Cautionary Tales of Inapproximability

Modeling biology as classical problems in computer science allows researchers to leverage the wealth of theoretical advancements in this field. Despite countless studies presenting heuristics that report improvement on specific benchmarking data, there has been comparatively little focus on exploring the theoretical bounds on the performance of practical (polynomial-time) algorithms. Conversely, theoretical studies tend to overstate the generalizability of their conclusions to physical biological processes. In this article we provide a fresh perspective on the concepts of NP-hardness and inapproximability in the computational biology domain, using popular sequence assembly and alignment (mapping) algorithms as illustrative examples. These algorithms exemplify how computer science theory can both (a) lead to substantial improvement in practical performance and (b) highlight areas ripe for future innovation. Importantly, we discuss caveats that seemingly allow the performance of heuristics to exceed their provable bounds

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems.JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Coupling Analysis, Simulation, and Experimentation in Natural and Engineered Biological Systems at the Molecular Scale

Cellular functions are controlled by genetic regulatory networks called gene circuits. Recently, there has been much interest in how gene circuits deal with or even exploit stochastic fluctuations in molecular species within the cellular environment. Through a coupling of analysis and simulation with experimentation, this dissertation work furthers the understanding of gene circuit noise behavior and makes significant contributions to the analytical and experimental tools that are currently available for the study and design of natural and synthetic gene circuits. In this study, models are developed for unregulated and autoregulated gene circuits. Results from the analysis are compared to computer simulations and experimental results. Exact stochastic simulations show that the derived analytical expressions are valid even for populations as low as 10 molecules, despite linear approximations made by the analysis. The experimental portion of this work presents a novel method for acquiring in vivo measurements of real-time gene expression. The techniques developed here are used to report the very first measurements of frequency content in gene circuit noise and verify theoretical predictions that negatively autoregulated gene circuits shift their noise spectra up to higher frequency. Through measured shifts in noise spectra, these frequency measurements can also reveal subtle and condition-dependent regulatory pathways. Measured noise spectra may also permit in vivo estimation of gene circuit kinetic rate parameters

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available

Digital craftsmanship: practitioners’ principles and their significance for defining a community of practice

Through the last two decades the spectrum of artefacts produced at the intersection of digital and hand-making processes has increased, seeing novel artefacts emerging under the umbrella of digital crafts. However, the challenge remains to specify a distinct set of shared characterising principles able to describe digital craft practitioners’ ethos as a community. Through the identification of shared principles practitioners could start more easily defining their community, developing an affinity among each other, and possibly interacting with other experts in the field – which is argued to be fundamental to ensure future acquisition, transfer, and preservation of tacit knowledge. This work explores the landscape of digital craftsmanship within Design Research and practice-based communities, highlighting the disparate backgrounds of digital craft practitioners. A combination of ethnographic, auto ethnographic, and paraethnographic approaches were adopted to articulate underlying principles by which digital craft practitioners can be addressed as a community of practice with shared motivations and ethos. Central to this study is the use of Kelly’s Repertory Grid framework, through which the researcher supported and facilitated a set of diverse expert practitioners to reflect on a range of examples of digital crafts and making processes. Through the insights obtained using these methods, and supported by theoretical debates unpacked through a critical contextual review, three principles currently shared among digital craft practitioners are tentatively proposed as a key contribution from this research: (1) digital craft practitioner’s nurture creative complex imitative learning through craft material knowledge, (2) they strongly believe aspects of the making- process need to include mostly “polymorphic” actions as opposed to “mimeomorphic” sequences, and (3) their main motivation is bound to the making process as it expresses the practitioners’ material contributory expertise–rather than the reaction or experience their outputs could elicit in viewers/users. These principles offer a definition of the community considering digital craft practitioners’ perspectives, providing the opportunity for practitioners and several stakeholder groups to engage with a provisional description of the community. Moreover, they set the basis for future research in the field and reflections on digital craftsmanship as a form of both explicit and tacit knowledge