Acta Biotheoretica最新文献

Bacteria are divided primarily into monoderms (with one cell membrane, and usually Gram-positive, due to a thick peptidoglycan layer) and diderms (with two cell membranes, and mostly Gram-negative, due to a thin peptidoglycan layer sandwiched between the two membranes). Photosynthetic species are spread among the taxonomic groups, some having type I reaction centers (RCI in monoderm phylum Firmicutes; and diderm phyla Acidobacteria and Chlorobi), others with type II reaction centers (RCII in monoderm phylum Chloroflexi; and diderm taxa Gemmatimonadetes, and alpha-, beta-, and gamma-Proteobacteria), and some containing both (RCI and RCII, only in diderm phylum Cyanobacteria). In most bacterial phylograms, photosystem types and diderm taxa are polyphyletic. A more parsimonious arrangement, which is supported by photosystem evolution, as well as additional sets of molecular characters, suggests that endosymbiotic events resulted in the formation of the diderms. In the model presented, monoderms readily form a monophyletic group, while diderms are produced by at least two endosymbiotic events, followed by additional evolutionary changes.

Evolution in modern life requires high replication fidelity to allow for natural selection. A simulation model utilizing simulated phenotype data on cellular probability of survival was developed to determine how self-replication fidelity could evolve in early life. The results indicate that initial survivability and replication fidelity both contribute to overall fitness as measured by growth rates of the cell population. Survival probability was the more dominant feature, and evolution was possible even with zero replication fidelity. A derived formula for the relationship of survival probability and replication fidelity with growth rate was consistent with the simulated empirical data. Quantitative assessment of continuity and other evidence was obtained for a saltation (non-continuous) evolutionary process starting from low to moderate levels of survival probability and self-replication fidelity to reach the high levels seen in modern life forms.

Does natural selection favor veridical percepts—those that accurately (if not exhaustively) depict objective reality? Perceptual and cognitive scientists standardly claim that it does. Here we formalize this claim using the tools of evolutionary game theory and Bayesian decision theory. We state and prove the “Fitness-Beats-Truth (FBT) Theorem” which shows that the claim is false: If one starts with the assumption that perception involves inference to states of the objective world, then the FBT Theorem shows that a strategy that simply seeks to maximize expected-fitness payoff, with no attempt to estimate the “true” world state, does consistently better. More precisely, the FBT Theorem provides a quantitative measure of the extent to which the fitness-only strategy dominates the truth strategy, and of how this dominance increases with the size of the perceptual space. The FBT Theorem supports the Interface Theory of Perception (e.g. Hoffman et al. in Psychon Bull Rev https://doi.org/10.3758/s13423-015-0890-8, 2015), which proposes that our perceptual systems have evolved to provide a species-specific interface to guide adaptive behavior, and not to provide a veridical representation of objective reality.

The static properties of leaves with parallel venation from terrestrial orchids of the genus Epipactis were modelled as coupled elastic rods using the geometrically exact Cosserat theory and the resulting boundary-value problem was solved numerically using a method from Shampine, Muir and Xu. The response of the leaf structure to the applied force was obtained from preliminary measurements. These measurements allowed the Young’s modulus of the Epipactis leaves to be determined. The appearance of wrinkles and undulation characteristics for some leaves has been attributed to the small torsional stiffness of the leaf edges.

The emphasis on the organization of entities and their activities and interactions has been labeled one of the most distinct contributions of mechanistic philosophy. In this paper I discuss the manner in which the organization of entities and their activities and interactions participates in bringing about phenomena. I present a well-known example from molecular biology—the functioning of the genetic switch in phage lambda—and discuss Marco J. Nathan’s notion of causation by concentration. Nathan introduces causation by concentration to account for the irreducible causal role that the concentration ratio between two kinds of proteins possesses in the genetic switch mechanism in phage lambda. I discuss what the irreducibility of this causal role amounts to and provide a mechanistic interpretation of Nathan’s causation by concentration; that is, I explain this irreducible causal role as one organizational feature of this mechanism. The paper concludes that biological mechanisms need a causal pluralist framework [similar to Glennan’s account in (2009), (2010) and (2017) but slightly modified] where organizational features such as the concentration ratio have a causally relevant role, yet all the causally productive relations occur at the level of entities or individuals.

In this paper, we adopt a physiological perspective in order to produce an intelligible overview of biological transmission in all its diversity. This allows us to put forward the analysis of transmission mechanisms, with the aim of complementing the usual focus on transmitted factors. We underline the importance of the structural, dynamical, and functional features of transmission mechanisms throughout organisms’ life cycles in order to answer to the question of what is passed on across generations, how and why. On this basis, we propose a vision of biological transmission as networks of heterogeneous physiological mechanisms, not restricted to transmission mechanisms stricto sensu. They prove to be themselves suited candidates for evolutionary explanations. They are processes both necessary for evolution to happen and resulting themselves from evolution. This leads us to call for a strategy of endogenization to account for transmission, and more specifically inheritance, as evolved and evolving physiological mechanisms.

The risk of cigarette smoking plays a pivotal role in increasing the incidence rates of lung cancer. This paper sheds new light on modeling the impact of cigarette smoking on lung cancer evolution, especially genetic instability and the number of gene mutations in the genome of stem cells. To handle this issue, we have set up stochastic multi-stage models to fit the data set of the probabilities of current and former smokers from the Nurses’ Health Study cohort of females (NHS) and the Health Professionals Follow up Study cohort of men (HPFS). Throughout this paper, we consider both mutation rates and clonal expansion rates as parameters in each compartment. For current and former smokers, three-driver mutations are most likely to take place in the progression of lung cancer under smoking risk. For current smokers, our findings reveal that two to sixteen gene mutations are required to obtain a cancerous cell among men and women in US. Moreover, two to six (eleven) cancer-mutations are available in the pathway to lung cancer among former smokers who have quit smoking for more (less) than ten years for both male and female patients. This highlights that cigarette smoking stimulates the number of driver mutations during lung tumorigenesis in both sexes. It is very crucial to examine the role of cigarette smoking in determining whether genomic instability is an early stage or late stage in the process of lung carcinogenesis.

Thresholds for disease extinction provide essential information for the prevention and control of diseases. In this paper, a stochastic epidemic model, a continuous-time Markov chain, for the transmission dynamics of West Nile virus in birds is developed based on the assumptions of its analogous deterministic model. The branching process is applied to derive the extinction threshold for the stochastic model and conditions for disease extinction or persistence. The probability of disease extinction computed from the branching process is shown to be in good agreement with the probability approximated from numerical simulations. The disease dynamics of both models are compared to ascertain the effect of demographic stochasticity on West Nile virus dynamics. Analytical and numerical results show differences in model predictions and asymptotic dynamics between stochastic and deterministic models that are crucial for the prevention of disease outbreaks. It is found that there is a high probability of disease extinction if the disease emerges from exposed mosquitoes unlike if it emerges from infectious mosquitoes and birds. Finite-time to disease extinction is estimated using sample paths and it is shown that the epidemic duration is shortest if the disease is introduced by exposed mosquitoes.

We investigate the epistemological consequences of a positive polymerase chain reaction SARS-CoV test for two relevant hypotheses: (i) V is the hypothesis that an individual has been infected with SARS-CoV-2; (ii) C is the hypothesis that SARS-CoV-2 is the cause of flu-like symptoms in a given patient. We ask two fundamental epistemological questions regarding each hypothesis: First, how much confirmation does a positive test lend to each hypothesis? Second, how much evidence does a positive test provide for each hypothesis against its negation? We respond to each question within a formal Bayesian framework. We construe degree of confirmation as the difference between the posterior probability of the hypothesis and its prior, and the strength of evidence for a hypothesis against its alternative in terms of their likelihood ratio. We find that test specificity—and coinfection probabilities when making inferences about C—were key determinants of confirmation and evidence. Tests with < 87% specificity could not provide strong evidence (likelihood ratio > 8) for V against ¬V regardless of sensitivity. Accordingly, low specificity tests could not provide strong evidence in favor of C in all plausible scenarios modeled. We also show how a positive influenza A test disconfirms C and provides weak evidence against C in dependence on the probability that the patient is influenza A infected given that his/her symptoms are not caused by SARS-CoV-2. Our analysis points out some caveats that should be considered when attributing symptoms or death of a positively tested patient to SARS-CoV-2.