrary sparse cloud, current–voltage characteristics, Boolean logic gate operations, first principle approach, logic design, hydrogen‐doped single‐strand DNA, molecular logic gate, density functional theory, electronic transmission characteristics, H, analytical model, NXOR logic gates, Ex‐OR logic gates, OR logic gates, hydrogen‐doped‐ssDNA chain, nonequilibrium Green''s function, nucleobases, dense electron cloud, improved electronic transmission, enhanced logic properties, H‐ssDNA device, enhanced quantum‐ballistic transmission, gold electrode     

Formal reasoning about synthetic biology using higher‐order‐logic theorem proving

Synthetic biology is an interdisciplinary field that uses well‐established engineering principles for performing the analysis of the biological systems, such as biological circuits, pathways, controllers and enzymes. Conventionally, the analysis of these biological systems is performed using paper‐and‐pencil proofs and computer simulation methods. However, these methods cannot ensure accurate results due to their inherent limitations. Higher‐order‐logic (HOL) theorem proving is proposed and used as a complementary approach for analysing linear biological systems, which is based on developing a mathematical model of the genetic circuits and the bio‐controllers used in synthetic biology based on HOL and analysing it using deductive reasoning in an interactive theorem prover. The involvement of the logic, mathematics and the deductive reasoning in this method ensures the accuracy of the analysis. It is proposed to model the continuous dynamics of the genetic circuits and their associated controllers using differential equations and perform their transfer function‐based analysis using the Laplace transform in a theorem prover. For illustration, the genetic circuits of activated and repressed expressions and autoactivation of protein, and phase lag and lead controllers, which are widely used in cancer‐cell identifiers and multi‐input receptors for precise disease detection, are formally analyzed.Inspec keywords: program verification, diseases, genetics, cancer, formal logic, theorem proving, formal verification, differential equations, proteins, transfer functions, inference mechanisms, Laplace transformsOther keywords: biological system, biological circuits, genetic circuits, associated controllers, computer simulation methods, higher‐order‐logic theorem proving, analysing linear biological systems, bio‐controllers, synthetic biology, deductive reasoning, reaction‐based models, transfer function based analysis, differential equation based models, phase lag, lead controllers, computer systems     

Dynamics and robustness of the cardiac progenitor cell induced pluripotent stem cell network during cell phenotypes transition

Robustness is a fundamental characteristic of biological systems since all living systems need to adapt to internal or external perturbations, unpredictable environments, stochastic events and unreliable components, and so on. A long‐term challenge in systems biology is to reveal the origin of robustness underlying molecular regulator network. In this study, a simple Boolean model is used to investigate the global dynamic properties and robustness of cardiac progenitor cell (CPC) induced pluripotent stem cell network that governs reprogramming and directed differentiation process. It is demonstrated that two major attractors correspond to source and target cell phenotypes, respectively, and two dominating attracting trajectories characterise the biological pathways between two major cell phenotypes. In particular, the experimentally observed transition between different cell phenotypes can be reproduced and explained theoretically. Furthermore, the robustness of major attractors and trajectories is largely maintained with respect to small perturbations to the network. Taken together, the CPC‐induced pluripotent stem cell network is extremely robustly designed for their functions.Inspec keywords: cellular biophysics, Boolean functions, perturbation theory, molecular biophysics, cardiologyOther keywords: cardiac progenitor cell induced pluripotent stem cell network, cell phenotypes transition, biological systems, living systems, internal perturbations, external perturbations, unpredictable environments, stochastic events, unreliable components, long‐term challenge, systems biology, molecular regulator network, Boolean model, global dynamic properties, directed differentiation process, CPC‐induced pluripotent stem cell network     

Identifying genuine protein–protein interactions within communities of gene co‐expression networks using a deconvolution method

Direct relationships between biological molecules connected in a gene co‐expression network tend to reflect real biological activities such as gene regulation, protein–protein interactions (PPIs), and metabolisation. As correlation‐based networks contain numerous indirect connections, those direct relationships are always ‘hidden’ in them. Compared with the global network, network communities imply more biological significance on predicting protein function, detecting protein complexes and studying network evolution. Therefore, identifying direct relationships in communities is a pervasive and important topic in the biological sciences. Unfortunately, this field has not been well studied. A major thrust of this study is to apply a deconvolution algorithm on communities stemming from different gene co‐expression networks, which are constructed by fixing different thresholds for robustness analysis. Using the fifth Dialogue on Reverse Engineering Assessment and Methods challenge (DREAM5) framework, the authors demonstrate that nearly all new communities extracted from a ‘deconvolution filter’ contain more genuine PPIs than before deconvolution.Inspec keywords: proteins, deconvolution, genetics, bioinformatics, biology computing, molecular biophysicsOther keywords: identifying genuine protein–protein interactions, gene co‐expression network, deconvolution method, direct relationships, biological molecules, biological activities, gene regulation, correlation‐based networks, numerous indirect connections, global network, network communities, biological significance, protein function, protein complexes, studying network evolution, biological sciences, different gene co‐expression networks     

Dichotomous feedback: a signal sequestration-based feedback mechanism for biocontroller design

We introduce a new design framework for implementing negative feedback regulation in synthetic biology, which we term ‘dichotomous feedback’. Our approach is different from current methods, in that it sequesters existing fluxes in the process to be controlled, and in this way takes advantage of the process’s architecture to design the control law. This signal sequestration mechanism appears in many natural biological systems and can potentially be easier to realize than ‘molecular sequestration’ and other comparison motifs that are nowadays common in biomolecular feedback control design. The loop is closed by linking the strength of signal sequestration to the process output. Our feedback regulation mechanism is motivated by two-component signalling systems, where a second response regulator could be competing with the natural response regulator thus sequestering kinase activity. Here, dichotomous feedback is established by increasing the concentration of the second response regulator as the level of the output of the natural process increases. Extensive analysis demonstrates how this type of feedback shapes the signal response, attenuates intrinsic noise while increasing robustness and reducing crosstalk.     

Properties of neuronal facilitation that improve target tracking in natural pursuit simulations

Although flying insects have limited visual acuity (approx. 1°) and relatively small brains, many species pursue tiny targets against cluttered backgrounds with high success. Our previous computational model, inspired by electrophysiological recordings from insect ‘small target motion detector’ (STMD) neurons, did not account for several key properties described from the biological system. These include the recent observations of response ‘facilitation’ (a slow build-up of response to targets that move on long, continuous trajectories) and ‘selective attention’, a competitive mechanism that selects one target from alternatives. Here, we present an elaborated STMD-inspired model, implemented in a closed loop target-tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. We test this system against heavily cluttered natural scenes. Inclusion of facilitation not only substantially improves success for even short-duration pursuits, but it also enhances the ability to ‘attend’ to one target in the presence of distracters. Our model predicts optimal facilitation parameters that are static in space and dynamic in time, changing with respect to the amount of background clutter and the intended purpose of the pursuit. Our results provide insights into insect neurophysiology and show the potential of this algorithm for implementation in artificial visual systems and robotic applications.     

Design of synthetic biological logic circuits based on evolutionary algorithm

The construction of an artificial biological logic circuit using systematic strategy is recognised as one of the most important topics for the development of synthetic biology. In this study, a real‐structured genetic algorithm (RSGA), which combines general advantages of the traditional real genetic algorithm with those of the structured genetic algorithm, is proposed to deal with the biological logic circuit design problem. A general model with the cis ‐regulatory input function and appropriate promoter activity functions is proposed to synthesise a wide variety of fundamental logic gates such as NOT, Buffer, AND, OR, NAND, NOR and XOR. The results obtained can be extended to synthesise advanced combinational and sequential logic circuits by topologically distinct connections. The resulting optimal design of these logic gates and circuits are established via the RSGA. The in silico computer‐based modelling technology has been verified showing its great advantages in the purpose.Inspec keywords: biocomputing, biological techniques, combinational circuits, genetic algorithms, logic design, logic gates, sequential circuitsOther keywords: in silico computer‐based modelling, RSGA, sequential logic circuits, XOR gates, NOR gates, NAND gates, OR gates, AND gates, Buffer gates, NOT gates, fundamental logic gates, cis‐regulatory input function, real‐structured genetic algorithm, artiflcial biological logic circuit design     

Network morphospace

The structure of complex networks has attracted much attention in recent years. It has been noted that many real-world examples of networked systems share a set of common architectural features. This raises important questions about their origin, for example whether such network attributes reflect common design principles or constraints imposed by selectional forces that have shaped the evolution of network topology. Is it possible to place the many patterns and forms of complex networks into a common space that reveals their relations, and what are the main rules and driving forces that determine which positions in such a space are occupied by systems that have actually evolved? We suggest that these questions can be addressed by combining concepts from two currently relatively unconnected fields. One is theoretical morphology, which has conceptualized the relations between morphological traits defined by mathematical models of biological form. The second is network science, which provides numerous quantitative tools to measure and classify different patterns of local and global network architecture across disparate types of systems. Here, we explore a new theoretical concept that lies at the intersection between both fields, the ‘network morphospace’. Defined by axes that represent specific network traits, each point within such a space represents a location occupied by networks that share a set of common ‘morphological’ characteristics related to aspects of their connectivity. Mapping a network morphospace reveals the extent to which the space is filled by existing networks, thus allowing a distinction between actual and impossible designs and highlighting the generative potential of rules and constraints that pervade the evolution of complex systems.     

CMOS/nano co-design for crossbar-based molecular electronic systems

Future electronic systems will need to adopt novel nanoelectronic solutions to keep pace with Moore's Law. Crossbar-based molecular electronics are among the most promising of nanotechnologies. However, circuits similar to the conventional mainstream electronics of today will have a presence in future complex systems for some time. This paper presents a circuit paradigm where silicon and molecular electronics are integrated. We discuss methods for realizing memory and logic using nanoscale crossbars as well as for interfacing the crossbars to CMOS circuitry. Using custom nanoscale device models, we perform circuit simulation and analysis of the crossbar circuits and the peripheral CMOS circuitry. Finally, we present a design methodology to accompany the CMOS/nano paradigm.     

Asymptotic burnout and homeostatic awakening: a possible solution to the Fermi paradox?

Previous studies show that city metrics having to do with growth, productivity and overall energy consumption scale superlinearly, attributing this to the social nature of cities. Superlinear scaling results in crises called ‘singularities’, where population and energy demand tend to infinity in a finite amount of time, which must be avoided by ever more frequent ‘resets’ or innovations that postpone the system''s collapse. Here, we place the emergence of cities and planetary civilizations in the context of major evolutionary transitions. With this perspective, we hypothesize that once a planetary civilization transitions into a state that can be described as one virtually connected global city, it will face an ‘asymptotic burnout’, an ultimate crisis where the singularity-interval time scale becomes smaller than the time scale of innovation. If a civilization develops the capability to understand its own trajectory, it will have a window of time to affect a fundamental change to prioritize long-term homeostasis and well-being over unyielding growth—a consciously induced trajectory change or ‘homeostatic awakening’. We propose a new resolution to the Fermi paradox: civilizations either collapse from burnout or redirect themselves to prioritizing homeostasis, a state where cosmic expansion is no longer a goal, making them difficult to detect remotely.     

Evolution on neutral networks accelerates the ticking rate of the molecular clock

Large sets of genotypes give rise to the same phenotype, because phenotypic expression is highly redundant. Accordingly, a population can accept mutations without altering its phenotype, as long as the genotype mutates into another one on the same set. By linking every pair of genotypes that are mutually accessible through mutation, genotypes organize themselves into neutral networks (NNs). These networks are known to be heterogeneous and assortative, and these properties affect the evolutionary dynamics of the population. By studying the dynamics of populations on NNs with arbitrary topology, we analyse the effect of assortativity, of NN (phenotype) fitness and of network size. We find that the probability that the population leaves the network is smaller the longer the time spent on it. This progressive ‘phenotypic entrapment’ entails a systematic increase in the overdispersion of the process with time and an acceleration in the fixation rate of neutral mutations. We also quantify the variation of these effects with the size of the phenotype and with its fitness relative to that of neighbouring alternatives.     

Stochastic analysis of biochemical reaction networks with absolute concentration robustness

It has recently been shown that structural conditions on the reaction network, rather than a ‘fine-tuning’ of system parameters, often suffice to impart ‘absolute concentration robustness’ (ACR) on a wide class of biologically relevant, deterministically modelled mass-action systems. We show here that fundamentally different conclusions about the long-term behaviour of such systems are reached if the systems are instead modelled with stochastic dynamics and a discrete state space. Specifically, we characterize a large class of models that exhibit convergence to a positive robust equilibrium in the deterministic setting, whereas trajectories of the corresponding stochastic models are necessarily absorbed by a set of states that reside on the boundary of the state space, i.e. the system undergoes an extinction event. If the time to extinction is large relative to the relevant timescales of the system, the process will appear to settle down to a stationary distribution long before the inevitable extinction will occur. This quasi-stationary distribution is considered for two systems taken from the literature, and results consistent with ACR are recovered by showing that the quasi-stationary distribution of the robust species approaches a Poisson distribution.     

An analytical approach to bistable biological circuit discrimination using real algebraic geometry

Biomolecular circuits with two distinct and stable steady states have been identified as essential components in a wide range of biological networks, with a variety of mechanisms and topologies giving rise to their important bistable property. Understanding the differences between circuit implementations is an important question, particularly for the synthetic biologist faced with determining which bistable circuit design out of many is best for their specific application. In this work we explore the applicability of Sturm''s theorem—a tool from nineteenth-century real algebraic geometry—to comparing ‘functionally equivalent’ bistable circuits without the need for numerical simulation. We first consider two genetic toggle variants and two different positive feedback circuits, and show how specific topological properties present in each type of circuit can serve to increase the size of the regions of parameter space in which they function as switches. We then demonstrate that a single competitive monomeric activator added to a purely monomeric (and otherwise monostable) mutual repressor circuit is sufficient for bistability. Finally, we compare our approach with the Routh–Hurwitz method and derive consistent, yet more powerful, parametric conditions. The predictive power and ease of use of Sturm''s theorem demonstrated in this work suggest that algebraic geometric techniques may be underused in biomolecular circuit analysis.     

Testing the reproducibility and robustness of the cancer biology literature by robot

Scientific results should not just be ‘repeatable’ (replicable in the same laboratory under identical conditions), but also ‘reproducible’ (replicable in other laboratories under similar conditions). Results should also, if possible, be ‘robust’ (replicable under a wide range of conditions). The reproducibility and robustness of only a small fraction of published biomedical results has been tested; furthermore, when reproducibility is tested, it is often not found. This situation is termed ‘the reproducibility crisis'', and it is one the most important issues facing biomedicine. This crisis would be solved if it were possible to automate reproducibility testing. Here, we describe the semi-automated testing for reproducibility and robustness of simple statements (propositions) about cancer cell biology automatically extracted from the literature. From 12 260 papers, we automatically extracted statements predicted to describe experimental results regarding a change of gene expression in response to drug treatment in breast cancer, from these we selected 74 statements of high biomedical interest. To test the reproducibility of these statements, two different teams used the laboratory automation system Eve and two breast cancer cell lines (MCF7 and MDA-MB-231). Statistically significant evidence for repeatability was found for 43 statements, and significant evidence for reproducibility/robustness in 22 statements. In two cases, the automation made serendipitous discoveries. The reproduced/robust knowledge provides significant insight into cancer. We conclude that semi-automated reproducibility testing is currently achievable, that it could be scaled up to generate a substantive source of reliable knowledge and that automation has the potential to mitigate the reproducibility crisis.     

Biology by design: reduction and synthesis of cellular components and behaviour.

Biological research is experiencing an increasing focus on the application of knowledge rather than on its generation. Thanks to the increased understanding of cellular systems and technological advances, biologists are more frequently asking not only ‘how can I understand the structure and behaviour of this biological system?’, but also ‘how can I apply that knowledge to generate novel functions in different biological systems or in other contexts?’ Active pursuit of the latter has nurtured the emergence of synthetic biology. Here, we discuss the motivation behind, and foundational technologies enabling, the development of this nascent field. We examine some early successes and applications while highlighting the challenges involved. Finally, we consider future directions and mention non-scientific considerations that can influence the field''s growth.     

The feasibility of coherent energy transfer in microtubules

It was once purported that biological systems were far too ‘warm and wet’ to support quantum phenomena mainly owing to thermal effects disrupting quantum coherence. However, recent experimental results and theoretical analyses have shown that thermal energy may assist, rather than disrupt, quantum coherent transport, especially in the ‘dry’ hydrophobic interiors of biomolecules. Specifically, evidence has been accumulating for the necessary involvement of quantum coherent energy transfer between uniquely arranged chromophores in light harvesting photosynthetic complexes. The ‘tubulin’ subunit proteins, which comprise microtubules, also possess a distinct architecture of chromophores, namely aromatic amino acids, including tryptophan. The geometry and dipolar properties of these aromatics are similar to those found in photosynthetic units indicating that tubulin may support coherent energy transfer. Tubulin aggregated into microtubule geometric lattices may support such energy transfer, which could be important for biological signalling and communication essential to living processes. Here, we perform a computational investigation of energy transfer between chromophoric amino acids in tubulin via dipole excitations coupled to the surrounding thermal environment. We present the spatial structure and energetic properties of the tryptophan residues in the microtubule constituent protein tubulin. Plausibility arguments for the conditions favouring a quantum mechanism of signal propagation along a microtubule are provided. Overall, we find that coherent energy transfer in tubulin and microtubules is biologically feasible.