No. 20:Inclusive Growth and the Informal Food Sector in Bangalore, India

This report presents and analyzes the findings of a food vendor survey conducted by the Indian Institute for Human Settlements as part of the Hungry Cities Partnership (HCP) in Bangalore, India, in September and October 2018. It is a supplement to, and should be read in conjunction with, HCP Report No. 5: The Urban Food System of Bangalore, India (Surie and Sami 2017) and HCP Report No. 14, The State of Household Food Security in Bangalore, India (Koduganti et al 2019). The former provides essential contextual background on the history, demography, and economy of Bangalore, while the latter presents findings from a city-wide household food security survey. This report provides new empirical knowledge about food vendors and the informal food economy within which they operate. It also contributes to comparative studies among the seven cities of the HCP project. The report consists of 11 sections. Section Two provides an overview of the sampling strategies and methodologies of the city-wide vendor survey. Section Three profiles the food vendors included in the sample. Section Four discusses the vendors’ enterprise structure. Section Five explores the business strategies employed by the vendors. Section Six examines the financial metrics of the food enterprises. Section Seven examines the vendors’ business challenges and Section Eight explores food storage and electricity provision. Section Nine profiles the employees working at the surveyed enterprises and Section Ten explores business aspirations and plans. The final section presents a brief discussion of the survey findings

Overview of the processes modelled in this study.

(A)—Integrins bind to talin and vinculin in a precomplexation step, then form a small cluster, termed ‘seed’. Seeds can dimerise to form larger clusters, termed ‘clusts’. Actin filaments pull on talin and vinculin causing cryptic vinculin-binding sites on talin to be exposed, promoting more vinculin recruitment. This chain can then break at the integrin–ligand catch-slip bond or the talin–actin slip bond (black boxes). See Table 1 for a detailed description of the terminology. (B)—Overview of reactions in the model. Int, tal, and vinc refer to concentrations of integrins, talin and vinculin respectively. Black rectangle encloses the reinforcement reactions (expanded further in Fig 1C). Grey arrows represent clust formation reactions. Red arrows represent actin binding reactions. Dotted arrows represent force-dependent reactions—blue dotted: reinforcement, black dotted: actin unbinding. Dashed arrows represent adhesion disassembly reactions, black dashed: talin refolding, purple dashed: cluster breakdown. Yellow lightning bolts indicate rates that undergo signal-dependent rate modification (SDRM), dark green solid hourglasses represent rates that undergo time-dependent rate modification (TDRM). The rate constants undergoing signal-dependent modifications are driven to zero after ∼158 s leaving active only the lower part of the model, enclosed in the blue box, representing adhesions that will undergo further maturation. (C)—Talin and vinculin are modelled as Hookean springs (also see Fig A in S1 Appendix). In this model, to capture the process of reinforcement, a maximum of three vinculin binding events occur sequentially (blue arrows) at different points along the talin rod, thereby increasing the stiffness of individual integrin–talin–vinculin spring systems. Clustering is modelled as an increase in the number of integrin–talin–vinculin spring systems in parallel (grey arrows). This figure was created using BioRender.com.</p

Sensitivity analysis results.

(A)—Maturation fraction vs stiffness (outcome 1) and (B)—Mean retrograde velocity vs stiffness for a local variation in parameter values of different parameters. Black arrows in (B) point in the direction of increasing parameter value and track the optimal stiffness (outcome 2).</p

Schematic showing the flowchart for simulation and force quantification.

Schematic showing the flowchart for simulation and force quantification.</p

Mean periods in s of different actin-bound clutches for different substrate stiffnesses.

Mean periods in s of different actin-bound clutches for different substrate stiffnesses.</p

S1 Appendix -

Detailed explanations of the methods (Text A), additional results (Text B), figures (Fig A—Fig N) and tables (Table A and Table B). [83–104] are cited in this file. (ZIP)</p

While NA formation is substrate stiffness independent, maturation is influenced considerably by stiffness.

(A)—Concentration over time of species in the model that represent NAs (S1 and C1). The curves for all tested substrate rigidities overlap and hence appear as a single (blue) line. The vertical dotted line marks the time point when the signal threshold is crossed and hence new NA formation reduces. (B)—Concentrations over time of all the actin-bound species. Species representing NAs (S1a, C1a) increase initially before being driven to 0 after the signal concentration drops below the threshold. The highest levels of maturation occur on substrate of moderate stiffness (ksub = 1 pN/nm).</p

Terminology used in this manuscript.

Cells interact with the extracellular matrix (ECM) via cell–ECM adhesions. These physical interactions are transduced into biochemical signals inside the cell which influence cell behaviour. Although cell–ECM interactions have been studied extensively, it is not completely understood how immature (nascent) adhesions develop into mature (focal) adhesions and how mechanical forces influence this process. Given the small size, dynamic nature and short lifetimes of nascent adhesions, studying them using conventional microscopic and experimental techniques is challenging. Computational modelling provides a valuable resource for simulating and exploring various “what if?” scenarios in silico and identifying key molecular components and mechanisms for further investigation. Here, we present a simplified mechano-chemical model based on ordinary differential equations with three major proteins involved in adhesions: integrins, talin and vinculin. Additionally, we incorporate a hypothetical signal molecule that influences adhesion (dis)assembly rates. We find that assembly and disassembly rates need to vary dynamically to limit maturation of nascent adhesions. The model predicts biphasic variation of actin retrograde velocity and maturation fraction with substrate stiffness, with maturation fractions between 18–35%, optimal stiffness of ∼1 pN/nm, and a mechanosensitive range of 1-100 pN/nm, all corresponding to key experimental findings. Sensitivity analyses show robustness of outcomes to small changes in parameter values, allowing model tuning to reflect specific cell types and signaling cascades. The model proposes that signal-dependent disassembly rate variations play an underappreciated role in maturation fraction regulation, which should be investigated further. We also provide predictions on the changes in traction force generation under increased/decreased vinculin concentrations, complementing previous vinculin overexpression/knockout experiments in different cell types. In summary, this work proposes a model framework to robustly simulate the mechanochemical processes underlying adhesion maturation and maintenance, thereby enhancing our fundamental knowledge of cell–ECM interactions.</div

Model predictions of mean actin retrograde velocity and maturation fraction for the baseline model.

(A) shows the predicted velocity vs substrate stiffness compared to previous studies, (B) shows the predicted velocity (blue) and mean force exerted by all adhesions (red) in this model, and (C) shows the NA maturation fraction vs substrate stiffness.</p

Vinculin concentration can influence maturation fraction.

(A) and (B) are cell types or biological contexts where vinculin availability is low and high respectively. In A, the likelihood of vinculin binding to the exposed vinculin-binding sites on talin is low leading to a low maturation fraction. However, in B, due to a relatively higher vinculin availability, the integrin-actin link is highly likely to be reinforced by vinculin, increasing the maturation fraction. This figure was created using BioRender.com.</p