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Energy dissipation through physical downscaling towards more complex types of memory and logic devices, loss of ultrapure water and consumption of large amounts of (toxic) chemicals for wafer cleaning processes, as well as high thermal budget of solid-state synthesis and thin film growth of standard semiconductors including the use of rare earth elements – all this poses great challenges for semiconductor materials science and technology. Therefore, research and development of alternative methods for micro- and nanofabrication and chemical functionalization of a new type of resource- and energy-efficient semiconductors as the core component of every computer chip is crucial. One of the promising opportunities is the transformation of today's complementary metal-oxide-semiconductor (CMOS) electronics into ecofriendly and neuroinspired electronics driven by molecular design and multi-level switching mechanisms at room temperature. The sustainable chemical technology of electron transport and switching materials in semiconductor manufacturing and the development of devices with new unconventional nanophysics, improved performance, and augmented functionalities (beyond-CMOS and More-than-Moore) is becoming increasingly important in the context of a gradual transition to a future-oriented concept of Internet of Everything (IoE). In this article, we focus on the technological significance of semiconductor preparation from single-source (molecular) precursors and the prospect of functionalizing semiconductors using DNA origami nanotechnology and stimuli-responsive metal–oxygen cluster ions such as polyoxometalates (POMs). We also describe the advanced characterization of these qualified molecular systems by soft X-rays. We emphasize the technical relevance of using solution-based methods for the bottom-up preparation of novel and hybrid semiconductors as well as their challenging scalability and the compatibility of methods of molecular technology with lithography-based mass production. Our article aims to contribute to the achievement of the United Nations' Sustainable Development Goal 9 (Industry, Innovation and Infrastructure). [ABSTRACT FROM AUTHOR]

Ubiquitination is an essential post-translational modification mechanism involving the ubiquitin protein's bonding to a substrate protein. It is crucial in a variety of physiological activities including cell survival and differentiation, and innate and adaptive immunity. Any alteration in the ubiquitin system leads to the development of various human diseases. Numerous researches show the highly reversibility and dynamic of ubiquitin system, making the experimental identification quite difficult. To solve this issue, this article develops a model using a machine learning approach, tending to improve the ubiquitin protein prediction precisely. We deeply investigate the ubiquitination data that is proceed through different features extraction methods, followed by the classification. The evaluation and assessment are conducted considering Jackknife tests and 10-fold cross-validation. The proposed method demonstrated the remarkable performance in terms of 100 %, 99.88 %, and 99.84 % accuracy on Dataset-I, Dataset-II, and Dataset-III, respectively. Using Jackknife test, the method achieves 100 %, 99.91 %, and 99.99 % for Dataset-I, Dataset-II and Dataset-III, respectively. This analysis concludes that the proposed method outperformed the state-of-the-arts to identify the ubiquitination sites and helpful in the development of current clinical therapies. The source code and datasets will be made available at Github.

In the mid-1930s, the English mathematician and logician Alan Turing invented an imaginary machine which could emulate the process of manipulating finite symbolic configurations by human computers. His machine launched the field of computer science and provided a foundation for the modern-day programmable computer. A decade later, building on Turing's machine, the American-Hungarian mathematician John von Neumann invented an imaginary self-reproducing machine capable of open-ended evolution. Through his machine, von Neumann answered one of the deepest questions in Biology: Why is it that all living organisms carry a self-description in the form of DNA? The story behind how two pioneers of computer science stumbled on the secret of life many years before the discovery of the DNA double helix is not well known, not even to biologists, and you will not find it in biology textbooks. Yet, the story is just as relevant today as it was eighty years ago: Turing and von Neumann left a blueprint for studying biological systems as if they were computing machines. This approach may hold the key to answering many remaining questions in Biology and could even lead to advances in computer science. [ABSTRACT FROM AUTHOR]

Evolution-based processes have been widely applied in bioengineering as well as in cellular computing. For example, different approaches for protein evolution have been proposed. Moreover, implementations of evolutionary heuristics to solve optimisation problems using cellular populations have been demonstrated. However, heuristics implemented with cellular populations to optimise their own response, have not yet been reported. Here we present a heuristic optimisation framework that integrates a programmable synthetic evolution into a cellular population. The proposed evolution is based on the automatic selection of computing parts to execute a given objective. These parts are implemented in the form of plasmids, which are randomly distributed among a cellular population. Further evolution of their distribution is guided by a fitness function integrated within each cell in the population. While high values of fitness functions stimulate the propagation of computing parts composing optimal solutions through the population, low fitness values trigger the apoptosis of a cell. We provide a theoretical implementation of the framework in which we demonstrate the programmable evolution of different functions with different levels of complexity. To the best of our knowledge, our approach describes the first synthetic evolution framework for programmable self-optimisation of cellular populations. It requires little human intervention without a requirement to specify the exact implementation of a biological function the population should perform. Namely, the designer only needs to define the response the population should obtain and does not need to know how this response will be implemented. The proposed computational framework is available at https://github.com/mmoskon/evolution. [ABSTRACT FROM AUTHOR]

Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) are expressive , performing diverse multi-input computations. Further, dimerization networks are versatile , performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough (≥8 proteins), can perform nearly all (~90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing., Competing Interests: Declaration of Interests M.B.E. is a scientific advisory board member or consultant at TeraCyte, Plasmidsaurus, and Spatial Genomics.

The idea of computational processes, which take place in nature, for example, DNA computation, is discussed in the literature. DNA computation that is going on in the immunoglobulin locus of vertebrates shows how the computations in the biological possibility space could operate during evolution. We suggest that the origin of evolutionarily novel genes and genome evolution constitute the original intrinsic computation of the information about new structures in the space of unrealized biological possibilities. Due to DNA computation, the information about future structures is generated and stored in DNA as genetic information. In evolving ontogenies, search algorithms are necessary, which can search for information about evolutionary innovations and morphological novelties. We believe that such algorithms include stochastic gene expression, gene competition, and compatibility search at different levels of structural organization. We formulate the increase in complexity principle in terms of biological computation and hypothesize the possibility of in silico computing of future functions of evolutionarily novel genes. [ABSTRACT FROM AUTHOR]

Probabilistic inference—the process of estimating the values of unobserved variables in probabilistic models—has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially "predictive" behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing. [ABSTRACT FROM AUTHOR]

Fitness landscapes are a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other through mutation and related through fitness. Empirical studies of fitness landscapes have increasingly revealed conserved topographical features across diverse taxa, e.g., the accessibility of genotypes and "ruggedness". As a result, theoretical studies are needed to investigate how evolution proceeds on fitness landscapes with such conserved features. Here, we develop and study a model of evolution on fitness landscapes using the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as phenotypes. With the assumption that regulation is a binary process, we prove the existence of empirically observed, topographical features such as accessibility and connectivity. We further show that these results hold across arbitrary fitness functions and that a trade-off between accessibility and ruggedness need not exist. Then, using graph theory and a coarse-graining approach, we deduce a mesoscopic structure underlying GRN fitness landscapes where the information necessary to predict a population's evolutionary trajectory is retained with minimal complexity. Using this coarse-graining, we develop a bottom-up algorithm to construct such mesoscopic backbones, which does not require computing the genotype network and is therefore far more efficient than brute-force approaches. Altogether, this work provides mathematical results of high-dimensional fitness landscapes and a path toward connecting theory to empirical studies. [ABSTRACT FROM AUTHOR]

The original computers were people using algorithms to get mathematical results such as rocket trajectories. After the invention of the digital computer, brains have been widely understood through analogies with computers and now artificial neural networks, which have strengths and drawbacks. We define and examine a new kind of computation better adapted to biological systems, called biological computation, a natural adaptation of mechanistic physical computation. Nervous systems are of course biological computers, and we focus on some edge cases of biological computing, hearts and flytraps. The heart has about the computing power of a slug, and much of its computing happens outside of its forty thousand neurons. The flytrap has about the computing power of a lobster ganglion. This account advances fundamental debates in neuroscience by illustrating ways that classical computability theory can miss complexities of biology. By this reframing of computation, we make way for resolving the disconnect between human and machine learning. [ABSTRACT FROM AUTHOR]

Abstract Synthetic biology aims at engineering gene regulatory circuits to end with cells (re)programmed on purpose to implement novel functions or discover natural behaviors. However, one overlooked question is whether the resulting circuits perform as intended in variety of environments or with time. Here, we considered a recently engineered genetic system that allows programming the cell to work as a minimal computer (arithmetic logic unit) in order to analyze its operability regime. This system involves transcriptional and post-transcriptional regulations. In particular, we studied the analog behavior of the system, the effect of physicochemical changes in the environment, the impact on cell growth rate of the heterologous expression, and the ability to maintain the arithmetic functioning over time. Conclusively, our results suggest 1) that there are wide input concentration ranges that the system can correctly process, the resulting outputs being predictable with a simple mathematical model, 2) that the engineered circuitry is quite sensitive to temperature effects, 3) that the expression of heterologous small RNAs is costly for the cell, not only of heterologous proteins, and 4) that a proper genetic reorganization of the system to reduce the amount of heterologous DNA in the cell can improve its evolutionary stability.

Biological computation involves the design and development of computational techniques inspired by natural biota. On the other hand, computational biology involves the development and application of computational techniques to study biological systems. We present a comprehensive review showcasing how biology and computer science can guide and benefit each other, resulting in improved understanding of biological processes and at the same time advances in the design of algorithms. Unfortunately, integration between biology and computer science is often challenging, especially due to the cultural idiosyncrasies of these two communities. In this study, we aim at highlighting how nature has inspired the development of various algorithms and techniques in computer science, and how computational techniques and mathematical modeling have helped to better understand various fields in biology. We identified existing gaps between biological computation and computational biology and advocate for bridging this gap between "wet" and "dry" research. [ABSTRACT FROM AUTHOR]

Fitness landscapes are a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other through mutation and related through fitness. Empirical studies of fitness landscapes have increasingly revealed conserved topographical features across diverse taxa, e.g., the accessibility of genotypes and “ruggedness”. As a result, theoretical studies are needed to investigate how evolution proceeds on fitness landscapes with such conserved features. Here, we develop and study a model of evolution on fitness landscapes using the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as phenotypes. With the assumption that regulation is a binary process, we prove the existence of empirically observed, topographical features such as accessibility and connectivity. We further show that these results hold across arbitrary fitness functions and that a trade-off between accessibility and ruggedness need not exist. Then, using graph theory and a coarse-graining approach, we deduce a mesoscopic structure underlying GRN fitness landscapes where the information necessary to predict a population’s evolutionary trajectory is retained with minimal complexity. Using this coarse-graining, we develop a bottom-up algorithm to construct such mesoscopic backbones, which does not require computing the genotype network and is therefore far more efficient than brute-force approaches. Altogether, this work provides mathematical results of high-dimensional fitness landscapes and a path toward connecting theory to empirical studies.

Probabilistic inference—the process of estimating the values of unobserved variables in probabilistic models—has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially “predictive” behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing.

What information-processing strategies and general principles are sufficient to enable self-organized morphogenesis in embryogenesis and regeneration? We designed and analyzed a minimal model of self-scaling axial patterning consisting of a cellular network that develops activity patterns within implicitly set bounds. The properties of the cells are determined by internal ‘genetic’ networks with an architecture shared across all cells. We used machine-learning to identify models that enable this virtual mini-embryo to pattern a typical axial gradient while simultaneously sensing the set boundaries within which to develop it from homogeneous conditions—a setting that captures the essence of early embryogenesis. Interestingly, the model revealed several features (such as planar polarity and regenerative re-scaling capacity) for which it was not directly selected, showing how these common biological design principles can emerge as a consequence of simple patterning modes. A novel “causal network” analysis of the best model furthermore revealed that the originally symmetric model dynamically integrates into intercellular causal networks characterized by broken-symmetry, long-range influence and modularity, offering an interpretable macroscale-circuit-based explanation for phenotypic patterning. This work shows how computation could occur in biological development and how machine learning approaches can generate hypotheses and deepen our understanding of how featureless tissues might develop sophisticated patterns—an essential step towards predictive control of morphogenesis in regenerative medicine or synthetic bioengineering contexts. The tools developed here also have the potential to benefit machine learning via new forms of backpropagation and by leveraging the novel distributed self-representation mechanisms to improve robustness and generalization.

Synthetic biology aims at engineering gene regulatory circuits to end with cells (re)programmed on purpose to implement novel functions or discover natural behaviors. However, one overlooked question is whether the resulting circuits perform as intended in variety of environments or with time. Here, we considered a recently engineered genetic system that allows programming the cell to work as a minimal computer (arithmetic logic unit) in order to analyze its operability regime. This system involves transcriptional and post-transcriptional regulations. In particular, we studied the analog behavior of the system, the effect of physicochemical changes in the environment, the impact on cell growth rate of the heterologous expression, and the ability to maintain the arithmetic functioning over time. Conclusively, our results suggest 1) that there are wide input concentration ranges that the system can correctly process, the resulting outputs being predictable with a simple mathematical model, 2) that the engineered circuitry is quite sensitive to temperature effects, 3) that the expression of heterologous small RNAs is costly for the cell, not only of heterologous proteins, and 4) that a proper genetic reorganization of the system to reduce the amount of heterologous DNA in the cell can improve its evolutionary stability. [ABSTRACT FROM AUTHOR]

Diverse animal species exhibit highly stereotyped behavioral actions and locomotor sequences as they explore their natural environments. In many such cases, the neural basis of behavior is well established, where dedicated neural circuitry contributes to the initiation and regulation of certain response sequences. At the microscopic scale, single-celled eukaryotes (protists) also exhibit remarkably complex behaviors and yet are completely devoid of nervous systems. Here, to address the question of how single cells control behavior, we study locomotor patterning in the exemplary hypotrich ciliate Euplotes , a highly polarized cell, which actuates a large number of leg-like appendages called cirri (each a bundle of ∼25–50 cilia) to swim in fluids or walk on surfaces. As it navigates its surroundings, a walking Euplotes cell is routinely observed to perform side-stepping reactions, one of the most sophisticated maneuvers ever observed in a single-celled organism. These are spontaneous and stereotyped reorientation events involving a transient and fast backward motion followed by a turn. Combining high-speed imaging with simultaneous time-resolved electrophysiological recordings, we show that this complex coordinated motion sequence is tightly regulated by rapid membrane depolarization events, which orchestrate the activity of different cirri on the cell. Using machine learning and computer vision methods, we map detailed measurements of cirri dynamics to the cell's membrane bioelectrical activity, revealing a differential response in the front and back cirri. We integrate these measurements with a minimal model to understand how Euplotes —a unicellular organism—manipulates its membrane potential to achieve real-time control over its motor apparatus. [Display omitted] • Euplotes uses bundles of cilia (cirri) on its ventral surface to walk and swim • Forward movement is interrupted by highly coordinated turning maneuvers • Membrane depolarizations trigger turning with distinct cirri behaving differently • A minimal mechanical model maps cirri dynamics to locomotor gait Laeverenz-Schlogelhofer et al. combine high-speed imaging with simultaneous electrophysiological recordings in Euplotes , a single cell with leg-like appendages (cirri), to show how the cell's membrane potential coordinates cirri movement and dynamic transitions between whole-cell walking or turning. [ABSTRACT FROM AUTHOR]

Marine macro-algae, commonly known as "seaweed," are used in everyday commodity products worldwide for food, feed, and biostimulant for plants and animals and continue to be one of the conspicuous components of world aquaculture production. However, the application of ANN in seaweeds remains limited. Here, we described how to perform ANN-based machine learning modeling and GA-based optimization to enhance seedling production for implications on commercial farming. The critical steps from seaweed seedling explant preparation, selection of independent variables for laboratory culture, formulating experimental design, executing ANN Modelling, and implementing optimization algorithm are described., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The goal of this article is to call attention to, and to express caution about, the extensive use of computation as an explanatory concept in contemporary biology. Inspired by Dennett's 'intentional stance' in the philosophy of mind, I suggest that a 'computational stance' can be a productive approach to evaluating the value of computational concepts in biology. Such an approach allows the value of computational ideas to be assessed without being diverted by arguments about whether a particular biological system is 'actually computing' or not. Because there is sufficient difference of agreement among computer scientists about the essential elements that constitute computation, any doctrinaire position about the application of computational ideas seems misguided. Closely related to the concept of computation is the concept of information processing. Indeed, some influential computer scientists contend that there is no fundamental difference between the two concepts. I will argue that despite the lack of widely accepted, general definitions of information processing and computation: (1) information processing and computation are not fully equivalent and there is value in maintaining a distinction between them and (2) that such value is particularly evident in applications of information processing and computation to biology. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'. [ABSTRACT FROM AUTHOR]

Information processing and storage underpins many biological processes of vital importance to organism survival. Like animals, plants also acquire, store and process environmental information relevant to their fitness, and this is particularly evident in their decision-making. The control of plant organ growth and timing of their developmental transitions are carefully orchestrated by the collective action of many connected computing agents, the cells, in what could be addressed as distributed computation. Here, we discuss some examples of biological information processing in plants, with special interest in the connection to formal computational models drawn from theoretical frameworks. Research into biological processes with a computational perspective may yield new insights and provide a general framework for information processing across different substrates. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'. [ABSTRACT FROM AUTHOR]

Exact cover is a non-deterministic polynomial time (NP)—complete problem that is central to optimization challenges such as airline fleet planning and allocation of cloud computing resources. Solving exact cover requires the exploration of a solution space that increases exponentially with cardinality. Hence, it is time- and energy consuming to solve large instances of exact cover by serial computers. One approach to address these challenges is to utilize the inherent parallelism and high energy efficiency of biological systems in a network-based biocomputation (NBC) device. NBC is a parallel computing paradigm in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. The network is then explored in parallel using a large number of biological agents, such as molecular-motor-propelled protein filaments. The answer to the combinatorial problem can then be inferred by measuring the positions through which the agents exit the network. Here, we (i) show how exact cover can be encoded and solved in an NBC device, (ii) define a formalization that allows to prove the correctness of our approach and provides a mathematical basis for further studying NBC, and (iii) demonstrate various optimizations that significantly improve the computing performance of NBC. This work lays the ground for fabricating and scaling NBC devices to solve significantly larger combinatorial problems than have been demonstrated so far.

We consider the solution of the subset sum problem based on a parallel computer consisting of self-propelled biological agents moving in a nanostructured network that encodes the computing task in its geometry. We develop an approximate analytical method to analyze the effects of small errors in the nonideal junctions composing the computing network by using a Gaussian confidence interval approximation of the multinomial distribution. We concretely evaluate the probability distribution for error-induced paths and determine the minimal number of agents required to obtain a proper solution. We finally validate our theoretical results with exact numerical simulations of the subset sum problem for different set sizes and error probabilities, and discuss the scalability of the nonideal problem using realistic experimental error probabilities.

Biological organisms must perform computation as they grow, reproduce and evolve.Moreover, ever since Landauer's bound was proposed, it has been known that all computation has some thermodynamic cost--and that the same computation can be achieved with greater or smaller thermodynamic cost depending on how it is implemented. Accordingly an important issue concerning the evolution of life is assessing the thermodynamic efficiency of the computations performed by organisms. This issue is interesting both from the perspective of how close life has come to maximally efficient computation (presumably under the pressure of natural selection), and from the practical perspective of what efficiencies we might hope that engineered biological computers might achieve, especially in comparison with current computational systems. Here we show that the computational efficiency of translation, defined as free energy expended per amino acid operation, outperforms the best supercomputers by several orders of magnitude, and is only about an order of magnitude worse than the Landauer bound. However, this efficiency depends strongly on the size and architecture of the cell in question. In particular, we show that the useful efficiency of an amino acid operation, defined as the bulk energy per amino acid polymerization, decreases for increasing bacterial size and converges to the polymerization cost of the ribosome. This cost of the largest bacteria does not change in cells as we progress through the major evolutionary shifts to both single- and multicellular eukaryotes. However, the rates of total computation per unit mass are non-monotonic in bacteria with increasing cell size, and also change across different biological architectures, including the shift from unicellular to multicellular eukaryotes. This article is part of the themed issue 'Re-conceptualizing the origins of life'. [ABSTRACT FROM AUTHOR]

Many biological circuits comprise sets of protein variants that interact with one another in a many-to-many, or promiscuous, fashion. These architectures can provide powerful computational capabilities that are especially critical in multicellular organisms. Understanding the principles of biochemical computations in these circuits could allow more precise control of cellular behaviors. However, these systems are inherently difficult to analyze, due to their large number of interacting molecular components, partial redundancies, and cell context dependence. Here, we discuss recent experimental and theoretical advances that are beginning to reveal how promiscuous circuits compute, what roles those computations play in natural biological contexts, and how promiscuous architectures can be applied for the design of synthetic multicellular behaviors., Competing Interests: Declaration of interests M.B.E. is a scientific advisory board member or consultant at TeraCyte, Primordium, and Spatial Genomics. Y.E.A. is a scientific advisory board member or consultant at TeraCyte., (Copyright © 2023 Elsevier Inc. All rights reserved.)

DNA nanotechnology is a promising approach for engineering computation at the nanoscale, with potential applications in biofabrication and intelligent nanomedicine. DNA strand displacement is a general strategy for implementing a broad range of nanoscale computations, including any computation that can be expressed as a chemical reaction network. Modelling and analysis of DNA strand displacement systems is an important part of the design process, prior to experimental realisation. As experimental techniques improve, it is important for modelling languages to keep pace with the complexity of structures that can be realised experimentally. In this paper we present a process calculus for modelling DNA strand displacement computations involving rich secondary structures, including DNA branches and loops. We prove that our calculus is also sufficiently expressive to model previous work on non-branching structures, and propose a mapping from our calculus to a canonical strand graph representation, in which vertices represent DNA strands, ordered sites represent domains, and edges between sites represent bonds between domains. We define interactions between strands by means of strand graph rewriting, and prove the correspondence between the process calculus and strand graph behaviours. Finally, we propose a mapping from strand graphs to an efficient implementation, which we use to perform modelling and simulation of DNA strand displacement systems with rich secondary structure. [ABSTRACT FROM AUTHOR]

In 1972, Efim Liberman, a Soviet biophysicist, pioneered a brand-new approach to studying the operation of the brain, the live cell and the human mind by publishing a paper titled "Cell as a molecular computer" (1972). In this paper, Liberman posited that a consecutive/parallel stochastic molecular computer (MCC) controls a living cell. An MCC operates with molecule-words (DNA, RNA, proteins) according to the program recorded in DNA and RNA. Computational operations are implemented by molecular operators acting as enzymes. An MCC is present in each live cell. A neuron cell MCC can be involved in solving tasks for the entire organism. Neuron MCC investigation was started with studying an impact of an intracellular injection of cyclic AMP on electric activity of a neuron. Cyclic nucleotides were considered as input words for an MCC, which are generated inside a neuron as a result of synaptic activity. This led Efim Liberman to the idea that, in order to solve complex physical problems, which are encountered by a neuron and require rapid solutions, the molecular computer adjusts the operation of the quantum molecular regulator, which uses the "computational environment" of the cytoskeleton and quantum properties of the elementary hypersound quasiparticles for completing mathematical operations for the minimum price of action. Efim Liberman suggested that the human self-consciousness is a quantum computer of even a higher level and designated it as an extreme quantum regulator. In order to describe such systems, he suggested to join biology, physics and mathematics into a unified science, and formulated its four fundamental principles. Results of Efim Liberman's theoretical and experimental studies on the topic of biological computation are summarized in this review. [ABSTRACT FROM AUTHOR]

Efim A. Liberman (1925–2011) can be considered as a founder of the new field of science that explores natural computation and its limits. He named it Chaimatics and suggested its generalization to the ultimate all-encompassing theory that unites biology, physics and mathematics. He made a number of experimental discoveries, including color coding in the retina, the participation mechanisms of Ca2+ ions in synaptic transmission, and the measurement of potential in the coupling membranes of mitochondria and chloroplasts. He also made a decisive contribution to the proof of the chemiosmotic hypothesis of oxidative phosphorylation. In a series of works started in 1972, Liberman developed the concept of the molecular computer of the cell, which includes the programs written on DNA and RNA nucleotide sequences and executed by enzymes playing the role of processing units whereas nucleotide sequences are interpreted as commands and addresses. In this framework, Liberman predicted RNA splicing before its discovery and suggested the role of processing of small informational molecules (later defined as small RNAs) in controlling biological processes. Efim Liberman defined the fundamental property of life as a molecular and quantum computational system and introduced the idea of quantum computing inside a cell for making decisions on complex control tasks described by equations of mathematical physics. He approached the brain as a net of molecular computers and created a model of neuron operation based on the transmission of hypersound signals via cytoskeleton where the molecular computational system encodes the digital output. In 1979 Liberman published a hypothesis of human self-consciousness associated with not a chemical, but with a physical quantum coherent system and named it "extremal quantum regulator". We review here the contributions of Liberman in understanding the mechanisms of intracellular processing of information and his efforts to create an integrative theory of natural computation that aims to unite biology, physics and mathematics. [ABSTRACT FROM AUTHOR]