Strategic evaluations and techno-economic networks. Vaccine innovation in the Cuban biotech sector: for public health – or for profits?

In this paper the Cuban biotech sector with its highly integrated vaccine industry is analyzed in the perspective of the techno-economic network model of Michel Callon. The paper discusses the strategic evaluations that have been performed in the sector. Given the emphasis on public health displayed by the Cuban government and the precarious condition of the Cuban economy (at least during the last 20 years), the strategic evaluations could be seen as an articulation of the (sometimes conflicting) interests of public health and commercialism. The main issue to be discussed in the present paper is how interests related to public health and economic considerations are articulated and balanced in the strategic evaluations that have been made in the Cuban biotech sector. There is a focus on the vaccine related activities of the sector, which will be loosely referred to as the Cuban vaccine industry. This is the first of two papers about the Cuban bitoech sector and vaccine industry.Innovation systems, techno-economic networks, Cuba, vaccines, biotechnology

Development, organization and management of techno-economic networks: the Cuban biotech sector and vaccine industry

This paper, the second out of two about the Cuban biotech sector and vaccine industry, gives an account of the creation, organization, and management mechanisms of the technoeconomic networks of the Cuban biotech sector and vaccine industry, by applying the concept of techno-economic evaluations developed by Michel Callon. It also seeks to explain the apparent paradox about shifting the emphasis of the biotech sector from that of targeting a ‘modern’ disease pattern to that of a’ traditional’ one that was identified in the first paper. Centralized and participatory decision making processes seem to facilitate close coordination and cooperation between the different research and development centers. The paper also inquires into the relation between the biotech sector and the national System for Science and Technological Innovation.Innovation systems, techno-economic networks, Cuba, vaccines, biotechnology

The genotype-phenotype relationship in multicellular pattern-generating models - the neglected role of pattern descriptors

Background: A deep understanding of what causes the phenotypic variation arising from biological patterning processes, cannot be claimed before we are able to recreate this variation by mathematical models capable of generating genotype-phenotype maps in a causally cohesive way. However, the concept of pattern in a multicellular context implies that what matters is not the state of every single cell, but certain emergent qualities of the total cell aggregate. Thus, in order to set up a genotype-phenotype map in such a spatiotemporal pattern setting one is actually forced to establish new pattern descriptors and derive their relations to parameters of the original model. A pattern descriptor is a variable that describes and quantifies a certain qualitative feature of the pattern, for example the degree to which certain macroscopic structures are present. There is today no general procedure for how to relate a set of patterns and their characteristic features to the functional relationships, parameter values and initial values of an original pattern-generating model. Here we present a new, generic approach for explorative analysis of complex patterning models which focuses on the essential pattern features and their relations to the model parameters. The approach is illustrated on an existing model for Delta-Notch lateral inhibition over a two-dimensional lattice. Results: By combining computer simulations according to a succession of statistical experimental designs, computer graphics, automatic image analysis, human sensory descriptive analysis and multivariate data modelling, we derive a pattern descriptor model of those macroscopic, emergent aspects of the patterns that we consider of interest. The pattern descriptor model relates the values of the new, dedicated pattern descriptors to the parameter values of the original model, for example by predicting the parameter values leading to particular patterns, and provides insights that would have been hard to obtain by traditional methods. Conclusion: The results suggest that our approach may qualify as a general procedure for how to discover and relate relevant features and characteristics of emergent patterns to the functional relationships, parameter values and initial values of an underlying pattern-generating mathematical model

Allele Interaction – Single Locus Genetics Meets Regulatory Biology

Background: Since the dawn of genetics, additive and dominant gene action in diploids have been defined by comparison of heterozygote and homozygote phenotypes. However, these definitions provide little insight into the underlying intralocus allelic functional dependency and thus cannot serve directly as a mediator between genetics theory and regulatory biology, a link that is sorely needed. Methodology/Principal Findings: We provide such a link by distinguishing between positive, negative and zero allele interaction at the genotype level. First, these distinctions disclose that a biallelic locus can display 18 qualitatively different allele interaction sign motifs (triplets of +, – and 0). Second, we show that for a single locus, Mendelian dominance is not related to heterozygote allele interaction alone, but is actually a function of the degrees of allele interaction in all the three genotypes. Third, we demonstrate how the allele interaction in each genotype is directly quantifiable in gene regulatory models, and that there is a unique, one-to-one correspondence between the sign of autoregulatory feedback loops and the sign of the allele interactions. Conclusion/Significance: The concept of allele interaction refines single locus genetics substantially, and it provides a direct link between classical models of gene action and gene regulatory biology. Together with available empirical data, our results indicate that allele interaction can be exploited experimentally to identify and explain intricate intra- and inter-locu

Insights into Early Stage of Antibiotic Development in Small and Medium-Sized Enterprises: A Survey of Targets, Costs, and Durations

Antibiotic innovation has dwindled to dangerously low levels in the past 30 years. Since resistance continues to evolve, this innovation deficit can have perilous consequences on patients. A number of new incentives have been suggested to stimulate greater antibacterial drug innovation. To design effective solutions, a greater understanding is needed of actual antibiotic discovery and development costs and timelines. Small and medium-sized enterprises (SMEs) undertake most discovery and early phase development for antibiotics and other drugs. This paper attempts to gather a better understanding of SMEs’ targets, costs, and durations related to discovery and early phase development of antibacterial therapies

Nonlinear regulation enhances the phenotypic expression of trans-acting genetic polymorphisms

<p>Abstract</p> <p>Background</p> <p>Genetic variation explains a considerable part of observed phenotypic variation in gene expression networks. This variation has been shown to be located both locally (<it>cis</it>) and distally (<it>trans</it>) to the genes being measured. Here we explore to which degree the phenotypic manifestation of local and distant polymorphisms is a dynamic feature of regulatory design.</p> <p>Results</p> <p>By combining mathematical models of gene expression networks with genetic maps and linkage analysis we find that very different network structures and regulatory motifs give similar <it>cis</it>/<it>trans </it>linkage patterns. However, when the shape of the <it>cis-</it>regulatory input functions is more nonlinear or threshold-like, we observe for all networks a dramatic increase in the phenotypic expression of distant compared to local polymorphisms under otherwise equal conditions.</p> <p>Conclusion</p> <p>Our findings indicate that genetic variation affecting the form of <it>cis</it>-regulatory input functions may reshape the genotype-phenotype map by changing the relative importance of <it>cis </it>and <it>trans </it>variation. Our approach combining nonlinear dynamic models with statistical genetics opens up for a systematic investigation of how functional genetic variation is translated into phenotypic variation under various systemic conditions.</p

An Algorithm for Qualitative Simulation of Gene Regulatory Networks with Steep Sigmoidal Response Functions

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The effect of negative feedback loops on the dynamics of Boolean networks

Feedback loops in a dynamic network play an important role in determining the dynamics of that network. Through a computational study, in this paper we show that networks with fewer independent negative feedback loops tend to exhibit more regular behavior than those with more negative loops. To be precise, we study the relationship between the number of independent feedback loops and the number and length of the limit cycles in the phase space of dynamic Boolean networks. We show that, as the number of independent negative feedback loops increases, the number (length) of limit cycles tends to decrease (increase). These conclusions are consistent with the fact, for certain natural biological networks, that they on the one hand exhibit generally regular behavior and on the other hand show less negative feedback loops than randomized networks with the same numbers of nodes and connectivity

Piecewise smooth systems near a co-dimension 2 discontinuity manifold: can one say what should happen?

We consider a piecewise smooth system in the neighborhood of a co-dimension 2 discontinuity manifold $\Sigma$. Within the class of Filippov solutions, if $\Sigma$ is attractive, one should expect solution trajectories to slide on $\Sigma$. It is well known, however, that the classical Filippov convexification methodology is ambiguous on $\Sigma$. The situation is further complicated by the possibility that, regardless of how sliding on $\Sigma$ is taking place, during sliding motion a trajectory encounters so-called generic first order exit points, where $\Sigma$ ceases to be attractive. In this work, we attempt to understand what behavior one should expect of a solution trajectory near $\Sigma$ when $\Sigma$ is attractive, what to expect when $\Sigma$ ceases to be attractive (at least, at generic exit points), and finally we also contrast and compare the behavior of some regularizations proposed in the literature. Through analysis and experiments we will confirm some known facts, and provide some important insight: (i) when $\Sigma$ is attractive, a solution trajectory indeed does remain near $\Sigma$, viz. sliding on $\Sigma$ is an appropriate idealization (of course, in general, one cannot predict which sliding vector field should be selected); (ii) when $\Sigma$ loses attractivity (at first order exit conditions), a typical solution trajectory leaves a neighborhood of $\Sigma$; (iii) there is no obvious way to regularize the system so that the regularized trajectory will remain near $\Sigma$ as long as $\Sigma$ is attractive, and so that it will be leaving (a neighborhood of) $\Sigma$ when $\Sigma$ looses attractivity. We reach the above conclusions by considering exclusively the given piecewise smooth system, without superimposing any assumption on what kind of dynamics near $\Sigma$ (or sliding motion on $\Sigma$) should have been taking place.Comment: 19 figure