Living things, the human genome, and patents.

For many years, patents and living things have not gotten along well in people's minds; nevertheless, patents are a real right. Even though people accept that a farmer can own a cow or that a Parisian can own a dog, they do not seem to understand that it is possible to patent a recombinant micro-organism or a DNA sequence. This is probably because industrial property is a hybrid concept, which mixes rights and science. Thus, this field is prone to misunderstanding by scientists and jurists, because of its juridical aspects for the former and because of its scientific aspects for the latter. The general public,as a result has two avenues of extravagant questioning to follow and consider. Finally, if industrial property is applied to a living phenomenon, it is often almost impossible to explain in layman's terms the ins and outs of the problems that may arise.     

Evolution and gradualness

Evolutionary systems are commonly considered to have two fundamental properties: (1) elements (or organisms) in the system reproduce with mutation and (2) only the fit elements survive. I propose that evolutionary systems have a third property — the property of gradualness. A system has gradualness, if, and only if, small changes in an element usually lead to small changes in that element's fitness.I have formalized a framework from which attempts to design evolutionary systems might proceed. Of particular importance are the criteria, based on the notion of perpetuation, which a system's behavior must satisfy in order to be considered evolutionary. By my standards, no computer programs have been designed that manifest meaningful evolutionary behavior.     

Systems biology and the origins of life? Part I. Are biochemical networks possible ancestors of living systems? Reproduction,identity and sensitivity to signals of biochemical networks

The set of these two theoretical papers offers an alternative to the hypothesis of a primordial RNA-world. The basic idea of these papers is to consider that the first prebiotic systems could have been networks of catalysed reactions encapsulated by a membrane. In order to test this hypothesis it was attempted to list the main obligatory features of living systems and see whether encapsulated biochemical networks could possibly display these features. The traits of living systems are the following: the ability they have to reproduce; the fact they possess an identity; the fact that biological events should be considered in the context of a history; the fact that living systems are able to evolve by selection of alterations of their structure and self-organization. The aim of these two papers is precisely to show that encapsulated biochemical networks can possess these properties and can be considered good candidates for the first prebiotic systems. In the present paper it is shown that if the proteinoids are not very specific catalysts and if some of the reactions of the network are autocatalytic whereas others are not, the resulting system does not reach a steady-state and tends to duplicate. In the same line, these biochemical networks possess an identity, viz. an information, defined from the probability of occurrence of these nodes. Moreover interaction of two ligands can increase, or decrease, this information. In the first case, the system is defined as emergent, in the second case it is considered integrated. Another property of living systems is that their behaviour is defined in the context of a time-arrow. For instance, they are able to sense whether the intensity of a signal is reached after an increase, or a decrease. This property can be mimicked by a simple physico-chemical system made up of the diffusion of a ligand followed by its chemical transformation catalysed by a proteinoid displaying inhibition by excess substrate. Under these conditions the system reacts differently depending on whether the same ligand concentration is reached after an increase or a decrease.     

Protecting wildlife from predation by owned domestic cats: Application of a precautionary approach to the acceptability of proposed cat regulations

Abstract While it is undeniable that owned domestic cats Felis catus (Mammalia: Felidae) kill large numbers of wildlife, it is contentious if this has significant impacts on wildlife populations. Under the precautionary principle such uncertainty does not preclude measures to reduce putative risk, but action should follow consultation with stakeholders. To initiate such consultation for the City of Armadale, Western Australia, we surveyed urban and rural residents to determine their opinions regarding putative impacts of owned cats on wildlife and the acceptability of proposed regulations. Key statements accepted by 70% or more of respondents, irrespective of their residence, gender or cat ownership status, included: (i) there is a need to regulate owned domestic cats; (ii) the presence of cats in nature reserves is harmful to wildlife; (iii) cats not owned by licensed breeders should be desexed; and (iv) local councils should be empowered to restrict the maximum number of cats per household. Seventy per cent or more of owners agreed to keep their cats on their property from sunset to sunrise and to register them if these measures became compulsory. All groups except urban men also indicated 70% or greater willingness to keep their cats on their property constantly if required. However, fewer than 40% of owners supported empowering local councils to enforce cat‐free zones. In this community, cat regulation excluding cat‐free zones should enjoy support. Similar approaches should be effective wherever the environmental impacts of owned domestic cats are debated, because compliance with such regulations should be high.     

Predicting range shifts under global change: the balance between local adaptation and dispersal

Bioclimate envelope models (BEMs) have often been criticized as being too simplistic due to e.g. not incorporating effects of biotic interactions or evolutionary adaptation. However, BEMs are widely applied and have proven to be often useful. Here we investigate, under which conditions evolution of dispersal, local adaptation or interspecific competition may be of minor importance for forecasting future range shifts. Therefore we use individual‐based simulations of metapopulations under climate change living in spatial temperature gradients. Scenarios incorporate single‐species systems or systems with competing species, respectively. Dispersal rate is evolving and adaptation to local conditions may also evolve in some scenarios. Results show that in single‐species scenarios excluding evolutionary adaptation, species either follow optimal habitat conditions or go extinct if habitat connectivity is too low. These simulations are in close accordance to predictions from BEMs. Including evolutionary adaptation qualitatively changes these results. In the absence of competing species the species either completely invades the world or goes extinct. With competitors, results strongly depend on habitat fragmentation. For highly connected habitats the range border may shift as predicted by BEMs, for intermediate connectivity it will lag behind, while species will go extinct if fragmentation is too high. Our results indicate that (simple) BEMs may work well if habitats are well connected and species will not encounter many difficulties in dispersing to new sites. Selection in this case may promote evolution of even higher dispersal activities. We thus show that the presence of biotic interactions may be ignored for predictions of range shifts when high dispersal can be expected.     

Extending the viability theory framework of resilience to uncertain dynamics,and application to lake eutrophication

Resilience, the capacity for a system to recover from a perturbation so as to keep its properties and functions, is of growing concern to a wide range of environmental systems. The challenge is often to render this concept operational without betraying it, nor diluting its content. The focus here is on building on the viability theory framework of resilience to extend it to discrete-time stochastic dynamical systems. The viability framework describes properties of the system as a subset of its state space. This property is resilient to a perturbation if it can be recovered and kept by the system after a perturbation: its trajectory can come back and stay in the subset. This is shown to reflect a general definition of resilience. With stochastic dynamics, the stochastic viability kernel describes the robust states, in which the system has a high probability of staying in the subset for a long time. Then, probability of resilience is defined as the maximal probability that the system reaches a robust state within a time horizon. Management strategies that maximize the probability of resilience can be found through dynamic programming. It is then possible to compute a range of statistics on the time for restoring the property. The approach is illustrated on the example of lake eutrophication and shown to foster the use of different indicators that are adapted to distinct situations. Its relevance for the management of ecological systems is also discussed.     

Diversity concept in ecology

Hierarchy of systems organization is used as a framework in advancing methodological guidelines for posing correct questions related to ecological diversity.Diversity if defined in general terms as a property of a set of elements dependent on and determines: by the epistemological perspective. Ontological diversity, because it is indefinite, is regarded as unmeasurable.Ecological diversity — be it species, spatial, reproductive or trophic, is a particular case of diversity of matter and must be precisely defined on the studied level of system organization.Diversity measurements combining more than one level of organization are information-void, for data become irretrievable as a result of such a treatment.Hypotheses explaining diversity sources are briefly surveyed. An integrated model interrelating them is constructed as a result of some basic views on the structure of matter coupled with the empirical knowledge of involved factors. The model reflects hierarchies of systems, their dynamics, and the complexities of factors affecting diversity. The model's properties suggest that it is conceptually related to an imaginary model of organization of ecological systems. Parameters recognized as invariant components of this complexity are: habitat differentiation, ecological specialization of species, ambiental changes, and integration of this system.Finally, it is shown that the peculiar shape of the species abundances curve is a necessary consequence of stratification of the system structure, which is an intrinsic attribute of hierarchical organization.     

Detergent-induced solubilization of cytochrome c oxidase as detected in a novel reconstituted system

A preparation of reconstituted cytochrome oxidase vesicles in which the enzyme is oriented facing inwards (such that it cannot interact with external cytochrome c) is described. No oxidase activity is expressed by these vesicles unless they are disrupted, allowing influx of cytochrome c or exposure of the oxidase-binding site to the external medium. We have exploited this property to follow detergent-induced solubilization of the membrane, a technique which allows membrane disruption and enzyme activity to be monitored simultaneously. This protocol can be employed to investigate the properties and mechanism of action of detergents as is illustrated for several ionic and nonionic detergents.     

On statistical inference for the random set generated Cox process with set-marking

Cox point process is a process class for hierarchical modelling of systems of non-interacting points in Rd under environmental heterogeneity which is modelled through a random intensity function. In this work a class of Cox processes is suggested where the random intensity is generated by a random closed set. Such heterogeneity appears for example in forestry where silvicultural treatments like harvesting and site-preparation create geometrical patterns for tree density variation in two different phases. In this paper the second order property, important both in data analysis and in the context of spatial sampling, is derived. The usefulness of the random set generated Cox process is highly increased, if for each point it is observed whether it is included in the random set or not. This additional information is easy and economical to obtain in many cases and is hence of practical value; it leads to marks for the points. The resulting random set marked Cox process is a marked point process where the marks are intensity-dependent. The problem with set-marking is that the marks are not a representative sample from the random set. This paper derives the second order property of the random set marked Cox process and suggests a practical estimation method for area fraction and covariance of the random set and for the point densities within and outside the random set. A simulated example and a forestry example are given.     

Control of kinetics by cooperative interactions

Cooperative effects in ligand binding and dissociation kinetics are much less investigated than steady state kinetics or equilibrium binding. Nevertheless, cooperativity in ligand binding leads necessarily to characteristic properties with respect to kinetic properties of the system. In case of positive cooperativity as found in oxygen binding proteins, a typical property is an autocatalytic ligand dissociation behavior leading to a time dependent, apparent ligand dissociation rate. To follow systematically the influence of the various potentially involved parameters on this characteristic property, simulations based on the simple MWC model were performed which should be relevant for all types of models based on the concept of an allosteric unit. In cases where the initial conformational distribution is very much dominated by the R-state, the intrinsic kinetic properties of the T-state are of minor influence for the observed ligand dissociation rate. Even for fast conformational transition rates, the R-state properties together with the size of the allosteric unit and the allosteric equilibrium constant define the shape of the curve. In such a case, a simplified model of the MWC-scheme (the irreversible n-chain model) is a good approximation of the full scheme. However, if in the starting conformational distribution some liganded T-molecules are present (a few percent is enough), the average off-rates can be significantly altered. Thus, the assignment of the initial rates to R-state properties has to be done with great care. However, if the R-state strongly dominates initially it is even possible to get an estimation of the lower limit for the number of interacting subunits from kinetic data: similar to the Hill-coefficient for equilibrium conditions, a measure for "kinetic cooperativity" can be derived by comparing initial and final ligand dissociation rates.     

From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0

This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific – it is what it is by how it differs from alternative experiences; integration says that it is unified – irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or logic gates, must be configured to generate experience (phenomenology). The postulates are used to define intrinsic information as “differences that make a difference” within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure (MICS, a constellation of concepts in qualia space), and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information Φ^Max its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true “zombies” – unconscious feed-forward systems that are functionally equivalent to conscious complexes.     

Conclusions via unique predictions obtained despite unidentifiability - new definitions and a general method

It is often predicted that model-based data analysis will revolutionize biology, just as it has physics and engineering. A widely used tool within such analysis is hypothesis testing, which focuses on model rejections. However, the fact that a systems biology model is non-rejected is often a relatively weak statement, as such models usually are highly over-parametrized with respect to the available data, and both parameters and predictions may therefore be arbitrarily uncertain. For this reason, we formally define and analyse the concept of a core prediction. A core prediction is a uniquely identified property that must be fulfilled if the given model structure is to explain the data, even if the individual parameters are non-uniquely identified. It is shown that such a prediction is as strong a conclusion as a rejection. Furthermore, a new method for core prediction analysis is introduced, which is beneficial for the uncertainty of specific model properties, as the method only characterizes the space of acceptable parameters in the relevant directions. This avoids the curse of dimensionality associated with the generic characterizations used by previously proposed methods. Analysis on examples shows that the new method is comparable to profile likelihood with regard to practical identifiability, and thus generalizes profile likelihood to the more general problem of observability. If used, the concepts and methods presented herein make it possible to distinguish between a conclusion and a mere suggestion, which hopefully will contribute to a more justified confidence in systems biology analyses.     

Consciousness: here,there and everywhere?

The science of consciousness has made great strides by focusing on the behavioural and neuronal correlates of experience. However, while such correlates are important for progress to occur, they are not enough if we are to understand even basic facts, for example, why the cerebral cortex gives rise to consciousness but the cerebellum does not, though it has even more neurons and appears to be just as complicated. Moreover, correlates are of little help in many instances where we would like to know if consciousness is present: patients with a few remaining islands of functioning cortex, preterm infants, non-mammalian species and machines that are rapidly outperforming people at driving, recognizing faces and objects, and answering difficult questions. To address these issues, we need not only more data but also a theory of consciousness—one that says what experience is and what type of physical systems can have it. Integrated information theory (IIT) does so by starting from experience itself via five phenomenological axioms: intrinsic existence, composition, information, integration and exclusion. From these it derives five postulates about the properties required of physical mechanisms to support consciousness. The theory provides a principled account of both the quantity and the quality of an individual experience (a quale), and a calculus to evaluate whether or not a particular physical system is conscious and of what. Moreover, IIT can explain a range of clinical and laboratory findings, makes a number of testable predictions and extrapolates to a number of problematic conditions. The theory holds that consciousness is a fundamental property possessed by physical systems having specific causal properties. It predicts that consciousness is graded, is common among biological organisms and can occur in some very simple systems. Conversely, it predicts that feed-forward networks, even complex ones, are not conscious, nor are aggregates such as groups of individuals or heaps of sand. Also, in sharp contrast to widespread functionalist beliefs, IIT implies that digital computers, even if their behaviour were to be functionally equivalent to ours, and even if they were to run faithful simulations of the human brain, would experience next to nothing.     

Entropy—enthalpy compensation: Fact or artifact?

The phenomenon of entropy–enthalpy (S‐H) compensation is widely invoked as an explanatory principle in thermodynamic analyses of proteins, ligands, and nucleic acids. It has been suggested that this compensation is an intrinsic property of either complex, fluctuating, or aqueous systems. The questions examined here are whether the observed compensation is extra‐thermodynamic (i.e., reflects anything more than the well‐known laws of statistical thermodynamics) and if so, what does it reveal about the system? Compensation is rather variably defined in the literature and different usages are discussed. The most precise and interesting one, which is considered here, is a linear relationship between ΔH and ΔS for some series of perturbations or changes in experimental variable. Some recent thermodynamic data on proteins purporting to show compensation is analyzed and shown to be better explained by other causes. A general statistical mechanical model of a complex system is analyzed to explore whether and under what conditions extra‐thermodynamic compensation can occur and what it reveals about the system. This model shows that the most likely behavior to be seen is linear S‐H compensation over a rather limited range of perturbations with a compensation temperature Tc = dΔH/dΔS within 20% of the experimental temperature. This behavior is insensitive to the details of the model, thus revealing little extra‐thermodynamic or causal information about the system. In addition, it will likely be difficult to distinguish this from more trivial forms of compensation in real experimental systems.     

Abstract biological systems as sequential machines II: Strong connectedness and reversibility

It was previously shown that the abstract biological systems called. (ℳ, ℛ)-systems could be regarded formally as sequential machines, and that when this was done, the reversibility of environmentally induced structural changes in these systems was closely related to the strong connectedness of the corresponding machines. In the present work it is shown that the sequential machines arising in this way are characterized by the property that the size of the input alphabet is very small compared with the size of the set of states of the machine. It is further shown that machines with this property almost always fail to be strongly connected. Therefore, it follows that one of the following alternatives holds: either most environmentally induced structural alterations are not environmentally reversible, or else many mappings in the category from which the (ℳ, ℛ)-systems are formed must not be physically realizable.     

Logarithmic and Power Law Input-Output Relations in Sensory Systems with Fold-Change Detection

Two central biophysical laws describe sensory responses to input signals. One is a logarithmic relationship between input and output, and the other is a power law relationship. These laws are sometimes called the Weber-Fechner law and the Stevens power law, respectively. The two laws are found in a wide variety of human sensory systems including hearing, vision, taste, and weight perception; they also occur in the responses of cells to stimuli. However the mechanistic origin of these laws is not fully understood. To address this, we consider a class of biological circuits exhibiting a property called fold-change detection (FCD). In these circuits the response dynamics depend only on the relative change in input signal and not its absolute level, a property which applies to many physiological and cellular sensory systems. We show analytically that by changing a single parameter in the FCD circuits, both logarithmic and power-law relationships emerge; these laws are modified versions of the Weber-Fechner and Stevens laws. The parameter that determines which law is found is the steepness (effective Hill coefficient) of the effect of the internal variable on the output. This finding applies to major circuit architectures found in biological systems, including the incoherent feed-forward loop and nonlinear integral feedback loops. Therefore, if one measures the response to different fold changes in input signal and observes a logarithmic or power law, the present theory can be used to rule out certain FCD mechanisms, and to predict their cooperativity parameter. We demonstrate this approach using data from eukaryotic chemotaxis signaling.     

“GREENCE” technology for <Emphasis Type="Italic">in silico</Emphasis> analysis of gene network dynamics in individual cells of a clonal population

The technology developed for in silico analysis of gene network behavior in a series of successive cell divisions makes it possible to obtain gene expression profiles in the cells of intermediate and final generations and to evaluate the relation among the cells heterogeneous by the functional states of gene subnetworks. Based on the model of a hypothetical gene network, which includes three cyclic digene systems with negative feedbacks, a new property of dynamic epigenes is confirmed, i.e., metastability of some epigenotypes, which earlier was predicted theoretically and found in experiments in vivo. A dynamic epigene is a cyclic system of genes with more than one inherited functional states, or epigenotypes. In a metastable state such a relation among repressors is set in a cell that, as a result of random distribution of the molecules and fluctuations of protein concentrations, subsequent divisions give daughter cells appear determined to alternative epigenotypes. It is shown in computer experiments that even systems in which dynamic epigenes are typical elements can possess this property.     

“GREENCE” technology for in silico analysis of gene network dynamics in individual cells of a clonal population

The technology developed for in silico analysis of gene network behavior in a series of successive cell divisions makes it possible to obtain gene expression profiles in the cells of intermediate and final generations and to evaluate the relation among the cells heterogeneous by the functional states of gene subnetworks. Based on the model of a hypothetical gene network, which includes three cyclic digene systems with negative feedbacks, a new property of dynamic epigenes is confirmed, i.e., metastability of some epigenotypes, which earlier was predicted theoretically and found in experiments in vivo. A dynamic epigene is a cyclic system of genes with more than one inherited functional states, or epigenotypes. In a metastable state such a relation among repressors is set in a cell that, as a result of random distribution of the molecules and fluctuations of protein concentrations, subsequent divisions give daughter cells appear determined to alternative epigenotypes. It is shown in computer experiments that even systems in which dynamic epigenes are typical elements can possess this property.

     

Effects of epistasis on phenotypic robustness in metabolic pathways

It is an open question whether phenomena such as phenotypic robustness to mutation evolve as adaptations or are simply an inherent property of genetic systems. As a case study, we examine this question with regard to dominance in metabolic physiology. Traditionally the conclusion that has been derived from Metabolic Control Analysis has been that dominance is an inevitable property of multi-enzyme systems and hence does not require an evolutionary explanation. This view is based on a mathematical result commonly referred to as the flux summation theorem. However it is shown here that for mutations involving finite changes (of any magnitude) in enzyme concentration, the flux summation theorem can only hold in a very restricted set of conditions. Using both analytical and simulation results we show that for finite changes, the summation theorem is only valid in cases where the relationship between genotype and phenotype is linear and devoid of non-linearities in the form of epistasis. Such an absence of epistasis is unlikely in metabolic systems. As an example, we show that epistasis can arise in scenarios where we assume generic non-linearities such as those caused by enzyme saturation. In such cases dominance levels can be modified by mutations that affect saturation levels. The implication is that dominance is not a necessary property of metabolic systems and that it can be subject to evolutionary modification.