Finding drug targets for antimicrobial treatment is a central focus in biomedical research.To discover new drug targets, we are interested in finding out which nutrients are essential for pathogenic microorganisms in the host or under specific circumstances. Besides, metabolic fluxes have been successfully constructed and predicted by employing flux balance analysis (FBA) technique. While 13 C metabolic data is the most informative way to explore metabolism, the data can be difficult to acquire in complicated environments, for example, osteomyelitis from S. aureus. On the other hand, although gene expression data is less informative in this case as compared to 13 C metabolic data, it is easier to generate, and it still provides us informative insights. We develop FBA models using the stoichiometric knowledge of the metabolic reactions of a cell and combine them with gene expression profiles. We aim to identify essential drug targets for specific nutritional uptakes of pathogenic microorganisms. As a case study, we implemented our method by applying data from B. subtilis to predict carbon sources based on given gene expression profiles. We validated our flux prediction results by comparing with 13 C metabolic flux data.With our method, we efficiently utilized gene expression profiles to predict carbon sources and investigate the metabolic network of B. subtilis. We show that our method is promising, generalizable, and versatile. We present that using FBA model with gene expression data is a good starting point to support subsequent hypotheses to conduct further studies; especially, in the environment that 13 C metabolic flux data is hard to achieve. Besides, from a technical aspect, our method performed faster in order to remove thermodynamically infeasible loops as compared to loopless COBRA (ll-COBRA), which is the well-established method in the community. 3 Keywords: flux balance analysis, mixed integer linear programming, bacillus subtilis, carbon source 4

Background Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by 13C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes. Result We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with 13C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods. Conclusion Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.