Beyond the gene

Thomas Hunt Morgan and the invisible gene: the right tool for the job

The paper analyzes the early theory building process of Thomas Hunt Morgan (1866-1945) from the 1910s to the 1930s and the introduction of the invisible gene as a main explanatory unit of heredity. Morgan's work marks the transition between two different styles of thought. In the early 1900s, he shifted from an embryological study of the development of the organism to a study of the mechanism of genetic inheritance and gene action. According to his contemporaries as well as to historiography, Morgan separated genetics from embryology, and the gene from the whole organism. Other scholars identified an underlying embryological focus in Morgan's work throughout his career. Our paper aims to clarify the debate by concentrating on Morgan's theory building-characterized by his confidence in the power of experimental methods, and carefully avoiding any ontological commitment towards the gene-and on the continuity of the questions to be addressed by both embryology and genetics.

A Postgenomic Perspective on Molecular Cytogenetics

BACKGROUND

The postgenomic era is featured by massive data collection and analyses from various large scale-omics studies. Despite the promising capability of systems biology and bioinformatics to handle large data sets, data interpretation, especially the translation of -omics data into clinical implications, has been challenging.

DISCUSSION

In this perspective, some important conceptual and technological limitations of current systems biology are discussed in the context of the ultimate importance of the genome beyond the collection of all genes. Following a brief summary of the contributions of molecular cytogenetics/cytogenomics in the pre- and post-genomic eras, new challenges for postgenomic research are discussed. Such discussion leads to a call to search for a new conceptual framework and holistic methodologies.

CONCLUSION

Throughout this synthesis, the genome theory of somatic cell evolution is highlighted in contrast to gene theory, which ignores the karyotype-mediated higher level of genetic information. Since "system inheritance" is defined by the genome context (gene content and genomic topology) while "parts inheritance" is defined by genes/epigenes, molecular cytogenetics and cytogenomics (which directly study genome structure, function, alteration and evolution) will play important roles in this postgenomic era.

Using Biology Education Research and Qualitative Inquiry to Inform Genomic Nursing Education

Decades of research in biology education show that learning genetics is difficult and reveals specific sources of learning difficulty. Little is known about how nursing students learn in this domain, although they likely encounter similar difficulties as nonnursing students. Using qualitative approaches, this study investigated challenges to learning genetics among nursing students. Findings indicate that nursing students face learning difficulties already identified among biology students, suggesting that nurse educators might benefit from biology education research.

The Evolving Definition of the Term "Gene"

This paper presents a history of the changing meanings of the term "gene," over more than a century, and a discussion of why this word, so crucial to genetics, needs redefinition today. In this account, the first two phases of 20th century genetics are designated the "classical" and the "neoclassical" periods, and the current molecular-genetic era the "modern period." While the first two stages generated increasing clarity about the nature of the gene, the present period features complexity and confusion. Initially, the term "gene" was coined to denote an abstract "unit of inheritance," to which no specific material attributes were assigned. As the classical and neoclassical periods unfolded, the term became more concrete, first as a dimensionless point on a chromosome, then as a linear segment within a chromosome, and finally as a linear segment in the DNA molecule that encodes a polypeptide chain. This last definition, from the early 1960s, remains the one employed today, but developments since the 1970s have undermined its generality. Indeed, they raise questions about both the utility of the concept of a basic "unit of inheritance" and the long implicit belief that genes are autonomous agents. Here, we review findings that have made the classic molecular definition obsolete and propose a new one based on contemporary knowledge.

The inheritance of process: a dynamical systems approach

A central unresolved problem of evolutionary biology concerns the way in which evolution at the genotypic level relates to the evolution of phenotypes. This genotype-phenotype map involves developmental and physiological processes, which are complex and not well understood. These processes co-determine the rate and direction of adaptive change by shaping the distribution of phenotypic variability on which selection can act. In this study, we argue-expanding on earlier ideas by Goodwin, Oster, and Alberch-that an explicit treatment of this map in terms of dynamical systems theory can provide an integrated understanding of evolution and development. We describe a conceptual framework, which demonstrates how development determines the probability of possible phenotypic transitions-and hence the evolvability of a biological system. We use a simple conceptual model to illustrate how the regulatory dynamics of the genotype-phenotype map can be passed on from generation to generation, and how heredity itself can be treated as a dynamic process. Our model yields explanations for punctuated evolutionary dynamics, the difference between micro- and macroevolution, and for the role of the environment in major phenotypic transitions. We propose a quantitative research program in evolutionary developmental systems biology-combining experimental methods with mathematical modeling-which aims at elaborating our conceptual framework by applying it to a wide range of evolving developmental systems. This requires a large and sustained effort, which we believe is justified by the significant potential benefits of an extended evolutionary theory that uses dynamic molecular genetic data to reintegrate development and evolution.

Towards a science of informed matter

Over the last couple of decades, a call has begun to resound in a number of distinct fields of inquiry for a reattachment of form to matter, for an understanding of 'information' as inherently embodied, or, as Jean-Marie Lehn calls it, for a "science of informed matter." We hear this call most clearly in chemistry, in cognitive science, in molecular computation, and in robotics-all fields looking to biological processes to ground a new epistemology. The departure from the values of a more traditional epistemological culture can be seen most clearly in changing representations of biological development. Where for many years now, biological discourse has accepted a sharp distinction (borrowed directly from classical computer science) between information and matter, software and hardware, data and program, encoding and enactment, a new discourse has now begun to emerge in which these distinctions have little meaning. Perhaps ironically, much of this shift depends on drawing inspiration from just those biological processes which the discourse of disembodied information was intended to describe.

What is a gene? - Revisited

The dialectic discourse of the 'gene' as the unit of heredity deduced from the phenotype, whether an intervening variable or a hypothetical construct, appeared to be settled with the presentation of the molecular model of DNA: the gene was reduced to a sequence of DNA that is transcribed into RNA that is translated into a polypeptide; the polypeptides may fold into proteins that are involved in cellular metabolism and structure, and hence function. This path turned out to be more bewildering the more the regulation of products and functions were uncovered in the contexts of integrated cellular systems. Philosophers struggling to define a unified concept of the gene as the basic entity of (molecular) genetics confronted those who suggested several different 'genes' according to the conceptual frameworks of the experimentalists. Researchers increasingly regarded genes de facto as generic terms for describing their empiric data, and with improved DNA-sequencing capacities these entities were as a rule bottom-up nucleotide sequences that determine functions. Only recently did empiricists return to discuss conceptual considerations, including top-down definitions of units of function that through cellular mechanisms select the DNA sequences which comprise 'genomic-footprints' of functional entities.

Modeling structure-function relationships in synthetic DNA sequences using attribute grammars

Recognizing that certain biological functions can be associated with specific DNA sequences has led various fields of biology to adopt the notion of the genetic part. This concept provides a finer level of granularity than the traditional notion of the gene. However, a method of formally relating how a set of parts relates to a function has not yet emerged. Synthetic biology both demands such a formalism and provides an ideal setting for testing hypotheses about relationships between DNA sequences and phenotypes beyond the gene-centric methods used in genetics. Attribute grammars are used in computer science to translate the text of a program source code into the computational operations it represents. By associating attributes with parts, modifying the value of these attributes using rules that describe the structure of DNA sequences, and using a multi-pass compilation process, it is possible to translate DNA sequences into molecular interaction network models. These capabilities are illustrated by simple example grammars expressing how gene expression rates are dependent upon single or multiple parts. The translation process is validated by systematically generating, translating, and simulating the phenotype of all the sequences in the design space generated by a small library of genetic parts. Attribute grammars represent a flexible framework connecting parts with models of biological function. They will be instrumental for building mathematical models of libraries of genetic constructs synthesized to characterize the function of genetic parts. This formalism is also expected to provide a solid foundation for the development of computer assisted design applications for synthetic biology.

An empirical investigation of organizational memetic variation

The potential of a theory based on organizational memes in coevolution with the environment has been postulated, but remains empirically under-developed. This study explores whether understanding organizational memetic variation is empirically possible and, if so, whether it might, with further development, provide managers with early indications of a misalignment between organizational action (operationalized as memetic variation) and strategic intent—the amount of memetic variation needed to stay aligned with the competitive environment. A method for empirically identifying knowledge-based memes in everyday practice is developed. It stems from modern advances in genetics allowing micro-level changes in genes to be linked to macro-environmental dynamics. Using the concept that memetic variation is caused by everyday, uncontrollable uncertainties of interpretation, memetic variation is reduced to seven categorical types. A highly exploratory quasi-experimental design allows a preliminary comparison of a would-be innovative ‘Portfolio Management Committee’ with self-organizing Internet chat-room settings that are representational of open-source innovation. Results are consistent with predictions showing that higher levels of variation are seen in the latter, despite the former’s strategic intent of innovating to stay aligned with a dynamic environment. Further work is needed, however, to test reliability and validity.

Intelligently deciphering unintelligible designs: algorithmic algebraic model checking in systems biology

Systems biology, as a subject, has captured the imagination of both biologists and systems scientists alike. But what is it? This review provides one researcher's somewhat idiosyncratic view of the subject, but also aims to persuade young scientists to examine the possible evolution of this subject in a rich historical context. In particular, one may wish to read this review to envision a subject built out of a consilience of many interesting concepts from systems sciences, logic and model theory, and algebra, culminating in novel tools, techniques and theories that can reveal deep principles in biology--seen beyond mere observations. A particular focus in this review is on approaches embedded in an embryonic program, dubbed 'algorithmic algebraic model checking', and its powers and limitations.

Transcriptional regulation of mouse alpha A-crystallin gene in a 148kb Cryaa BAC and its derivates

Background

αA-crystallin is highly expressed in the embryonic, neonatal and adult mouse lens. Previously, we identified two novel distal control regions, DCR1 and DCR3. DCR1 was required for transgenic expression of enhanced green fluorescent protein, EGFP, in lens epithelium, whereas DCR3 was active during "late" stages of lens primary fiber cell differentiation. However, the onset of transgenic EGFP expression was delayed by 12–24 hours, compared to the expression of the endogenous Cryaa gene.

Results

Here, we used bacterial artificial chromosome (BAC) and standard transgenic approaches to examine temporal and spatial regulation of the mouse Cryaa gene. Two BAC transgenes, with EGFP insertions into the third coding exon of Cryaa gene, were created: the intact αA-crystallin 148 kb BAC (αA-BAC) and αA-BAC(ΔDCR3), which lacks approximately 1.0 kb of genomic DNA including DCR3. Expression of EGFP in the majority of both BAC transgenics nearly recapitulated the endogenous expression pattern of the Cryaa gene in lens, but not outside of the lens. The number of cells expressing αA-crystallin in the lens pit was higher compared to the number of cells expressing EGFP. Next, we generated additional lines using a 15 kb fragment of αA-crystallin locus derived from αA-BAC(ΔDCR3), 15 kb Cryaa/EGFP. A 15 kb region of Cryaa/EGFP supported the expression pattern of EGFP also in the lens pit. However, co-localization studies of αA-crystallin and EGFP indicated that the number of cells that showed transgenic expression was higher compared to cells expressing αA-crystallin in the lens pit.

Conclusion

We conclude that a 148 kb αA-BAC likely contains all of the regulatory regions required for αA-crystallin expression in the lens, but not in retina, spleen and thymus. In addition, while the 15 kb Cryaa/EGFP region also supported the expression of EGFP in the lens pit, expression in regions such as the hindbrain, indicate that additional genomic regions may play modulatory functions in regulating extralenticular αA-crystallin expression. Finally, deletion of DCR3 in either αA-BAC(ΔDCR3) or Cryaa (15 kb) transgenic mice result in EGFP expression patterns that are consistent with DCR's previously established role as a distal enhancer active in "late" primary lens fiber cells.

Probability landscapes for integrative genomics

Background

The comprehension of the gene regulatory code in eukaryotes is one of the major challenges of systems biology, and is a requirement for the development of novel therapeutic strategies for multifactorial diseases. Its bi-fold degeneration precludes brute force and statistical approaches based on the genomic sequence alone. Rather, recursive integration of systematic, whole-genome experimental data with advanced statistical regulatory sequence predictions needs to be developed. Such experimental approaches as well as the prediction tools are only starting to become available and increasing numbers of genome sequences and empirical sequence annotations are under continual discovery-driven change. Furthermore, given the complexity of the question, a decade(s) long multi-laboratory effort needs to be envisioned. These constraints need to be considered in the creation of a framework that can pave a road to successful comprehension of the gene regulatory code.

Results

We introduce here a concept for such a framework, based entirely on systematic annotation in terms of probability profiles of genomic sequence using any type of relevant experimental and theoretical information and subsequent cross-correlation analysis in hypothesis-driven model building and testing.

Conclusion

Probability landscapes, which include as reference set the probabilistic representation of the genomic sequence, can be used efficiently to discover and analyze correlations amongst initially heterogeneous and un-relatable descriptions and genome-wide measurements. Furthermore, this structure is usable as a support for automatically generating and testing hypotheses for alternative gene regulatory grammars and the evaluation of those through statistical analysis of the high-dimensional correlations between genomic sequence, sequence annotations, and experimental data. Finally, this structure provides a concrete and tangible basis for attempting to formulate a mathematical description of gene regulation in eukaryotes on a genome-wide scale.