# Tommaso BiancalaniBroad Institute of MIT and Harvard · Klarman Cell Observatory

Tommaso Biancalani

PhD, Physics

## About

76

Publications

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Introduction

I am a Machine Learning Scientist in the Regev lab. I use deep learning to understand how the brain works.

**Skills and Expertise**

Additional affiliations

August 2015 - present

November 2013 - August 2015

September 2010 - October 2013

Education

September 2010 - September 2013

## Publications

Publications (76)

The observed single-handedness of biological amino acids and sugars has long
been attributed to autocatalysis. However, the stability of homochiral states
in deterministic autocatalytic systems relies on cross inhibition of the two
chiral states, an unlikely scenario for early life self-replicators. Here, we
present a theory for a stochastic indivi...

We present an analytical treatment of a genetic switch model consisting of
two mutually inhibiting genes operating without cooperative binding of the
corresponding transcription factors. Previous studies have numerically shown
that these systems can exhibit bimodal dynamics without possessing two stable
fixed points in the deterministic rate equati...

We investigate a type of bistability where noise not only causes transitions
between stable states, but also constructs the states themselves. We focus on
the experimentally well-studied system of ants choosing between two food
sources to illustrate the essential points, but the ideas are more general. The
mean time for switching between the two bi...

Effective tissue repair requires coordinated intercellular communication to sense damage, remodel the tissue, and restore function. Here, we dissected the healing response in the intestinal mucosa by mapping intercellular communication at single-cell resolution and integrating with spatial transcriptomics. We demonstrated that a risk variant for Cr...

Single-cell RNA-seq (scRNA-seq) studies have profiled over 100 million human cells across diseases, developmental stages, and perturbations to date. A singular view of this vast and growing expression landscape could help reveal novel associations between cell states and diseases, discover cell states in unexpected tissue contexts, and relate in vi...

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be...

Diffusion models have achieved state-of-the-art performance in generating many different kinds of data, including images, text, and videos. Despite their success, there has been limited research on how the underlying diffusion process and the final convergent prior can affect generative performance; this research has also been limited to continuous...

Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformational diversity and geometric constraints. Here, we introduce RINGER, a diffusion-based transformer model for sequence-conditioned generation of macrocycle structures bas...

Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning approaches have significantly accelerated protein structure p...

Generative flow networks (GFlowNets) are a family of algorithms that learn a generative policy to sample discrete objects $x$ with non-negative reward $R(x)$. Learning objectives guarantee the GFlowNet samples $x$ from the target distribution $p^*(x) \propto R(x)$ when loss is globally minimized over all states or trajectories, but it is unclear ho...

Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science. However, modeling real-world systems remain challenging , as most existing algorithms assume that the underlying causal graph is acyclic. While this is a convenient framework for developing theoretical developments a...

Diffusion models achieve state-of-the-art performance in generating realistic objects and have been successfully applied to images, text, and videos. Recent work has shown that diffusion can also be defined on graphs, including graph representations of drug-like molecules. Unfortunately, it remains difficult to perform conditional generation on gra...

Deep graph generative modeling has proven capable of learning the distribution of complex, multi-scale structures characterizing real-world graphs. However, one of the main limitations of existing methods is their large output space, which limits generation scalability and hinders accurate modeling of the underlying distribution. To overcome these...

Diffusion models have achieved justifiable popularity by attaining state-of-the-art performance in generating realistic objects from seemingly arbitrarily complex data distributions, including when conditioning generation on labels. Unfortunately, however, their iterative nature renders them very computationally inefficient during the sampling proc...

Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS), that implicitly learns a three-dimens...

How much explicit guidance is necessary for conditional diffusion? We consider the problem of conditional sampling using an unconditional diffusion model and limited explicit guidance (e.g., a noised classifier, or a conditional diffusion model) that is restricted to a small number of time steps. We explore a model predictive control (MPC)-like app...

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly. Spatial transcriptomics provides spatial location and pattern information about cells in tissue sections at single cell resolution. Cell type classification of spatially-resolved cell...

jats:p>Establishing causal relationships between genetic alterations of human cancers and specific phenotypes of malignancy remains a challenge. We sequentially introduced mutations into healthy human melanocytes in up to five genes spanning six commonly disrupted melanoma pathways, forming nine genetically distinct cellular models of melanoma. We...

Single cell RNA-Seq (scRNA-seq) and other profiling assays have opened new windows into understanding the properties, regulation, dynamics, and function of cells at unprecedented resolution and scale. However, these assays are inherently destructive, precluding us from tracking the temporal dynamics of live cells, in cell culture or whole organisms...

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but...

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1–3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigen...

Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcripto...

Single-cell RNA sequencing has revealed extensive transcriptional cell state diversity in cancer, often observed independently of genetic heterogeneity, raising the central question of how malignant cell states are encoded epigenetically. To address this, here we performed multiomics single-cell profiling—integrating DNA methylation, transcriptome...

The mammalian cerebral cortex has an unparalleled diversity of cell types, which are generated during development through a series of temporally orchestrated events that are under tight evolutionary constraint and are critical for proper cortical assembly and function1,2. However, the molecular logic that governs the establishment and organization...

Patients with chronic lung disease (CLD) have an increased risk for severe coronavirus disease-19 (COVID-19) and poor outcomes. Here, we analyze the transcriptomes of 611,398 single cells isolated from healthy and CLD lungs to identify molecular characteristics of lung cells that may account for worse COVID-19 outcomes in patients with chronic lung...

Human diffuse gliomas are incurable malignancies, where cellular state diversity fuels tumor progression and resistance to therapy. Single-cell RNA-sequencing (scRNAseq) studies recently charted the cellular states of the two major categories of human gliomas, IDH-mutant gliomas (IDH-MUT) and IDH-wildtype glioblastoma (GBM), showing that malignant...

Human diffuse gliomas are incurable brain tumors, where cellular state diversity fuels tumor progression and resistance to therapy. Single-cell RNA-sequencing (scRNAseq) studies recently charted the cellular states of the two major categories of human gliomas, IDH-mutant gliomas (IDH-MUT) and IDH-wildtype glioblastoma (GBM), showing that malignant...

We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell tran...

Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively, but relating those to their histological and anatomica...

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningfu...

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningfu...

Understanding the genetic and molecular drivers of phenotypic heterogeneity across individuals is central to biology. As new technologies enable fine-grained and spatially resolved molecular profiling, we need new computational approaches to integrate data from the same organ across different individuals into a consistent reference and to construct...

Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolu...

Abstract Quantifying virulence remains a central problem in human health, pest control, disease ecology, and evolutionary biology. Bacterial virulence is typically quantified by the LT50 (i.e., the time taken to kill 50% of infected hosts); however, such an indicator cannot account for the full complexity of the infection process, such as distingui...

Quantifying virulence remains a central problem in human health, pest control, disease ecology, and evolutionary biology. Bacterial virulence is typically quantified by the LT50 (i.e. the time taken to kill 50% of infected hosts), however, such an indicator cannot account for the full complexity of the infection process, such as distinguishing betw...

All known life on the Earth exhibits at least two non-trivial common features: the canonical genetic code and biological homochirality, both of which emerged prior to the Last Universal Common Ancestor state. This article describes recent efforts to provide a narrative of this epoch using tools from statistical mechanics. During the emergence of se...

We consider a stochastic version of the Wilson–Cowan model which accommodates for discrete populations of excitatory and inhibitory neurons. The model assumes a finite carrying capacity with the two populations being constant in size. The master equation that governs the dynamics of the stochastic model is analyzed by an expansion in powers of the...

[This corrects the article DOI: 10.1371/journal.pbio.1002540.].

Excel file containing the data used to create Figs 1–5 and S1–S5 Figs.
(XLSX)

The origin of homochirality, the observed single-handedness of biological amino acids and sugars, has long been attributed to autocatalysis, a frequently assumed precursor for early life self-replication. However, the stability of homochiral states in deterministic autocatalytic systems relies on cross-inhibition of the two chiral states, an unlike...

Virtually every interesting natural phenomenon, not least life itself, entails physical systems being forced to flow thermodynamically up-hill, away from equilibrium rather than towards it. This requires the action of a mechanism, acting as an ëngine”, which lashes the up-hill process to a more powerful one proceeding in its spontaneous, down-hill...

We consider a stochastic version of the Wilson-Cowan model which accommodates for discrete populations of excitatory and inhibitory neurons. The model assumes a finite carrying capacity with the two populations being constant in size. The master equation that governs the dynamics of the stochastic model is analyzed by an expansion in powers of the...

Mutualisms between species play an important role in ecosystem function and stability. However, in some environments, the competitive aspects of an interaction may dominate the mutualistic aspects. Although these transitions could have far-reaching implications, it has been difficult to study the causes and consequences of this mutualistic–competit...

Relative fitness of Leu- is lower than fitness of Trp-.
To analyze relative fitness, we grew the strains in co-culture at saturating amino acid concentrations (200 μM tryptophan and 1600 μM leucine). With such high concentrations, additional amino acids provided through cross-feeding will give negligible benefits, thus enabling us to compare the in...

Relative fitness as a function of relative abundance.
To determine equilibria in co-cultures that had not yet reached saturation, we determined relative fitness as a function of the fraction of Leu- cells. Co-cultures were grown for 2 d to reach carrying capacity, after which relative fitness was determined as described earlier (S1 Information). Re...

Smaller niche overlap results in a larger region of mutualistic interactions.
Simulations were run to determine qualitative interaction as a function of supplemented amino acids (a) and niche overlap (c). Niche overlap was modelled as the degree to which each strain affects the carrying capacity of the other strain (S1 Information), with c = 1 bein...

Order of qualitative interactions is robust for changing death rates.
Simulations were run to determine qualitative interactions as a function of supplemented amino acids (a) and death rate (δ). The model shifts through the same order of qualitative interactions in a large range of death rates.
(PDF)

Excel file containing the data used to create Figs 1–5 and S1–S5 Figs.
(XLSX)

Individual tracks of co-culture abundances at different amino acid concentrations.
Plots show individuals traces of experiments used for Fig 3. Co-cultures were grown at 16 different amino acid concentrations, ranging from 0 μM tryptophan and 0 μM leucine to 200 μM tryptophan and 1,600 μM leucine. Co-cultures were started at six different relative...

Growth curves of S. cerevisiae strains before and after adaptation to low amino acids.
Trp- (A) and Leu- (B) cells in exponential phase were seeded in 96-well flat bottom plates and incubated at 30°C for 32 h. Density was measured automatically every 10 min through spectrophotometry. Cells were either adapted (dashed lines) or not adapted (solid li...

Zip file containing the following files: “SM_analytical_treatment.nb” is a Wolfram Mathematica notebook file that contains the code used to generate Figs 2 and 4.
“SM_analytical_treatment.cdf” contains the same code, but for the freely available software CDF interactive player. “SM_analytical_treatment.pdf” contains the PDF version and the figures...

Different ecological regimes are revealed by bifurcation analysis of the model.
Insets I–V are phase portraits of Eqs 1 and 2 obtained for various values of a (other parameters values given in the main text). Eigenvectors have normalized length. Inset I: (a = 0.08, extinction) all trajectories are attracted to global extinction. Inset II: (a = 0.09...

Supplementary information on data analysis and modelling.
(PDF)

A cross-feeding mutualism can protect against invasion by other strains.
Plot shows equilibrium density of simulations with four strains as a function of supplemented amino acids. Double producers (yellow line) are modelled to have a lower growth rate than single producers (red and green lines, equivalent to strain X and Y in Eqs 1 and 2), whereas...

The amplitude of fluctuation-induced patterns might be expected to be proportional to the strength of the driving noise, suggesting that such patterns would be difficult to observe in nature. Here, we show that a large class of spatially-extended dynamical systems driven by intrinsic noise can exhibit giant amplification, yielding patterns whose am...

DOI:https://doi.org/10.1103/PhysRevE.92.039902

We develop an theoretical approach for predicting biodiversity in
multi-dimensional niche spaces, arising due to ecological drivers such as
competitive exclusion. The novelty of our approach relies on the fact that
ecological niches are described by sequences of strings, which allows us to
describe multiple traits. We define the mathematical framew...

We develop an theoretical approach for predicting biodiversity in
multi-dimensional niche spaces, arising due to ecological drivers such as
competitive exclusion. The novelty of our approach relies on the fact that
ecological niches are described by sequences of strings, which allows us to
describe multiple traits. We define the mathematical framew...

We investigate the statistics of the time taken for a system driven by
recruitment to reach fixation. Our model describes a series of experiments
where a population is confronted with two identical options, resulting in the
system fixating on one of the options. For a specific population size, we show
that the time distribution behaves like an inve...

We here review in more detail the mathematical formalism underlying the modelling of stochasticity in population systems, which will be used throughout the rest of the thesis. We begin with revisiting the basic mathematical definitions which lead to the concept of homogeneous stochastic process. Those describe the dynamics of the chemical concentra...

In order to study the phenomenon of noise-induced bistability we have expanded the master equation with respect to the cell volume, and obtained an approximated stochastic differential equation which was more amenable to analytical manipulations. As already remarked, the technique we have used made it possible to capture the multiplicative nature o...

The investigation of the stochastic waves in the previous chapter, has involved an analysis of a spatially extended model in which space was represented as a regular lattice.

Bistability indicates the ability of a system to reside in either of two states. This is a fairly general paradigm that has found numerous applications in science, from liquid crystal displays to the lac operon in E. coli to the cell cycle oscillator in Xenopus laevis.

Stochastic reaction-diffusion models can be analytically studied on complex
networks using the linear noise approximation. This is illustrated through the
use of a specific stochastic model, which displays traveling waves in its
deterministic limit. The role of stochastic fluctuations is investigated and
shown to drive the emergence of stochastic w...

Diffusion of a two component fluid is studied in the framework of differential equations, but where these equations are systematically derived from a well-defined microscopic model. The model has a finite carrying capacity imposed upon it at the mesoscopic level and this is shown to lead to nonlinear cross diffusion terms that modify the convention...

We investigate a type of bistability where noise not only causes transitions
between stable states, but also constructs the states themselves. We focus on
the experimentally well-studied system of ants choosing between two food
sources to illustrate the essential points, but the ideas are more general. The
mean time for switching between the two bi...

We review the mathematical formalism underlying the modelling of stochasticity in biological systems. Beginning with a description of the system in terms of its basic constituents, we derive the mesoscopic equations governing the dynamics which generalise the more familiar macroscopic equations. We apply this formalism to the analysis of two specif...

Background / Purpose:
We describe an experimental setup to investigate the behaviour of two dense ink droplets simultaneously evolving in a container filled with water. A CCD (charge coupled device) camera allows acquisition of a time sequence; the stack of images is then processed to extract the geometrical features of the system dynamics in the...

Intra-cellular biochemical reactions exhibit a rich dynamical phenomenology
which cannot be explained within the framework of mean-field rate equations and
additive noise. Here, we show that the presence of metastable states and
radically different timescales are general features of a broad class of
autocatalyic reaction networks, and moreover, tha...

We show that intrinsic noise can induce spatiotemporal phenomena such as Turing patterns and traveling waves in a Brusselator model with nonlocal interaction terms. In order to predict and to characterize these stochastic waves we analyze the nonlocal model using a system-size expansion. The resulting theory is used to calculate the power spectra o...

A stochastic version of the Brusselator model is proposed and studied via the system size expansion. The mean-field equations are derived and shown to yield to organized Turing patterns within a specific parameters region. When determining the Turing condition for instability, we pay particular attention to the role of cross-diffusive terms, often...

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