Genomics. The end of the beginning

Mutações biopolíticas e discursos sobre o normal: atualizações foucaultianas na era biotecnológica

A proposta desse artigo é apontar mutações biopolíticas contemporâneas provocadas, sobretudo, pelo uso cada vez mais frequente de (bio)tecnologias. Observamos um movimento crescente de formas de articulação coletiva para gestão de riscos e de formação de identidades individuais e coletivas pautadas em referentes corporais. Uma nova subjetividade biomédica se constitui e novas formas de responsabilidade – especialmente, genética – estão em jogo. Em uma sociedade em que a tecnologia de poder é centrada na gestão da vida, a normalização dos corpos e comportamentos parece inevitável. Mas vemos, também, o uso de tecnologias no corpo que subvertem a “coerência corporal” e evidenciam a complexidade de definir, de forma rígida, a fronteira entre normal e patológico.

Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems

Mathematical models of biochemical systems provide a means to elucidate the link between the genotype, environment, and phenotype. A subclass of mathematical models, known as mechanistic models, quantitatively describe the complex non-linear mechanisms that capture the intricate interactions between biochemical components. However, the study of mechanistic models is challenging because most are analytically intractable and involve large numbers of system parameters. Conventional methods to analyze them rely on local analyses about a nominal parameter set and they do not reveal the vast majority of potential phenotypes possible for a given system design. We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy. Instead, this approach relies on architectural features of the model to identify the phenotypic repertoire and then predict values for the parameters that yield specific instances of the system that realize desired phenotypic characteristics. Here, we present a collection of software tools, the Design Space Toolbox V2 based on the System Design Space method, that automates (1) enumeration of the repertoire of model phenotypes, (2) prediction of values for the parameters for any model phenotype, and (3) analysis of model phenotypes through analytical and numerical methods. The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach. We illustrate the power of these new tools by applying them to a synthetic gene circuit that can exhibit multi-stability. We then predict values for the system parameters such that the design exhibits 2, 3, and 4 stable steady states. In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count. This example shows the power of these new automated methods to rapidly identify behaviors of interest and efficiently predict parameter values for their realization. These tools may be applied to understand complex natural circuitry and to aid in the rational design of synthetic circuits.

Elucidating the genotype-phenotype map by automatic enumeration and analysis of the phenotypic repertoire

Background:

The gap between genotype and phenotype is filled by complex biochemical systems most of which are poorly understood. Because these systems are complex, it is widely appreciated that quantitative understanding can only be achieved with the aid of mathematical models. However, formulating models and measuring or estimating their numerous rate constants and binding constants is daunting. Here we present a strategy for automating difficult aspects of the process.

Methods:

The strategy, based on a system design space methodology, is applied to a class of 16 designs for a synthetic gene oscillator that includes seven designs previously formulated on the basis of experimentally measured and estimated parameters.

Results:

Our strategy provides four important innovations by automating: (1) enumeration of the repertoire of qualitatively distinct phenotypes for a system; (2) generation of parameter values for any particular phenotype; (3) simultaneous realization of parameter values for several phenotypes to aid visualization of transitions from one phenotype to another, in critical cases from functional to dysfunctional; and (4) identification of ensembles of phenotypes whose expression can be phased to achieve a specific sequence of functions for rationally engineering synthetic constructs. Our strategy, applied to the 16 designs, reproduced previous results and identified two additional designs capable of sustained oscillations that were previously missed.

Conclusions:

Starting with a system’s relatively fixed aspects, its architectural features, our method enables automated analysis of nonlinear biochemical systems from a global perspective, without first specifying parameter values. The examples presented demonstrate the efficiency and power of this automated strategy.

Strategy revealing phenotypic differences among synthetic oscillator designs

Considerable progress has been made in identifying and characterizing the component parts of genetic oscillators, which play central roles in all organisms. Nonlinear interaction among components is sufficiently complex that mathematical models are required to elucidate their elusive integrated behavior. Although natural and synthetic oscillators exhibit common architectures, there are numerous differences that are poorly understood. Utilizing synthetic biology to uncover basic principles of simpler circuits is a way to advance understanding of natural circadian clocks and rhythms. Following this strategy, we address the following questions: What are the implications of different architectures and molecular modes of transcriptional control for the phenotypic repertoire of genetic oscillators? Are there designs that are more realizable or robust? We compare synthetic oscillators involving one of three architectures and various combinations of the two modes of transcriptional control using a methodology that provides three innovations: a rigorous definition of phenotype, a procedure for deconstructing complex systems into qualitatively distinct phenotypes, and a graphical representation for illuminating the relationship between genotype, environment, and the qualitatively distinct phenotypes of a system. These methods provide a global perspective on the behavioral repertoire, facilitate comparisons of alternatives, and assist the rational design of synthetic gene circuitry. In particular, the results of their application here reveal distinctive phenotypes for several designs that have been studied experimentally as well as a best design among the alternatives that has yet to be constructed and tested.

Phenotypic deconstruction of gene circuitry

It remains a challenge to obtain a global perspective on the behavioral repertoire of complex nonlinear gene circuits. In this paper, we describe a method for deconstructing complex systems into nonlinear sub-systems, based on mathematically defined phenotypes, which are then represented within a system design space that allows the repertoire of qualitatively distinct phenotypes of the complex system to be identified, enumerated, and analyzed. This method efficiently characterizes large regions of system design space and quickly generates alternative hypotheses for experimental testing. We describe the motivation and strategy in general terms, illustrate its use with a detailed example involving a two-gene circuit with a rich repertoire of dynamic behavior, and discuss experimental means of navigating the system design space.

Biomedical engineering strategies in system design space

Modern systems biology and synthetic bioengineering face two major challenges in relating properties of the genetic components of a natural or engineered system to its integrated behavior. The first is the fundamental unsolved problem of relating the digital representation of the genotype to the analog representation of the parameters for the molecular components. For example, knowing the DNA sequence does not allow one to determine the kinetic parameters of an enzyme. The second is the fundamental unsolved problem of relating the parameters of the components and the environment to the phenotype of the global system. For example, knowing the parameters does not tell one how many qualitatively distinct phenotypes are in the organism's repertoire or the relative fitness of the phenotypes in different environments. These also are challenges for biomedical engineers as they attempt to develop therapeutic strategies to treat pathology or to redirect normal cellular functions for biotechnological purposes. In this article, the second of these fundamental challenges will be addressed, and the notion of a "system design space" for relating the parameter space of components to the phenotype space of bioengineering systems will be focused upon. First, the concept of a system design space will be motivated by introducing one of its key components from an intuitive perspective. Second, a simple linear example will be used to illustrate a generic method for constructing the design space in which qualitatively distinct phenotypes can be identified and counted, their fitness analyzed and compared, and their tolerance to change measured. Third, two examples of nonlinear systems from different areas of biomedical engineering will be presented. Finally, after giving reference to a few other applications that have made use of the system design space approach to reveal important design principles, some concluding remarks concerning challenges and opportunities for further development will be made.

Sequences and consequences

The conversion of data into knowledge constitutes a great challenge for future biological research. The new science of Systems Biology claims to be able to solve the problem but I contend that this approach will fail because deducing models of function from the behaviour of a complex system is an inverse problem that is impossible to solve. In addition, one cannot easily escape into high-level holistic approaches, since the essence of all biological systems is that they are encoded as molecular descriptions in their genes and since genes are molecules and exert their functions through other molecules, the molecular explanation must constitute the core of understanding biological systems. We then solve the forward problem of computing the behaviour of the system from its components and their interactions. I propose that the correct level of abstraction is the cell and provide an outline of Cellmap, a design for a system to organize biological information.

Qualitatively distinct phenotypes in the design space of biochemical systems

Although characterization of the genotype has undergone revolutionary advances as a result of the successful genome projects, the chasm between our understanding of a fully characterized gene sequence and the phenotypic repertoire of the organism is as broad and deep as it was in the pre-genomic era. There are two fundamental unsolved problems that provide the context for the challenges in relating genotype to phenotype. We address one of these and describe a generic method for constructing a system design space in which qualitatively distinct phenotypes can be identified and counted, their relative fitness analyzed and compared, and their tolerance to change measured.

Synthetic biology: ethical ramifications 2009

During 2007 and 2008 synthetic biology moved from the manifesto stage to research programs. As of 2009, synthetic biology is ramifying; to ramify means to produce differentiated trajectories from previous determinations. From its inception, most of the players in synthetic biology agreed on the need for (a) rationalized design and construction of new biological parts, devices, and systems as well as (b) the re-design of natural biological systems for specified purposes, and that (c) the versatility of designed biological systems makes them suitable to address such challenges as renewable energy, the production of inexpensive drugs, and environmental remediation, as well as providing a catalyst for further growth of biotechnology. What is understood by these goals, however, is diverse. Those assorted understandings are currently contributing to different ramifications of synthetic biology. The Berkeley Human Practices Lab, led by Paul Rabinow, is currently devoting its efforts to documenting and analyzing these ramifications as they emerge.

Beyond the gene

This paper is a response to the increasing difficulty biologists find in agreeing upon a definition of the gene, and indeed, the increasing disarray in which that concept finds itself. After briefly reviewing these problems, we propose an alternative to both the concept and the word gene—an alternative that, like the gene, is intended to capture the essence of inheritance, but which is both richer and more expressive. It is also clearer in its separation of what the organism statically is (what it tangibly inherits) and what it dynamically does (its functionality and behavior). Our proposal of a genetic functor, or genitor, is a sweeping extension of the classical genotype/phenotype paradigm, yet it appears to be faithful to the findings of contemporary biology, encompassing many of the recently emerging—and surprisingly complex—links between structure and functionality.

Functional genomics of the chicken--a model organism

Since the sequencing of the genome and the development of high-throughput tools for the exploration of functional elements of the genome, the chicken has reached model organism status. Functional genomics focuses on understanding the function and regulation of genes and gene products on a global or genome-wide scale. Systems biology attempts to integrate functional information derived from multiple high-content data sets into a holistic view of all biological processes within a cell or organism. Generation of a large collection ( approximately 600K) of chicken expressed sequence tags, representing most tissues and developmental stages, has enabled the construction of high-density microarrays for transcriptional profiling. Comprehensive analysis of this large expressed sequence tag collection and a set of approximately 20K full-length cDNA sequences indicate that the transcriptome of the chicken represents approximately 20,000 genes. Furthermore, comparative analyses of these sequences have facilitated functional annotation of the genome and the creation of several bioinformatic resources for the chicken. Recently, about 20 papers have been published on transcriptional profiling with DNA microarrays in chicken tissues under various conditions. Proteomics is another powerful high-throughput tool currently used for examining the dynamics of protein expression in chicken tissues and fluids. Computational analyses of the chicken genome are providing new insight into the evolution of gene families in birds and other organisms. Abundant functional genomic resources now support large-scale analyses in the chicken and will facilitate identification of transcriptional mechanisms, gene networks, and metabolic or regulatory pathways that will ultimately determine the phenotype of the bird. New technologies such as marker-assisted selection, transgenics, and RNA interference offer the opportunity to modify the phenotype of the chicken to fit defined production goals. This review focuses on functional genomics in the chicken and provides a road map for large-scale exploration of the chicken genome.

An approach of encoding for prediction of splice sites using SVM

In splice sites prediction, the accuracy is lower than 90% though the sequences adjacent to the splice sites have a high conservation. In order to improve the prediction accuracy, much attention has been paid to the improvement of the performance of the algorithms used, and few used for solving the fundamental issues, namely, nucleotide encoding. In this paper, a predictor is constructed to predict the true and false splice sites for higher eukaryotes based on support vector machines (SVM). Four types of encoding, which were mono-nucleotide (MN) encoding, MN with frequency difference between the true sites and false sites (FDTF) encoding, Pair-wise nucleotides (PN) encoding and PN with FDTF encoding, were applied to generate the input for the SVM. The results showed that PN with FDTF encoding as input to SVM led to the most reliable recognition of splice sites and the accuracy for the prediction of true donor sites and false sites were 96.3%, 93.7%, respectively, and the accuracy for predicting of true acceptor sites and false sites were 94.0%, 93.2%, respectively.

Alternative designs for a genetic switch: analysis of switching times using the piecewise power-law representation

Some genes are thought to be switched discontinuously ON or OFF in response to environmental or developmental stimuli, whereas other genes are thought to be switched in a continuously variable fashion. We have previously identified criteria that distinguish between discontinuous and continuous genetic switches for an inducible catabolic pathway. These two types of switches exhibit several additional characteristics, beyond their qualitatively distinct behaviors, that influence their natural selection. These characteristics include threshold value, magnitude of the input signal required for switching ('switching effort'), magnitude of the corresponding output signal, duty cycle, switching time, and robustness. In order to characterize the biological design principles governing such switches, we have developed mathematical models of generic gene circuits and analyzed their behavior. Here we report the results of a comparative study designed to identify essential differences in switching time. This study has been greatly facilitated by use of the piecewise power-law representation, which was first developed by systems engineers in the 1940s and adapted for biochemical systems in the early 1970s. With this approach, we have been able to derive analytical expressions for switching time. When the alternative designs are made as nearly equivalent as possible, by the method of mathematically controlled comparison, we find that the switching times for the continuous case are less than that for the corresponding discontinuous case. We also find that ON times are faster than OFF times in all cases. These results are discussed in the specific context of the inducible lactose operon of Escherichia coli.

Efficient isolation of regulatory sequences from human genome and BAC DNA

Isolation of regulatory DNA fragments is the basis of the identification of DNA binding proteins and the study of the regulation of gene expression. Presently there is a lack of efficient methods to broadly isolate and identify DNA regulatory fragments. We developed an efficient method to isolate regulatory DNA sequences from both genome and bacterial artificial chromosome (BAC) based on electrophoretic mobility shift assay and PCR techniques without purified transcription factors. Twenty-nine DNA fragments were isolated from human genome and 24 from BAC DNA containing human apolipoprotein AI gene cluster. Transient transfection assay showed that some fragments could enhance the transcription of reporter gene.

Fruits of human genome project and private venture, and their impact on life science

A small knowledge base was created by organizing the Human Genome Project (HGP) and its related issues in "Science" magazines between 1996 and 2000. This base revealed the stunning achievement of HGP and a private venture and its impact on today's biology and life science. In the mid-1990, they encouraged the development of advanced high throughput automated DNA sequencers and the technologies that can analyse all genes at once in a systematic fashion. Using these technologies, they completed the genome sequence of human and various other organisms. These fruits opened the door to comparative genomics, functional genomics, the interdisprinary field between computer and biology, and proteomics. They have caused a shift in biological investigation from studying single genes or proteins to studying all genes or proteins at once, and causing revolutional changes in traditional biology, drug discovery and therapy. They have expanded the range of potential drug targets and have facilitated a shift in drug discovery programs toward rational target-based strategies. They have spawned pharmacogenomics that could give rise to a new generation of highly effective drugs that treat causes, not just symptoms. They should also cause a migration from the traditional medications that are safe and effective for every members of the population to personalized medicine and personalized therapy.

The drosophila genome and its oncological implications

The genome of the fruit fly has recently been sequenced, prior to the release of the human genome sequence within the next few years. The fly has some 13 600 genes, compared with the estimated 80 000 genes in the human genome. Some 70% of genes appear to be broadly conserved across eukaryotic species, and some remarkable homologies have been found between 177 genes in the fly and the 289 human genes so far associated with diseases in man. The fruit fly genome is likely to prove an elegant model and a rich source of experimentation for the aetiology and regulation of human cancers.