Morgan's Canon: Is it still a useful rule of thumb?

For over 100 years, Morgan's Canon has served as the criterion for distinguishing what appears to be complex cognitive processes shown by animals from simpler associative learning processes (Pavlovian and instrumental conditioning). Morgan's ( 1894 ) canon states “In no case is an animal activity to be interpreted in terms of higher psychological processes if it can be fairly interpreted in terms of processes which stand lower in the scale of psychological evolution and development.” In the present article, several examples are provided in which complex human‐like processes are proposed to have been demonstrated but the judicious use of Morgan's Canon suggests that simpler mechanisms may be sufficient to account for the behavior. The use of Morgan's Canon is not meant to reduce behavior to its lowest common denominator but rather to challenge investigators to develop procedures that can distinguish between simple behavioral principles and the more complex processes that cannot be explained in terms of genetics or simple conditioning. Whatever the results of these experiments, they should help identify the underlying processes and mechanisms involved.     

A likelihood approach to character weighting and what it tells us about parsimony and compatibility

The statistical framework of maximum likelihood estimation is used to examine character weighting in inferring phylogenies. A simple probabilistic model of evolution is used, in which each character evolves independently among two states, and different lineages evolve independently. When different characters have different known probabilities of change, all sufficiently small, the proper maximum likelihood method of estimating phylogenies is a weighted parsimony method in which the weights are logarithmically related to the rates of change. When rates of change are taken extremely small, the weights become more equal and unweighted parsimony methods are obtained.
When it is known that a few characters have very high rates of change and the rest very low rates, but it is not known which characters are the ones having the high rates, the maximum likelihood criterion supports use of compatibility methods. By varying the fraction of characters believed to have high rates of change one obtains a 'threshold method' whose behavior depends on the value of a parameter. By altering this parameter the method changes smoothly from being a parsimony method to being a compatibility method. This provides us with a spectrum of intermediates between these methods. These intermediate methods may be of use in analysing real data.     

A likelihood approach to character weighting and what it tells us about parsimony and compatibility

The statistical framework of maximum likelihood estimation is used to examine character weighting in inferring phylogenies. A simple probabilistic model of evolution is used, in which each character evolves independently among two states, and different lineages evolve independently. When different characters have different known probabilities of change, all sufficiently small, the proper maximum likelihood method of estimating phylogenies is a weighted parsimony method in which the weights are logarithmically related to the rates of change. When rates of change are taken extremely small, the weights become more equal and unweighted parsimony methods are obtained. When it is known that a few characters have very high rates of change and the rest very low rates, but it is not known which characters are the ones having the high rates, the maximum likelihood criterion supports use of compatibility methods. By varying the fraction of characters believed to have high rates of change one obtains a ‘threshold method’ whose behavior depends on the value of a parameter. By altering this parameter the method changes smoothly from being a parsimony method to being a compatibility method. This provides us with a spectrum of intermediates between these methods. These intermediate methods may be of use in analysing real data.     

On the incidence of intron loss and gain in paralogous gene families

Understanding gene duplication and gene structure evolution are fundamental goals of molecular evolutionary biology. A previous study by Babenko et al. (2004. Prevalence of intron gain over intron loss in the evolution of paralogous gene families. Nucleic Acids Res. 32:3724-3733) employed Dollo parsimony to infer spliceosomal intron losses and gains in paralogous gene families and concluded that there was a general excess of gains over losses. This result contrasts with patterns in orthologous genes, in which most lineages show an excess of intron losses over gains, suggesting the possibility of fundamentally different modes of intron evolution between orthologous and paralogous genes. We further studied the data and found a low level of intron position conservation with outgroups, and this led to problems with using Dollo parsimony to analyze the data. Statistical reanalysis of the data suggests, instead, that intron losses have outnumbered intron gains in paralogous gene families.     

Molecular phylogeny of the New World monkeys (Platyrrhini,primates) based on two unlinked nuclear genes: IRBP intron 1 and ϵ-globin sequences

Nuclear sequences of the 1.8 kilobase (kb) long intron 1 of the interstitial retinol-binding protein gene (IRBP), previously determined for 11 of the 16 extant genera of New World monkeys (superfamily Ceboidea, infraorder Platyrrhini), have now been determined for the remaining 5 genera. The maximum parsimony trees found, first with IRBP sequences alone and then with tandemly combined IRBP and ϵ-globin gene sequences from the same species, supported a provisional cladistic classification with the following clusters. Subtribes Callitrichina (Callithrix, Cebuella), Callimiconina (Callimico), Leontopithecina (Leontopithecus) and Saguina (Saguinus) constitute subfamily Callitrichinae, and subfamilies Callitrichinae, Aotinae (Aotus), and Cebinae (Cebus, Saimiri) constitute family Cebidae. Subtribes Chiropotina (Chiropotes, Cacajao) and Pitheciina (Pithecia) constitute tribe Pitheciini; and tribes Pitheciini and Callicebini (Callicebus) constitute subfamily Pitheciinae. Subtribes Brachytelina (Brachyteles, Lagothrix) and Atelina (Ateles) constitute tribe Atelini, and tribes Atelini and Alouattini (Alouatta) constitute subfamily Atelinae. The parsimony results were equivocal as to whether Pitheciinae should be grouped with Atelinae in family Atelidae or have its own family Pitheciidae. The cladistic groupings of extant ceboids were also examined by different stochastic evolutionary models that employed the same stochastic process of nucleotide substitutions but alternative putative phylogenetic trees on which the nucleotide substitutions occurred. Each model, i.e., each different tree, predicted a different multinomial distribution of nucleotide character patterns for the contemporary sequences. The predicted distributions that were closest to the actual observed distributions identified the best fitting trees. The cladistic relationships depicted in these best fitting trees agreed in almost all cases with those depicted in the maximum parsimony trees. © 1996 Wiley-Liss, Inc.     

Maximum likelihood outperforms maximum parsimony even when evolutionary rates are heterotachous

Heterotachy occurs when the relative evolutionary rates among sites are not the same across lineages. Sequence alignments are likely to exhibit heterotachy with varying severity because the intensity of purifying selection and adaptive forces at a given amino acid or DNA sequence position is unlikely to be the same in different species. In a recent study, the influence of heterotachy on the performance of different phylogenetic methods was examined using computer simulation for a four-species phylogeny. Maximum parsimony (MP) was reported to generally outperform maximum likelihood (ML). However, our comparisons of MP and ML methods using the methods and evaluation criteria employed in that study, but considering the possible range of proportions of sites involved in heterotachy, contradict their findings and indicate that, in fact, ML is significantly superior to MP even under heterotachy.     

Controlling organoid symmetry breaking uncovers an excitable system underlying human axial elongation

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An overview of mechanistic modeling of liquid chromatography

Abstract

Liquid chromatography is considered to be the bottleneck for purification of therapeutic proteins. Development and optimization of chromatography process is a cumbersome activity due to the increasing complexities in the types and content of impurities present in the high product titer cell culture harvest obtained from the upstream processing. Further, regulatory expectations are continuously rising with the recent initiatives of quality by design and process analytical technology expecting the manufacturer to have a deeper understanding of the process and the product. Mechanistic modeling is one approach to gain this deeper understanding of a process step. It involves modeling of the underlying physicochemical processes. A well calibrated model with acceptable predictability can be very effective in both process optimization and process characterization activities. In this paper we provide an overview of mechanistic modeling of liquid chromatography. We discuss the various components that such a model entails and also presents the status quo of this area.     

Mathematical properties and bounds on haplotyping populations by pure parsimony

Although the haplotype data can be used to analyze the function of DNA, due to the significant efforts required in collecting the haplotype data, usually the genotype data is collected and then the population haplotype inference (PHI) problem is solved to infer haplotype data from genotype data for a population. This paper investigates the PHI problem based on the pure parsimony criterion (HIPP), which seeks the minimum number of distinct haplotypes to infer a given genotype data. We analyze the mathematical structure and properties for the HIPP problem, propose techniques to reduce the given genotype data into an equivalent one of much smaller size, and analyze the relations of genotype data using a compatible graph. Based on the mathematical properties in the compatible graph, we propose a maximal clique heuristic to obtain an upper bound, and a new polynomial-sized integer linear programming formulation to obtain a lower bound for the HIPP problem.     

Improving the Development and Use of Biologically Based Dose Response Models (BBDR) in Risk Assessment

Biologically based dose-response (BBDR) models predict health outcomes (response) resulting from the presence of a toxicant at a biological target (dose). The benefits of BBDR models are many, and research programs are increasingly focusing on mechanistic research to support model development; however, progress has been slow. Impediments to progress include the complexity of dose response modeling, the need for a multidisciplinary team and consistent funding support, and difficulty in identifying and extracting the needed data. Of immediate concern is the lack of transparency of published models to the supporting data and literature, difficulty in accessing model code and simulation conditions sufficient to allow independent replication of results, and absence of well-defined quality criteria. Suggestions are presented to improve the development and use of BBDR models in risk assessment and to address the above limitations. Examples from BBDR models for methylmercury neurotoxicity and 5-fluorouracil embryotoxicity are presented to illustrate the suggestions including what kinds of databases are needed to support model development and transparency, quality assurance for modeling, and how the internet can advance database development and collaboration within the biological modeling community.