Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, Yueming Jin, Vicente Grau. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Synthetic biology is predicated upon the paradigm of rationally engineering biological systems to enrich them with new functions. Departing from the path outlined by other engineering disciplines, synthetic biology has made limited use of mathematical models so far. Indeed, their laborious inference leverages on noisy data that often provide partial insights into the system behaviour. If the quality of data generated via a perturbation is a road-block to inference, how should we analyse the informativeness of a stimulus? Which are the factors that contribute to it? Here we combine ideas from System Identification and Bayesian inference to quantify the information content of in vivo experiments for the calibration of a deterministic model of the genetic toggle switch. Beyond establishing a link between Bayes- and Fisher Information-based inference approaches, we find a ~ 40% gain can be ascribed to the dynamical properties of the perturbation scheme. Our results hint at the importance of including Bayesian experimental design in the characterisation of synthetic circuits.

We describe an innovative experimental and computational approach to control the expression of a protein in a population of yeast cells. We designed a simple control algorithm to automatically regulate the administration of inducer molecules to the cells by comparing the actual protein expression level in the cell population with the desired expression level. We then built an automated platform based on a microfluidic device, a time-lapse microscopy apparatus, and a set of motorized syringes, all controlled by a computer. We tested the platform to force yeast cells to express a desired fixed, or time-varying, amount of a reporter protein over thousands of minutes. The computer automatically switched the type of sugar administered to the cells, its concentration and its duration, according to the control algorithm. Our approach can be used to control expression of any protein, fused to a fluorescent reporter, provided that an external molecule known to (indirectly) affect its promoter activity is available.

Understanding the relationship between topology and dynamics of transcriptional regulatory networks in mammalian cells is essential to elucidate the biology of complex regulatory and signaling pathways. Here, we characterised, via a synthetic biology approach, a transcriptional positive feedback loop (PFL) by generating a clonal population of mammalian cells (CHO) carrying a stable integration of the construct. The PFL network consists of the Tetracycline-controlled transactivator (tTA), whose expression is regulated by a tTA responsive promoter (CMV-TET), thus giving rise to a positive feedback. The same CMV-TET promoter drives also the expression of a destabilised yellow fluorescent protein (d2EYFP), thus the dynamic behaviour can be followed by time-lapse microscopy. The PFL network was compared to an engineered version of the network lacking the positive feedback loop (NOPFL), by expressing the tTA mRNA from a constitutive promoter. Doxycycline was used to repress tTA activation (switch off), and the resulting changes in fluorescence intensity for both the PFL and NOPFL networks were followed for up to 43 h. We observed a striking difference in the dynamics of the PFL and NOPFL networks. Using non-linear dynamical models, able to recapitulate experimental observations, we demonstrated a link between network topology and network dynamics. Namely, transcriptional positive autoregulation can significantly slow down the "switch off" times, as compared to the non-autoregulated system. Doxycycline concentration can modulate the response times of the PFL, whereas the NOPFL always switches off with the same dynamics. Moreover, the PFL can exhibit bistability for a range of Doxycycline concentrations. Since the PFL motif is often found in naturally occurring transcriptional and signaling pathways, we believe our work can be instrumental to characterise their behaviour.

BACKGROUND: Mechanistic models are becoming more and more popular in Systems Biology; identification and control of models underlying biochemical pathways of interest in oncology is a primary goal in this field. Unfortunately the scarce availability of data still limits our understanding of the intrinsic characteristics of complex pathologies like cancer: acquiring information for a system understanding of complex reaction networks is time consuming and expensive. Stimulus response experiments (SRE) have been used to gain a deeper insight into the details of biochemical mechanisms underlying cell life and functioning. Optimisation of the input time-profile, however, still remains a major area of research due to the complexity of the problem and its relevance for the task of information retrieval in systems biology-related experiments. RESULTS: We have addressed the problem of quantifying the information associated to an experiment using the Fisher Information Matrix and we have proposed an optimal experimental design strategy based on evolutionary algorithm to cope with the problem of information gathering in Systems Biology. On the basis of the theoretical results obtained in the field of control systems theory, we have studied the dynamical properties of the signals to be used in cell stimulation. The results of this study have been used to develop a microfluidic device for the automation of the process of cell stimulation for system identification. CONCLUSION: We have applied the proposed approach to the Epidermal Growth Factor Receptor pathway and we observed that it minimises the amount of parametric uncertainty associated to the identified model. A statistical framework based on Monte-Carlo estimations of the uncertainty ellipsoid confirmed the superiority of optimally designed experiments over canonical inputs. The proposed approach can be easily extended to multiobjective formulations that can also take advantage of identifiability analysis. Moreover, the availability of fully automated microfluidic platforms explicitly developed for the task of biochemical model identification will hopefully reduce the effects of the 'data rich--data poor' paradox in Systems Biology.

In this paper we present a comparative study among well established data mining algorithm (namely J48 and naive Bayes tree) and novel machine learning paradigms like ant miner and gene expression programming. The aim of this study was to discover significant rules discriminating ER+ and ER-cases of breast cancer. We compared both statistical accuracy and biological validity of the results using common statistical methods and gene ontology. Some worth noting characteristics of these systems have been observed and analysed even giving some possible interpretations of findings. With this study we tried to show how intelligent systems can be employed in the design of experimental pipeline in disease processes investigation and how deriving high-throughput results can be validated using new computational tools. Results returned by this approach seem to encourage new efforts in this field

Genomic DNA copy number aberrations are frequent in solid tumours although their underlying causes remain obscure. In this paper we show how Artificial Immune System (AIS) paradigm can be successfully employed in the elucidation of biological dynamics of cancerous processes using a novel fuzzy rule induction system for data mining (IFRAIS). Competitive results have been obtained using IFRAIS. A biological interpretation of the results, carried out using Gene Ontology, followed the statistical assessment and put in evidence interesting patterns that are currently under investigation.

This paper presents a novel approach to artificial neural network (ANN) topology optimisation that uses multi-objective genetic algorithm in order to find the best network configuration for the Wisconsin breast cancer database (WBCD) classification problem. The WBCD [Mangasarian, OL., et al., 1995][Mangasarian, OL., et al.][Wolberg, WH., et al., 1995] is a publicly available database composed by 699 cases, each of which is defined by 11 parameters. The former first 10 values of each record account for geometrical features of cells extracted with FNA biopsy. The last parameter represents the nature of the tumour; two classes of tumour are considered in this database: benignant and malignant tumours. An Intelligent System, IDEST, was designed and implemented. At the core of this system there's an Artificial Neural Network that is able to classify cases. The design of such an ANN is a non trivial task and choices incoherent with the problem could lead to instability of the network. For these reasons a fixed topology genetic algorithm (GA) approach was used to find an optimal topology for the given problem. In a second step a multi-objective GA (MOGA) was developed and employed in order to refine the search in the "topology space". Results shown by the IDEST demonstrate the great potentialities of similar approaches.