The rational design of biological complexity: a deceptive metaphor

Biologists often claim that they follow a rational design strategy when their research is based on molecular knowledge of biological systems. This claim implies that their knowledge of the innumerable causal connections present in biological systems is sufficient to allow them to deduce and predict the outcome of their experimental interventions. The design metaphor is shown to originate in human intentionality and in the anthropomorphic fallacy of interpreting objects, events, and the behavior of all living organisms in terms of goals and purposes. Instead of presenting rational design as an effective research strategy, it would be preferable to acknowledge that advances in biomedicine are nearly always derived from empirical observations based on trial and error experimentation. The claim that rational design is an effective research strategy was tested in the case of current attempts to develop synthetic vaccines, in particular against human immunodeficiency virus. It was concluded that in this field of biomedicine, trial and error experimentation is more likely to succeed than a rational design approach. Current developments in systems biology may give us eventually a better understanding of the immune system and this may enable us in the future to develop improved vaccines.     

The Limitations of Kim’s Reductive Physicalism in Accounting for Living Systems and an Alternative Nonreductionist Ontology

Jaegwon Kim's exclusion argument is a general ontological argument, applicable to any properties deemed supervenient on a microproperty basis, including biological properties. It implies that the causal power of any higher-level property must be reducible to the subset of the causal powers of its lower-level properties. Moreover, as Kim's recent version of the argument indicates, a higher-level property can be causally efficient only to the extent of the efficiency of its micro-basis. In response, I argue that the ontology that aims to capture experimentally based explanations of metabolic control systems and morphogenetic systems must involve causally relevant contextual properties. Such an ontology challenges the exclusiveness of micro-based causal efficiency that grounds Kim's reductionism, since configurations themselves are inherently causally efficient constituents. I anticipate and respond to the reductionist's objection that the nonreductionist ontology's account of causes and inter-level causal relations is incoherent. I also argue that such an ontology is not open to Kim's overdetermination objection.     

Antigenicity and Immunogenicity of Synthetic Peptides

The ability of a peptide to react specifically with the functional binding site of a complementary antibody is known as its antigenic reactivity or antigenicity. Our understanding of peptide antigenicity has improved considerably in recent years mainly through the X-ray crystallographic analysis of peptide-monoclonal antibody complexes. This knowledge is obtained along reductionist lines by turning the biological question of antigen recognition into the purely chemical phenomenon of protein-peptide interactions described in terms of atomic forces and non-covalent bonds. This makes it possible to improve the degree of steric complementarity between a peptide and a single monoclonal antibody and thus to improve the peptide's antigenicity following structure-based rational design principles.The situation is quite different with immunogenicity which is the ability of the peptide to induce an immune response in a competent host. Whereas antigenicity can be reduced to the level of chemistry, such a reduction is not achievable in the case of immunogenicity which depends on many complex interactions with various elements of the host immune system. These cellular and regulatory mechanisms cannot be controlled by adjusting the structure of the peptide in a predetermined manner. For this reason, it is not possible to develop a synthetic peptide vaccine using molecular design principles.     

A Characterization of Children's Intuitive Theories of Drug Action

In an attempt to devise a methodology for characterizing children's intuitive theories of drug action, 217 children in Grades 1 to 6 were interviewed about how two substances, alcohol and cocaine, cause behavioral changes in their users. Measures tapped both structure (Piagetian complexity of causal reasoning, coherence, and construction of a causal explanatory framework with nodes, links, and causal mechanisms) and content (a relevant biological ontology, mastery of propositions in a scientifically correct theory of drug action, and reliance on alternative theoretical models). Scores on all measures increased with age, especially between first and second grade and third and fourth grade. Growth between third/fourth and fifth/sixth grades was more evident for cocaine, the less familiar of the two substances. Measures of structure and content were correlated. Overall, elementary school age children appear capable of framing causal explanations of drug action and possess relevant biological knowledge, particularly about the central role of the brain in mediating the effects of drugs on behavior. It remains to be seen whether programs guided by an intuitive theories perspective and designed to increase children's understandings of how drugs alter behavior can contribute meaningfully to drug prevention efforts.     

Getting the most out of Shannon information

Shannon information is commonly assumed to be the wrong way in which to conceive of information in most biological contexts. Since the theory deals only in correlations between systems, the argument goes, it can apply to any and all causal interactions that affect a biological outcome. Since informational language is generally confined to only certain kinds of biological process, such as gene expression and hormone signalling, Shannon information is thought to be unable to account for this restriction. It is often concluded that a richer, teleosemantic sense of information is needed. I argue against this view, and show that a coherent and sufficiently restrictive theory of biological information can be constructed with Shannon information at its core. This can be done by paying due attention some crucial distinctions: between information quantity and its fitness value, and between carrying information and having the function of doing so. From this I construct an account of how informational functions arise, and show that the “subject matter” of these functions can easily be seen as the natural information dealt with by Shannon’s theory.     

Survey of chemical speciation of trace elements using synchrotron radiation

Information concerning the chemical state of trace elements in biological systems generally has not been available. Such information for toxic elements and metals in metalloproteins could prove extremely valuable in the elucidation of their metabolism and other biological processes. The shielding of core electrons by binding electrons affect the energy required for creating inner-shell holes. Furthermore, the molecular binding and symmetry of the local environment of an atom affect the absorption spectrum in the neighborhood of the absorption edge. X-ray absorption near-edge structure (XANES) using synchrotron radiation excitation can be used to provide chemical speciation information for trace elements at concentrations as low as 10 ppm. The structure and position of the absorption curve in the region of an edge can yield vital data about the local structure and oxidation state of the trace element in question. Data are most easily interpreted by comparing the observed edge structure and position with those of model compounds of the element covering the entire range of possible oxidation states. Examples of such analyses will be reviewed.     

A systematic comparison of protein structure classifications: SCOP, CATH and FSSP.

BACKGROUND: Several methods of structural classification have been developed to introduce some order to the large amount of data present in the Protein Data Bank. Such methods facilitate structural comparisons and provide a greater understanding of structure and function. The most widely used and comprehensive databases are SCOP, CATH and FSSP, which represent three unique methods of classifying protein structures: purely manual, a combination of manual and automated, and purely automated, respectively. In order to develop reliable template libraries and benchmarks for protein-fold recognition, a systematic comparison of these databases has been carried out to determine their overall agreement in classifying protein structures. RESULTS: Approximately two-thirds of the protein chains in each database are common to all three databases. Despite employing different methods, and basing their systems on different rules of protein structure and taxonomy, SCOP, CATH and FSSP agree on the majority of their classifications. Discrepancies and inconsistencies are accounted for by a small number of explanations. Other interesting features have been identified, and various differences between manual and automatic classification methods are presented. CONCLUSIONS: Using these databases requires an understanding of the rules upon which they are based; each method offers certain advantages depending on the biological requirements and knowledge of the user. The degree of discrepancy between the systems also has an impact on reliability of prediction methods that employ these schemes as benchmarks. To generate accurate fold templates for threading, we extract information from a consensus database, encompassing agreements between SCOP, CATH and FSSP.     

Teleology and its constitutive role for biology as the science of organized systems in nature

'Nothing in biology makes sense, except in the light of teleology'. This could be the first sentence in a textbook about the methodology of biology. The fundamental concepts in biology, e.g. 'organism' and 'ecosystem', are only intelligible given a teleological framework. Since early modern times, teleology has often been considered methodologically unscientific. With the acceptance of evolutionary theory, one popular strategy for accommodating teleological reasoning was to explain it by reference to selection in the past: functions were reconstructed as 'selected effects'. But the theory of evolution obviously presupposes the existence of organisms as organized and regulated, i.e. functional systems. Therefore, evolutionary theory cannot provide the foundation for teleology. The underlying reason for the central methodological role of teleology in biology is not its potential to offer particular forms of (evolutionary) explanations for the presence of parts, but rather an ontological one: organisms and other basic biological entities do not exist as physical bodies do, as amounts of matter with a definite form. Rather, they are dynamic systems in stable equilibrium; despite changes of their matter and form (in metabolism and metamorphosis) they maintain their identity. What remains constant in these kinds of systems is their 'organization', i.e. the causal pattern of interdependence of parts with certain effects of each part being relevant for the working of the system. Teleological analysis consists in the identification of these system-relevant effects and at the same time of the system as a whole. Therefore, the identity of biological systems cannot be specified without teleological reasoning.     

On the number of experiments required to find the causal structure of complex systems

The need to capture the complexity of biological systems in a simpler formalism is the underlying impetus of biological sciences. Understanding the function of many biological complex systems, such as genetic networks or molecular signalling pathways, requires precise identification of the interactions between their individual components. A number of questions in the study of complex systems are then important-in particular, what can be inferred about the interactions in a complex system from an arbitrary set of experiments, and, what is the minimum number of experiments required to characterize the system? This paper shows that the problem of finding the minimal causal structure of a system based on a set of observations is computationally intractable for even moderately sized systems (it is NP-hard), but a reasonable approximation can be found in a relatively short (polynomial) time. Next, it is shown that the number of experiments required to characterize a complex system grows exponentially with the upper bound on the number of immediate upstream influences of each element, but only logarithmically with the number of elements in the system. This makes it possible to study biological systems with extremely large number of interacting elements and relatively sparse interconnections, such as gene regulatory and cell signalling networks. Finally, the construction of a randomized experimental sequence which achieves this bound is discussed.     

Identification of a binding site for the anti-inflammatory tripeptide feG

The mechanism of action of feG, an anti-inflammatory peptide, was explored using data mining, molecular modeling, and enzymatic techniques. The molecular coordinates of protein kinase A (PKA) were used to create six virtual isoforms of protein kinase C (PKCalpha, betaI, betaII, delta, iota, and zeta). With in silico techniques a binding site for feG was identified on PKCbetaI that correlated significantly with a biological activity, the inhibition of intestinal anaphylaxis. Since feG selectively increased the binding of a PKCbetaI antibody, it is proposed that this peptide inhibits the reassociation of the hydrophobic tail of PKCbetaI with its binding site and prevents the enzyme from assuming an inactive conformation.     

A framework for considering prior information in network-based approaches to omics data analysis

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.     

The cysteine proteome

The cysteine (Cys) proteome is a major component of the adaptive interface between the genome and the exposome. The thiol moiety of Cys undergoes a range of biologic modifications enabling biological switching of structure and reactivity. These biological modifications include sulfenylation and disulfide formation, formation of higher oxidation states, S-nitrosylation, persulfidation, metalation, and other modifications. Extensive knowledge about these systems and their compartmentalization now provides a foundation to develop advanced integrative models of Cys proteome regulation. In particular, detailed understanding of redox signaling pathways and sensing networks is becoming available to allow the discrimination of network structures. This research focuses attention on the need for atlases of Cys modifications to develop systems biology models. Such atlases will be especially useful for integrative studies linking the Cys proteome to imaging and other omics platforms, providing a basis for improved redox-based therapeutics. Thus, a framework is emerging to place the Cys proteome as a complement to the quantitative proteome in the omics continuum connecting the genome to the exposome.     

Mechanists Must be Holists Too! Perspectives from Circadian Biology

The pursuit of mechanistic explanations in biology has produced a great deal of knowledge about the parts, operations, and organization of mechanisms taken to be responsible for biological phenomena. Holist critics have often raised important criticisms of proposed mechanistic explanations, but until recently holists have not had alternative research strategies through which to advance explanations. This paper argues both that the results of mechanistic strategies has forced mechanists to confront ways in which whole systems affect their components and that new representational and modeling strategies are providing tools for understanding these effects of whole systems upon components. Drawing from research on the mechanism responsible for circadian rhythms in mammals, I develop two examples in which mechanistic analysis is being integrated into a more holist perspective: research revealing intercellular integration of circadian mechanisms with those involved in cell metabolism and research revealing that stable?rhythms are dependent on how individual cells in the suprachiasmatic nucleus synchronize with each other to generate regular rhythms. Tools such as network diagramming and computational modeling are providing means to integrate mechanistic models into accounts of whole systems.     

Anti-glycosyl antibodies: resolution of anti-gal isoantibodies into individual components by electrofocusing.

Rabbit anti-galactose isoantibodies which are specific for the terminal galactose units of a streptococcal antigenic diheteroglycan of glucose and galactose have been separated into individual components by a preparative electrofocusing technique. Ultracentrifugation and gel electrophoretic patterns indicate that each component consists of a single protein type of uniform molecular structure and size. Such antibody preparations will be of value for studies on the molecular structure of a natural antibody, on the nature of the bonds in an antigen-antibody complex and on the biological mechanism for the synthesis of antibodies.     

Enriched partial correlations in genome-wide gene expression profiles of hybrids (A. thaliana): a systems biological approach towards the molecular basis of heterosis

Heterosis is a well-known phenomenon but the underlying molecular mechanisms are not yet established. To contribute to the understanding of heterosis at the molecular level, we analyzed genome-wide gene expression profile data of Arabidopsis thaliana in a systems biological approach. We used partial correlations to estimate the global interaction structure of regulatory networks. Our hypothesis states that heterosis comes with an increased number of partial correlations which we interpret as increased numbers of regulatory interactions leading to enlarged adaptability of the hybrids. This hypothesis is true for mid-parent heterosis for our dataset of gene expression in two homozygous parental lines and their reciprocal crosses. For the case of best-parent heterosis just one hybrid is significant regarding our hypothesis based on a resampling analysis. Summarizing, both metabolome and gene expression level of our illustrative dataset support our proposal of a systems biological approach towards a molecular basis of heterosis.     

Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context

Background

Knowledge regarding causal relationships among traits is important to understand complex biological systems. Structural equation models (SEM) can be used to quantify the causal relations between traits, which allow prediction of outcomes to interventions applied to such a network. Such models are fitted conditionally on a causal structure among traits, represented by a directed acyclic graph and an Inductive Causation (IC) algorithm can be used to search for causal structures. The aim of this study was to explore the space of causal structures involving bovine milk fatty acids and to select a network supported by data as the structure of a SEM.

Results

The IC algorithm adapted to mixed models settings was applied to study 14 correlated bovine milk fatty acids, resulting in an undirected network. The undirected pathway from C4:0 to C12:0 resembled the de novo synthesis pathway of short and medium chain saturated fatty acids. By using prior knowledge, directions were assigned to that part of the network and the resulting structure was used to fit a SEM that led to structural coefficients ranging from 0.85 to 1.05. The deviance information criterion indicated that the SEM was more plausible than the multi-trait model.

Conclusions

The IC algorithm output pointed towards causal relations between the studied traits. This changed the focus from marginal associations between traits to direct relationships, thus towards relationships that may result in changes when external interventions are applied. The causal structure can give more insight into underlying mechanisms and the SEM can predict conditional changes due to such interventions.