A physical characterization of biological information and communication system model of ecosystems

What is information for living organisms? An answer to this question is given on a physical basis and a contrast between genetic information and sensory information is stressed with a relation to information theory. A simple model of an environment of living organisms is investigated on the basis of communication systems model proposed by the author and a cost of information transmission is taken into consideration through capacity cost theory. It is shown that channel capacity of information theory can be interpreted as an environment, and furthermore that a large diversity of genetic messages needs a large capacity of the environment. In addition, a definition of life in terms of information is proposed and a unified view on life processes is suggested.     

Information theory and the ethylene genetic network

The original aim of the Information Theory (IT) was to solve a purely technical problem: to increase the performance of communication systems, which are constantly affected by interferences that diminish the quality of the transmitted information. That is, the theory deals only with the problem of transmitting with the maximal precision the symbols constituting a message. In Shannon''s theory messages are characterized only by their probabilities, regardless of their value or meaning. As for its present day status, it is generally acknowledged that Information Theory has solid mathematical foundations and has fruitful strong links with Physics in both theoretical and experimental areas. However, many applications of Information Theory to Biology are limited to using it as a technical tool to analyze biopolymers, such as DNA, RNA or protein sequences. The main point of discussion about the applicability of IT to explain the information flow in biological systems is that in a classic communication channel, the symbols that conform the coded message are transmitted one by one in an independent form through a noisy communication channel, and noise can alter each of the symbols, distorting the message; in contrast, in a genetic communication channel the coded messages are not transmitted in the form of symbols but signaling cascades transmit them. Consequently, the information flow from the emitter to the effector is due to a series of coupled physicochemical processes that must ensure the accurate transmission of the message. In this review we discussed a novel proposal to overcome this difficulty, which consists of the modeling of gene expression with a stochastic approach that allows Shannon entropy (H) to be directly used to measure the amount of uncertainty that the genetic machinery has in relation to the correct decoding of a message transmitted into the nucleus by a signaling pathway. From the value of H we can define a function I that measures the amount of information content in the input message that the cell''s genetic machinery is processing during a given time interval. Furthermore, combining Information Theory with the frequency response analysis of dynamical systems we can examine the cell''s genetic response to input signals with varying frequencies, amplitude and form, in order to determine if the cell can distinguish between different regimes of information flow from the environment. In the particular case of the ethylene signaling pathway, the amount of information managed by the root cell of Arabidopsis can be correlated with the frequency of the input signal. The ethylene signaling pathway cuts off very low and very high frequencies, allowing a window of frequency response in which the nucleus reads the incoming message as a varying input. Outside of this window the nucleus reads the input message as an approximately non-varying one. This frequency response analysis is also useful to estimate the rate of information transfer during the transport of each new ERF1 molecule into the nucleus. Additionally, application of Information Theory to analysis of the flow of information in the ethylene signaling pathway provides a deeper insight in the form in which the transition between auxin and ethylene hormonal activity occurs during a circadian cycle. An ambitious goal for the future would be to use Information Theory as a theoretical foundation for a suitable model of the information flow that runs at each level and through all levels of biological organization.Key words: information theory, shannon entropy, frequency systems analysis, Arabidopsis thaliana, ethylene signaling systems, plant genetic networks, circadian cycles     

Plant–insect chemical communication in ecological communities: An information theory perspective

Cross-species communication, where signals are sent by one species and perceived by others, is one of the most intriguing types of communication that functionally links different species to form complex ecological networks. Global change and human activity can affect communication by increasing fluctuations in species composition and phenology, altering signal profiles and intensity, and introducing noise. So far, most studies on cross-species communication have focused on a few specific species isolated from ecological communities. Scaling up investigations of cross-species communication to the community level is currently hampered by a lack of conceptual and practical methodologies. Here, we propose an interdisciplinary framework based on information theory to investigate mechanisms shaping cross-species communication at the community level. We use plants and insects, the cornerstones of most ecosystems, as a showcase and focus on chemical communication as the key communication channel. We first introduce some basic concepts of information theory, then we illustrate information patterns in plant–insect chemical communication, followed by a further exploration of how to integrate information theory into ecological and evolutionary processes to form testable mechanistic hypotheses. We conclude by highlighting the importance of community-level information as a means to better understand the maintenance and workings of ecological systems, especially during rapid global change.     

Random amino acid mutations and protein misfolding lead to Shannon limit in sequence-structure communication

The transmission of genomic information from coding sequence to protein structure during protein synthesis is subject to stochastic errors. To analyze transmission limits in the presence of spurious errors, Shannon's noisy channel theorem is applied to a communication channel between amino acid sequences and their structures established from a large-scale statistical analysis of protein atomic coordinates. While Shannon's theorem confirms that in close to native conformations information is transmitted with limited error probability, additional random errors in sequence (amino acid substitutions) and in structure (structural defects) trigger a decrease in communication capacity toward a Shannon limit at 0.010 bits per amino acid symbol at which communication breaks down. In several controls, simulated error rates above a critical threshold and models of unfolded structures always produce capacities below this limiting value. Thus an essential biological system can be realistically modeled as a digital communication channel that is (a) sensitive to random errors and (b) restricted by a Shannon error limit. This forms a novel basis for predictions consistent with observed rates of defective ribosomal products during protein synthesis, and with the estimated excess of mutual information in protein contact potentials.     

Communication systems in healthcare

The care of patients now almost inevitably seems to involve many different individuals, all needing to share patient information and discuss their management. As a consequence there is increasing interest in, and use of, information and communication technologies to support health services. Yet, while there is significant discussion of, and investment in, information technologies, communication systems receive much less attention and the clinical adoption of even simpler services like voice-mail or electronic mail is still not commonplace in many health services. There remain enormous gaps in our broad understanding of the role of communication services in health care delivery. Laboratory medicine is perhaps even more poorly studied than many other areas, such as the interface between primary care and hospital services. Given this lack of specific information about laboratory communication services, this paper will step back and generally review the components of a communication system, including the basic concepts of a communication channel, service, device and interaction mode. The review will then try and summarise some of what is known about specific communication problems that arise across health services in the main, including the community and hospital service delivery.     

By‐product information can stabilize the reliability of communication

Although communication underpins many biological processes, its function and basic definition remain contentious. In particular, researchers have debated whether information should be an integral part of a definition of communication and how it remains reliable. So far the handicap principle, assuming signal costs to stabilize reliable communication, has been the predominant paradigm in the study of animal communication. The role of by‐product information produced by mechanisms other than the communicative interaction has been neglected in the debate on signal reliability. We argue that by‐product information is common and that it provides the starting point for ritualization as the process of the evolution of communication. Second, by‐product information remains unchanged during ritualization and enforces reliable communication by restricting the options for manipulation and cheating. Third, this perspective changes the focus of research on communication from studying signal costs to studying the costs of cheating. It can thus explain the reliability of signalling in many communication systems that do not rely on handicaps. We emphasize that communication can often be informative but that the evolution of communication does not cause the evolution of information because by‐product information often predates and stimulates the evolution of communication. Communication is thus a consequence but not a cause of reliability. Communication is the interplay of inadvertent, informative traits and evolved traits that increase the stimulation and perception of perceivers. Viewing communication as a complex of inadvertent and derived traits facilitates understanding of the selective pressures shaping communication and those shaping information and its reliability. This viewpoint further contributes to resolving the current controversy on the role of information in communication.     

The disadvantage of combinatorial communication

Combinatorial communication allows rapid and efficient transfer of detailed information, yet combinatorial communication is used by few, if any, non-human species. To complement recent studies illustrating the advantages of combinatorial communication, we highlight a critical disadvantage. We use the concept of information value to show that deception poses a greater and qualitatively different threat to combinatorial signalling than to non-combinatorial systems. This additional potential for deception may represent a strategic barrier that has prevented widespread evolution of combinatorial communication. Our approach has the additional benefit of drawing clear distinctions among several types of deception that can occur in communication systems.     

Information retrieval for patient care.

Doctors need clinical information during most consultations with patients, and much of this need could be satisfied by material from online sources. Advances in data communication technologies mean that multimedia information can be transported rapidly to various clinical care locations. However, selecting the few items of information likely to be useful in a particular clinical situation from the mass of information available is a major problem. Current information retrieval systems are designed primarily for use in research rather than clinical care. The design, implementation, and critical evaluation of new information retrieval systems for clinical care should be guided by knowledgeable clinical users.     

Theory of molecular machines. I. Channel capacity of molecular machines

Like macroscopic machines, molecular-sized machines are limited by their material components, their design, and their use of power. One of these limits is the maximum number of states that a machine can choose from. The logarithm to the base 2 of the number of states is defined to be the number of bits of information that the machine could "gain" during its operation. The maximum possible information gain is a function of the energy that a molecular machine dissipates into the surrounding medium (Py), the thermal noise energy which disturbs the machine (Ny) and the number of independently moving parts involved in the operation (dspace): Cy = dspace log2 [( Py + Ny)/Ny] bits per operation. This "machine capacity" is closely related to Shannon's channel capacity for communications systems. An important theorem that Shannon proved for communication channels also applies to molecular machines. With regard to molecular machines, the theorem states that if the amount of information which a machine gains is less than or equal to Cy, then the error rate (frequency of failure) can be made arbitrarily small by using a sufficiently complex coding of the molecular machine's operation. Thus, the capacity of a molecular machine is sharply limited by the dissipation and the thermal noise, but the machine failure rate can be reduced to whatever low level may be required for the organism to survive.     

Information in morphological characters

The construction of morphological character matrices is central to paleontological systematic study, which extracts paleontological information from fossils. Although the word information has been repeatedly mentioned in a wide array of paleontological systematic studies, its meaning has rarely been clarified nor specifically defined. It is important, however, to establish a standard to measure paleontological information because fossils are hardly complete, rendering the recognition of homologous and homoplastic structures difficult. Here, based on information theory, we show the deep connections between paleontological systematic study and communication system engineering. Information is defined as the decrease of uncertainty and it is the information in morphological characters that allows distinguishing operational taxonomic units (OTUs) and reconstructing evolutionary history. We propose that concepts in communication system engineering such as source coding and channel coding, correspond to the construction of diagnostic features and the entire character matrices in paleontological studies. The two coding strategies should be distinguished following typical communication system engineering, because they serve dual purposes. With character matrices from six different vertebrate groups, we analyzed their information properties including source entropy, mutual information, and channel capacity. Estimation of channel capacity shows character saturation of all matrices in transmitting paleontological information, indicating that, due to the presence of noise, oversampling characters not only increases the burden in character scoring, but also may decrease quality of matrices. We further test the use of information entropy, which measures how informative a variable is, as a character weighting criterion in parsimony‐based systematic studies. The results show high consistency with existing knowledge with both good resolution and interpretability.     

THE EVOLUTION OF INDIVIDUAL VARIATION IN COMMUNICATION STRATEGIES

Communication is a process in which senders provide information via signals and receivers respond accordingly. This process relies on two coevolving conventions: a “sender code” that determines what kind of signal is to be sent given the sender's state; and a “receiver code” that determines the appropriate responses to different signal types. By means of a simple but generic model, we show that polymorphic sender and receiver strategies emerge naturally during the evolution of communication, and that the number of alternative strategies observed at equilibrium depends on the potential for error in signal production. Our model suggests that alternative communication strategies will evolve whenever senders possess imperfect information about their own quality or state, signals are costly, and genetic mechanisms allow for a correlation between sender and receiver behavior. These findings provide an explanation for recent reports of individual differences in communication strategies, and suggest that the amount of individual variation that can be expected in communication systems depends on the type of information being conveyed. Our model also suggests a link between communication and the evolution of animal personalities, which is that individual differences in the production and interpretation of signals can result in consistent differences in behavior.     

Social complexity as a proximate and ultimate factor in communicative complexity

The 'social complexity hypothesis' for communication posits that groups with complex social systems require more complex communicative systems to regulate interactions and relations among group members. Complex social systems, compared with simple social systems, are those in which individuals frequently interact in many different contexts with many different individuals, and often repeatedly interact with many of the same individuals in networks over time. Complex communicative systems, compared with simple communicative systems, are those that contain a large number of structurally and functionally distinct elements or possess a high amount of bits of information. Here, we describe some of the historical arguments that led to the social complexity hypothesis, and review evidence in support of the hypothesis. We discuss social complexity as a driver of communication and possible causal factor in human language origins. Finally, we discuss some of the key current limitations to the social complexity hypothesis-the lack of tests against alternative hypotheses for communicative complexity and evidence corroborating the hypothesis from modalities other than the vocal signalling channel.     

Cry-wolf signals emerging from coevolutionary feedbacks in a tritrophic system

For a communication system to be stable, senders should convey honest information. Providing dishonest information, however, can be advantageous to senders, which imposes a constraint on the evolution of communication systems. Beyond single populations and bitrophic systems, one may ask whether stable communication systems can evolve in multitrophic systems. Consider cross-species signalling where herbivore-induced plant volatiles (HIPVs) attract predators to reduce the damage from arthropod herbivores. Such plant signals may be honest and help predators to identify profitable prey/plant types via HIPV composition and to assess prey density via the amount of HIPVs. There could be selection for dishonest signals that attract predators for protection from possible future herbivory. Recently, we described a case in which plants release a fixed, high amount of HIPVs independent of herbivore load, adopting what we labelled a ‘cry-wolf’ strategy. To understand when such signals evolve, we model coevolutionary interactions between plants, herbivores and predators, and show that both ‘honest’ and ‘cry-wolf’ types can emerge, depending on the assumed plant–herbivore encounter rates and herbivore population density. It is suggested that the ‘cry-wolf’ strategy may have evolved to reduce the risk of heavy damage in the future. Our model suggests that eco-evolutionary feedback loops involving a third species may have important consequences for the stability of this outcome.     

Superposition properties of interacting ion channels.

Quantitative analysis of patch clamp data is widely based on stochastic models of single-channel kinetics. Membrane patches often contain more than one active channel of a given type, and it is usually assumed that these behave independently in order to interpret the record and infer individual channel properties. However, recent studies suggest there are significant channel interactions in some systems. We examine a model of dependence in a system of two identical channels, each modeled by a continuous-time Markov chain in which specified transition rates are dependent on the conductance state of the other channel, changing instantaneously when the other channel opens or closes. Each channel then has, e.g., a closed time density that is conditional on the other channel being open or closed, these being identical under independence. We relate the two densities by a convolution function that embodies information about, and serves to quantify, dependence in the closed class. Distributions of observable (superposition) sojourn times are given in terms of these conditional densities. The behavior of two channel systems based on two- and three-state Markov models is examined by simulation. Optimized fitting of simulated data using reasonable parameters values and sample size indicates that both positive and negative cooperativity can be distinguished from independence.     

Robustness and complexity co-constructed in multimodal signalling networks

In animal communication, signals are frequently emitted using different channels (e.g. frequencies in a vocalization) and different modalities (e.g. gestures can accompany vocalizations). We explore two explanations that have been provided for multimodality: (i) selection for high information transfer through dedicated channels and (ii) increasing fault tolerance or robustness through multichannel signals. Robustness relates to an accurate decoding of a signal when parts of a signal are occluded. We show analytically in simple feed-forward neural networks that while a multichannel signal can solve the robustness problem, a multimodal signal does so more effectively because it can maximize the contribution made by each channel while minimizing the effects of exclusion. Multimodality refers to sets of channels where within each set information is highly correlated. We show that the robustness property ensures correlations among channels producing complex, associative networks as a by-product. We refer to this as the principle of robust overdesign. We discuss the biological implications of this for the evolution of combinatorial signalling systems; in particular, how robustness promotes enough redundancy to allow for a subsequent specialization of redundant components into novel signals.     

70% efficiency of bistate molecular machines explained by information theory,high dimensional geometry and evolutionary convergence

The relationship between information and energy is key to understanding biological systems. We can display the information in DNA sequences specifically bound by proteins by using sequence logos, and we can measure the corresponding binding energy. These can be compared by noting that one of the forms of the second law of thermodynamics defines the minimum energy dissipation required to gain one bit of information. Under the isothermal conditions that molecular machines function this is joules per bit ( is Boltzmann''s constant and T is the absolute temperature). Then an efficiency of binding can be computed by dividing the information in a logo by the free energy of binding after it has been converted to bits. The isothermal efficiencies of not only genetic control systems, but also visual pigments are near 70%. From information and coding theory, the theoretical efficiency limit for bistate molecular machines is ln 2 = 0.6931. Evolutionary convergence to maximum efficiency is limited by the constraint that molecular states must be distinct from each other. The result indicates that natural molecular machines operate close to their information processing maximum (the channel capacity), and implies that nanotechnology can attain this goal.     

Information processing in brain microtubules

Models of the mind are based on the idea that neuron microtubules can perform computation. From this point of view, information processing is the fundamental issue for understanding the brain mechanisms that produce consciousness. The cytoskeleton polymers could store and process information through their dynamic coupling mediated by mechanical energy. We analyze the problem of information transfer and storage in brain microtubules, considering them as a communication channel. We discuss the implications of assuming that consciousness is generated by the subneuronal process.     

Ion-channel noise places limits on the miniaturization of the brain's wiring

The action potential (AP) is transmitted by the concerted action of voltage-gated ion channels. Thermodynamic fluctuations in channel proteins produce probabilistic gating behavior, causing channel noise. Miniaturizing signaling systems increases susceptibility to noise, and with many cortical, cerebellar, and peripheral axons <0.5 mum diameter [1, 2 and 3], channel noise could be significant [4 and 5]. Using biophysical theory and stochastic simulations, we investigated channel-noise limits in unmyelinated axons. Axons of diameter below 0.1 microm become inoperable because single, spontaneously opening Na channels generate spontaneous AP at rates that disrupt communication. This limiting diameter is relatively insensitive to variations in biophysical parameters (e.g., channel properties and density, membrane conductance and leak) and will apply to most spiking axons. We demonstrate that the essential molecular machinery can, in theory, fit into 0.06 microm diameter axons. However, a comprehensive survey of anatomical data shows a lower limit for AP-conducting axons of 0.08-0.1 microm diameter. Thus, molecular fluctuations constrain the wiring density of brains. Fluctuations have implications for epilepsy and neuropathic pain because changes in channel kinetics or axonal properties can change the rate at which channel noise generates spontaneous activity.     

Superposition properties of independent ion channels

Membrane patches usually contain several ion channels of a given type. However, most of the stochastic modelling on which data analysis (in particular, estimation of kinetic constants) is currently based, relates to a single channel rather than to multiple channels. Attempts to circumvent this problem experimentally by recording under conditions where channel activity is low are restrictive and can introduce bias; moreover, possibly important information on how multichannel systems behave will be missed. We have extended existing theory to multichannel systems by applying results from point process theory to derive some distributional properties of the various types of sojourn time that occur when a given number of channels are open in a system containing a specified number of independent channels in equilibrium. Separate development of properties of a single channel and the superposition of several such independent channels simplifies the presentation of known results and extensions. To illustrate the general theory, particular attention is given to the types of sojourn time that occur in a two channel system; detailed expressions are presented for a selection of models, both Markov and non-Markov.