Over the course of the twentieth century, theoretical biology changed beyond all recognition. Although the field today is synonymous with mathematical biology, when it first emerged it had a drastically different agenda: to critically analyze the conceptual foundations of biology in order to resolve long-standing theoretical disputes, abstract from the ‘burden of details,’ and bring about the epistemic unification of biological science. The field began acquiring its now familiar mathematical character in the 1940s, as formal models became increasingly applied in different areas of biology, such as growth, ecology, genetics, and evolution. Regrettably, the early ‘philosophical’ period of theoretical biology has been almost completely forgotten and its existence is seldom acknowledged—let alone carefully examined (but see Nicholson & Gawne 2015, Baedke 2019). Much of this early discourse took place in a handful of book series, monographs, and journals, the majority of which were published in German (at least initially). Hence, it is perhaps not surprising that Anglophone scholars remain almost completely ignorant of this large, and surprisingly rich, body of literature. Our aim is to analyze this forgotten corpus and rescue it from the dustbin of history. Our guiding question is: What did theoretical biology look like in the early 20th century? More specifically, we ask: (i) What were the central debates and topics? (ii) Who were the central authors and how international was the scientific community at the beginning? (iii) Can distinct language-(of-origin-)specific camps be identified in terms of the kinds of topics they addressed? (iv) What, where, and when did transitions occur in networks of authors and topics? (v) When, how, and why did the discipline develop its emphasis on formal modeling? At this early exploratory stage of the project, we operationalize these central questions mainly as a topic-modelling problem: (1) Which central topics can be identified and how does their ‘share’ of the documents develop? Which topic clusters can be identified? (2-3) Are certain topics dominated by particular authors, languages (of origin), and nationalities? (4) Can certain ‘turning points’ be identified? Additionally: (5) How steadily does the proportion of publications that use mathematical formulas increase over time? Is it gradual or rather discontinuous? After (a) preparing and selecting documents for the corpus on a historical basis (encompassing monographs, book series and journal articles)—digitizing, and OCR-ing with tesseract where necessary—we (b) machine translate the non-German texts into German using the Google Translate API. As de Vries, Schoonvelde, and Schumacher (2018) argue for topic-modelling in general, and Malaterre (2021) for the special use-case of history of science, modern machine translations deliver useful results that are reliable for multilingual topic-modelling. Additionally, we plan to assess our translation accuracy with Malaterre’s proposed “Semantic Topology Preservation Test” (2021). Then, we (c) preprocess the corpus: Following a general cleaning of common OCR-errors and stop words, we reduce the corpus to lemmatized adjectives and nouns via spaCy’s POS tagging and lemmatization algorithms. We assume that the conceptual topics we aim to explore are mostly expressed in nouns and adjectives (see Jockers 2013, Malaterre et al. 2020). The preprocessed documents are then (d) analyzed with LDA topic-modelling, using gensim’s MALLET-wrapper and (e) analyzed with top2vec, to cluster the documents thematically – enabling a different granularity and perspective, since top2vec does not treat the documents as bags-of-words and tends to generate few more general topics (see Angelov 2020). Finally, (f) we calculate document embeddings using UMAP and (g) visualize the embedding as an interactive scatter plot (with the option of time-period slices) with Bokeh, since the heterogeneity of our corpus does not allow for a simple linear visualization. We enrich the scatter plot with metadata for a mouse-over pop-up window, generated from the most important topics for each document, and color the documents by their top2vec cluster, complementing the visual clustering and topological distribution the document embedding shows. Thus, we create an interactive tool for exploration, hoping to motivate future research. Moreover, we plan to utilize tesseract’s equ language data to detect mathematical equations in documents. We take the use of mathematical formulas as a signal of affiliation with the mathematical side of the discourse on theoretical biology. This way, each document is assigned a gradual mathematization score. To model the mathematization of theoretical biology, we then analyze the mathematization scores per year and the scores’ correlations with topics. 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