Tobias Schwarz

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers -as caused by the additional burden of artificial genetic circuits-shifts a cell’s priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state (NESS) model of gene expression, capturing characteristics like energy dissipation -which we link to the entropy production rate- and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit’s functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energy efficiency of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo . TOC Graphic:

Tuning the conductance of a memristive device is a process that requires energy and involves power dissipation. In this letter, the role the memory state programming strategy plays in this connection is investigated. To this end, the device model equations representing the electron transport and metal ion/oxygen vacancy displacement caused by the application of an external signal must be solved consistently. However, if instead of considering the applied voltage as the model input, a memory state trajectory is assumed, the model equations can be decoupled allowing an analytic description of the problem. In order to accomplish this objective a more accurate version of the dynamic memdiode model is used which incorporates additional physical considerations in the characteristic switching times. It is demonstrated that alternative trajectories (concave, convex, and sigmoidal) lead to a variety of energy consumption-maximum dissipated power relationships highlighting the key role played by the selected programming strategy. This kind of study contributes to the basic understanding of the writing process of memristors (synaptic weight assignment in neural networks) shedding light on the associated electrical effects.

Genetic design automation (GDA) tools hold promise to speed-up circuit design in synthetic biology. Their widespread adoption is hampered by their limited predictive power, resulting in frequent deviations between the in silico and in vivo performance of a genetic circuit. Context effects, i.e., the change in overall circuit functioning, due to the intracellular environment of the host and due to cross-talk among circuits components are believed to be a major source for the aforementioned deviations. Incorporating these effects in computational models of GDA tools is challenging but is expected to boost their predictive power and hence their deployment. Using fine-grained thermodynamic models of promoter activity, we show in this work how to account for two major components of cellular context effects: (i) crosstalk due to limited specificity of used regulators and (ii) titration of circuit regulators to off-target binding sites on the host genome. We show how we can compensate the incurred increase in computational complexity through dedicated branch-and-bound techniques during the technology mapping process. Using the synthesis of several combinational logic circuits based on Cello's device library as a case study, we analyze the effect of different intensities and distributions of crosstalk on circuit performance and on the usability of a given device library.

Synthetic Biology aims to create biological systems from scratch that do not exist in nature. An important method in this context is the engineering of DNA sequences such that cells realize Boolean functions that serve as control mechanisms in biological systems, e.g. in medical or agricultural applications. Libraries of logic gates exist as predefined gene sequences, based on the genetic mechanism of transcriptional regulation. Each individual gate is composed of different biological parts to allow for the differentiation of their output signals. Even gates of the same logic type therefore exhibit different transfer characteristics, i.e. relation from input to output signals. Thus, simulation of the whole network of genetic gates is needed to determine the performance of a genetic circuit. This makes mapping Boolean functions to these libraries much more complicated compared to EDA. Yet, optimal results are desired in the design phase due to high lab implementation costs. In this work, we identify fundamental features of the transfer characteristic of gates based on transcriptional regulation which is widely used in genetic gate technologies. Based on this, we present novel exact (Branch-and-Bound) and heuristic (Branch-and-Bound, Simulated Annealing) algorithms for the problem of technology mapping of genetic circuits and evaluate them using a prominent gate library. In contrast to state-of-the-art tools, all obtained solutions feature a (near) optimal output performance. Our exact method only explores 6.5 % and the heuristics even 0.2 % of the design space.

Genetic design automation (GDA) tools hold promise to speed-up circuit design in synthetic biology. Their wide-spread adoption is hampered by their limited predictive power, resulting in frequent deviations between in-silico and in-vivo performance of a genetic circuit. Context-effects, i.e., the change in overall circuit functioning, due to the intracellular environment of the host and due to cross-talk among circuits components are believed to be a major source for the aforementioned deviations. Incorporating these effects in computational models of GDA tools is challenging but is expected to boost their predictive power, and hence, their deployment. Using fine-grained thermodynamic models of promoter activity we show in this work, how to account for two major components of cellular context effects: (i) Crosstalk due to limited specificity of used regulators and (ii) titration of circuit regulators to off-target binding sites on the host genome. We show, how we can compensate the incurred increase in computational complexity through dedicated branch-and-bound techniques during the technology mapping process. Using the synthesis of several combinational logic circuits based on Cello’s device library as a case study, we analyze the effect of different intensities and distributions of crosstalk on circuit performance and on the usability of a given device library.

Genetic design automation methods for combinational circuits often rely on standard algorithms from electronic design automation in their circuit synthesis and technology mapping. However, those algorithms are domain-specific and are hence often not directly suitable for the biological context. In this work we identify aspects of those algorithms that require domain-adaptation. We first demonstrate that enumerating structural variants for a given Boolean specification allows us to find better performing circuits and that stochastic gate assignment methods need to be properly adjusted in order to find the best assignment. Second, we present a general circuit scoring scheme that accounts for the limited accuracy of biological device models including the variability across cells and show that circuits selected according to this score exhibit higher robustness with respect to parametric variations. If gate characteristics in a library are just given in terms of intervals, we provide means to efficiently propagate signals through such a circuit and compute corresponding scores. We demonstrate the novel design approach using the Cello gate library and 33 logic functions that were synthesized and implemented in vivo recently (Nielsen, A., et al., Science, 2016, 352 (6281), DOI: 10.1126/science.aac7341). Across this set of functions, 32 of them can be improved by simply considering structural variants yielding performance gains of up to 7.9-fold, whereas 22 of them can be improved with gains up to 26-fold when selecting circuits according to the novel robustness score. We furthermore report on the synergistic combination of the two proposed improvements.

Genetic design automation methods for combinational circuits often rely on standard algorithms from electronic design automation in their circuit synthesis and technology mapping. However, those algorithms are domain-specific and are hence often not directly suitable for the biological context. In this work we identify aspects of those algorithms that require domain-adaptation. We first demonstrate that enumerating structural variants for a given Boolean specification allows us to find better performing circuits and that stochastic gate assignment methods need to be properly adjusted in order to find the best assignment. Second, we present a general circuit scoring scheme that accounts for the limited accuracy of biological device models including the variability across cells and show that circuits selected according to this score exhibit higher robustness with respect to parametric variations. If gate characteristics in a library are just given in terms of intervals, we provide means to efficiently propagate signals through such a circuit and compute corresponding scores. We demonstrate the novel design approach using the Cello gate library and 33 logic functions that were synthesized and implemented in vivo recently ( 1 ). We show that an average 1.3-fold and a peak 6.5-fold performance increase can be achieved by simply considering structural variants and that an average 1.8-fold and a peak 30-fold gain in the novel robustness score can be obtained when selecting circuits according to it. Graphical TOC Entry: