TY  - JOUR
AB  - Large-scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances. Following this analogy, we identify several emergent statistical laws in single-cell transcriptomic data closely similar to regularities found in linguistics, ecology, or genomics. A simple mathematical framework can be used to analyze the relations between different laws and the possible mechanisms behind their ubiquity. Importantly, treatable statistical models can be useful tools in transcriptomics to disentangle the actual biological variability from general statistical effects present in most component systems and from the consequences of the sampling process inherent to the experimental technique.
AU  - Lazzardi, S.*
AU  - Valle, F.*
AU  - Mazzolini, A.*
AU  - Scialdone, A.
AU  - Caselle, M.*
AU  - Osella, M.*
C1  - 67925
C2  - 54403
CY  - One Physics Ellipse, College Pk, Md 20740-3844 Usa
TI  - Emergent statistical laws in single-cell transcriptomic data.
JO  - Phys. Rev. E
VL  - 107
IS  - 4-1
PB  - Amer Physical Soc
PY  - 2023
SN  - 1063-651X
ER  - 

TY  - JOUR
AB  - We propose a statistical learning framework based on group-sparse regression that can be used to (i) enforce conservation laws, (ii) ensure model equivalence, and (iii) guarantee symmetries when learning or inferring differential-equation models from data. Directly learning interpretable mathematical models from data has emerged as a valuable modeling approach. However, in areas such as biology, high noise levels, sensor-induced correlations, and strong intersystem variability can render data-driven models nonsensical or physically inconsistent without additional constraints on the model structure. Hence, it is important to leverage prior knowledge from physical principles to learn biologically plausible and physically consistent models rather than models that simply fit the data best. We present the group iterative hard thresholding algorithm and use stability selection to infer physically consistent models with minimal parameter tuning. We show several applications from systems biology that demonstrate the benefits of enforcing priors in data-driven modeling.
AU  - Maddu, S.*
AU  - Cheeseman, B.L.*
AU  - Müller, C.L.
AU  - Sbalzarini, I.F.*
C1  - 61896
C2  - 50293
CY  - One Physics Ellipse, College Pk, Md 20740-3844 Usa
TI  - Learning physically consistent differential equation models from data using group sparsity.
JO  - Phys. Rev. E
VL  - 103
IS  - 4
PB  - Amer Physical Soc
PY  - 2021
SN  - 1063-651X
ER  - 

TY  - JOUR
AB  - Input-output tables describe the flows of goods and services between the sectors of an economy. These tables can be interpreted as weighted directed networks. At the usual level of aggregation, they contain nodes with strong self-loops and are almost completely connected. We derive two measures of node centrality that are well suited for such networks. Both are based on random walks and have interpretations as the propagation of supply shocks through the economy. Random walk centrality reveals the vertices most immediately affected by a shock. Counting betweenness identifies the nodes where a shock lingers longest. The two measures differ in how they treat self-loops. We apply both to data from a wide set of countries and uncover salient characteristics of the structures of these national economies. We further validate our indices by clustering according to sectors’ centralities. This analysis reveals geographical proximity and similar developmental status.
AU  - Blöchl, F.
AU  - Theis, F.J.
AU  - Vega-Redondo, F.*
AU  - Fisher, E.*
C1  - 6778
C2  - 29256
TI  - Vertex centralities in input-output networks reveal the structure of modern economies.
JO  - Phys. Rev. E
VL  - 83
IS  - 4
PB  - American Physical Society
PY  - 2011
SN  - 1063-651X
ER  - 

TY  - JOUR
AB  - Glycolysis is one of the most essential intracellular networks, found in a wide range of organisms. Due to its importance and due to its wide industrial applications, many experimental studies on all details of this process have been performed. Until now, however, to the best of our knowledge, there has been no comprehensive investigation of the robustness of this important process with respect to internal and external noise. To close this gap, we applied two complementary and mutually supporting approaches to a full-scale model of glycolysis in yeast: (a) a linear stability analysis based on a generalized modeling that deals only with those effective parameters of the system that are relevant for its stability, and (b) a numerical integration of the rate equations in the presence of noise, which accounts for imperfect mixing. The results suggest that the occurrence of metabolite oscillations in part of the parameter space is a side effect of the optimization of the system for maintaining a constant adenosine triphosphate level in the face of a varying energy demand and of fluctuations in the parameters and metabolite concentrations.
AU  - Gehrmann, E.*
AU  - Gläßer, C.
AU  - Jin, Y.*
AU  - Sendhoff, B.*
AU  - Drossel, B.*
AU  - Hamacher, K.*
C1  - 6923
C2  - 29431
TI  - Robustness of glycolysis in yeast to internal and external noise.
JO  - Phys. Rev. E
VL  - 84
IS  - 2
PB  - Amewr Physical Soc
PY  - 2011
SN  - 1063-651X
ER  -